Automation of building information modelling process of sanitary drainage system with Dynamo

Chua JTJ and Fadeyi MO (2021). Automation of building information modelling process of sanitary drainage system with Dynamo. Built Environment Applied Research Sharing #09.

ISSUU Digital Publishing.

© BEARS reserves the right to this applied research article

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Automation of building information modelling process of sanitary drainage system with Dynamo Jing Ting Jamie Chua and Moshood Olawale Fadeyi,* Sustainable Infrastructure Engineering (Building Services) Programme, Singapore Institute of Technology, 10, Dover Drive, Singapore 138683, Singapore *Corresponding author’s email: fadeyi.moshood@singaporetech.edu.sg

ABSTRACT This project aims to design an adoptable solution that improves the quality of work and increases work productivity for the modelling of inspection chambers, waste sumps and manholes, and their pipe connections in Revit. Existing workflows and design requirements of the sanitary discharge system at a consultancy firm were analysed. Since the project aims to address a practical problem, the mixed-methods research (MMR) overall approach was employed for this study. The MMR was conducted across four phases with different data collection and analysis approaches. Phase 1 involves understanding existing workflows and the design requirements of the sanitary discharge system the existing Dynamo workflow used at the consultancy firm managed to generate pipe connections. Phase 2 involves evaluating the performance and user experience of existing workflows. Phase 3 involves the creation and development of the solution. Phase 4 involves evaluating the performance and adoptability of the solution – the new Dynamo workflow. The new Dynamo workflow developed in this study provided users a better work experience than the existing modelling workflows. The new Dynamo workflow significantly reduced labour hours required for completing the given modelling tasks accurately.

Keywords: Design Productivity, Dynamo workflow, Building information modelling, Design management, Computational BIM

1.0

INTRODUCTION

A major component of the plumbing and sanitary system that requires rework is the sanitary drainage system. It removes wastewater, water no longer suitable for use, out of the building to prevent flooding and reduce sewer odor. The system involves a network of drain pipelines, 2

floor traps, floor wastes, waste sumps, inspection chambers, grease traps from food establishments, gully traps, manholes, car park waste sumps and ejector pits. An illustration of connections from the sanitary plumbing to the sanitary drainage system is provided in Figure 1 to aid in the description of how the two systems operate.

Figure 1: Schematic connections from risers to basement drainage system

The sanitary system involves connections to sanitary appliances such as water closets, urinals, washbasins, baths and showers. Soil waste pipes carrying soiled water from toilets and waste pipes carrying water from floor drains such as floor traps and floor wastes would be directed into vertical soil waste stacks and waste stacks found in risers. Wastewater flowing through the main discharge stacks would then enter inspection chambers located at the basement or ground level. At this stage, the sanitary system is considered to be connected to the sanitary discharge system.

Wastewater from inspection chambers at the basement level would enter the ejector tank found in an ejector pit. The ejector pumps would then direct the wastewater to connecting ground level inspection chambers. It is to note that waste sumps located at the basement level are connected solely to floor drains unlike inspection chambers which may be connected to the main discharge stacks found in risers and gully trap from the bin center. Pipes from waste sumps at the basement level would enter a grease trap, removing any grease that may have entered the floor drains. They would then enter larger carpark waste sumps through drain pipelines and the wastewater would be pumped to ground level inspection chambers by pumps found in the 3

carpark waste sumps. Ground level inspection chambers are finally directed to a manhole outside the building.

At a consultancy firm, the design stage consists of a design process where mechanical, electrical, and plumbing (MEP) engineers are responsible for designing the sanitary drainage system according to design requirements while modellers are responsible for building two dimensional or three-dimensional models onto modelling software. In the past, the design stage consists solely of the design process where multiple hardcopy drawings are submitted to the relevant authorities for review. A growing trend in the construction industry to use sustainable technologies such as building information modelling (BIM) in many stages of the building lifecycle was discovered. BIM, an intelligent 3D model-based process, is used as a solution to improve the planning, design, and management of infrastructures (Li et al., 2014). It is an attractive technology as it has high potential in reducing project costs and material wastage.

BIM modellers and engineers can easily identify changes to be made, make revisions and streamline repetitive tasks during the design stage (Röck et al., 2018). Other additional BIM tools such as Dynamo, a plug-in found in Revit that functions as a visual programming design tool, could be utilized as well (Nezamaldin, 2019). Despite the multiple advantages of utilizing modelling software platforms, factors such as users’ competency, complexity of design and use of suitable additional tools contribute to the time required to model the sanitary discharge system. BIM professionals must constantly communicate with MEP engineers to check if models are abiding to design requirements. The

internal depth of waste sumps,

inspection chambers, grease trap, gully trap and manholes must be calculated individually as it affects the internal depth of connecting components. Pipe connections modelled and aligned individually where section views must be created for visualization during this process. Ultimately, modelling the sanitary discharge system using current methods is a timeconsuming process that increases project costs, rework time and often result in multiple design errors not abiding to the COP.

It is important to consider not just the solution’s effectiveness in resolving errors but adoptability as well. If the Revit user perceives that Dynamo scripts would not increase their work productivity or is hard to use, no matter how effective the solution might be, it would 4

not be adopted (Batarseh and Kamardeen, 2017). The aim of this project is to design an adoptable solution that improves the quality of work and increases work productivity for the modelling of inspection chambers, waste sumps and manholes and their pipe connections in Revit.

The project addresses the effectiveness of computational BIM where algorithms are defined to increase the effectiveness of work processes. End users of the developed solution include competent Revit users in relevant working industries such as BIM professionals in consultancy firms, educational institutions such as students or professors and other individuals.

The number of errors, time spent, and estimated project costs generated for the modelling process using the traditional and existing Dynamo scripts would be examined. A solution would be designed to resolve the present issues and additional features that improve the quality of work. The solution’s effectiveness would then be evaluated in terms of the number of labour hours reduced, adoptability and quality as compared to the existing methods.

2.0

LITERATURE REVIEW

Inefficient processes such as poor communication have led to not only high energy consumption but large amounts of resource wastages, high project costs and poor quality of work delivered (Love and Edwards, 2004). As a result, clients on the receiving end would be unsatisfied due to the increased construction costs, delay in project schedule and unsafe structures constructed (Rahman et al., 2013).

Rework was found to be a result of inefficient work processes that impacted project costs, quality and material wastage (Love and Edwards, 2004). Chidiebere and Ebhohimen (2018) conducted a study which revealed the impacts rework had on building

project and

organizational performance. It was discovered that rework could contribute up to 12.4% of the total project costs. Such additional costs were six times the cost of rectifying construction failures and could have been avoided (Love and Edwards, 2002a). Overtime was present at both construction sites and design consultancy firms to conduct rework (Love and Edwards, 2002b). Additional manpower may be required to complete the project scope and the possible downfall of including other workers midway during a project would be a decreasing quality of work.

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Sustainable and innovative solutions were adopted by the built environment sector to reduce the need for rework. BIM is currently considered the most innovative technology used in the built environment sector (Vilutieneet al., 2019). The multidimensional information platform integrates project data for professionals to access and

use throughout the entire project

lifecycle (Wang and Love, 2012). Such level of visualization and integration improves decision making during the design stage as well (Chen et al., 2018). Objectives such as improving project performance by reducing the occurrence of errors can be achieved with the use of BIM (Baldwin et al., 2008). Key indicators used to measure the impact BIM has on productivity are quantity of request for information (RFI), amount of rework, schedule compliance and change orders due to plan conflicts (Fan et al., 2014). A study was conducted to measure the impact BIM has on productivity and found that actual number of labour hours was less than budgeted with the use of BIM (Poirier et al., 2015).

Dynamo is a popular visual programming extension in Revit used to reduce repetitive work such as calculations by having users create algorithms (Khosakitchalert et al., 2018). Across multiple studies, Dynamo was found to be a popular tool used as compared to computational BIM tools such as Grasshopper and Rhino. Dynamo was selected to automate the compliance checking for BIM as it is a plugin already available in Revit (Reinhardt and Matthews, 2017). When comparing its effectiveness to other methods used to extract BIM data, the advantages and restrictions of Dynamo was highlighted. Users do not require programming skills to operate the Dynamo BIM tool as it is a visual programming platform (Ignatova et al., 2018). They can simply connect what is known as ‘nodes’ which have integrated functions to one another. Users who are trained in C## and Python programming language could use their skills to simplify connections in Dynamo as well (Asl et al., 2015). However, one restriction of the Dynamo BIM tool is that nodes have to be downloaded from packages available in either its library or online as the generic set of nodes available do not cover all functions of the Revit application programming interface (API) (Ignatova et al., 2018).

Adoptability of a solution is also highlighted by previous research as an important factor

to

consider. Resistance to utilizing solutions may be due to factors such as perceived usefulness, perceived ease of use (Asl et al., 2015). These factors may be influenced by external variables such as output quality, job relevance and even the appearance of the solution according to the technology acceptance model. The user experience questionnaire was used to evaluate a selfmonitoring tool designed and results highlighted the need to improve both usability and user6

experience of a product or solution for it to have a wider adoption (Santoso et al., 2017).

The above cited articles have indicated that rework is a common occurrence in the built environment sector and its negative impacts on the environment and work performance. The benefits brought about with the integration of BIM and computational BIM tools during the design stage were highlighted across multiple studies as well. A crucial discovery was that despite the effectiveness of a solution to resolving present issues, they would not be adopted if users perceive them to be unhelpful and hard to use.

3.0

METHODOLOGY

Since the aim of the project is to address a practical problem, the mixed-methods research (MMR) overall approach was employed for this study. The five primary characteristics of an MMR are as follows (Schoonenboom and Johnson, 2017). First, there needs to be a purpose for integrating both qualitative and quantitative methods to achieve the goal of answering the research question. Second, the theoretical drive has to be discovered where it in this case it is to identify if the research is qualitatively driven, quantitatively driven or an equal- status study where both data can provide insight for all areas of the study. Third, the simultaneity of data collection and analysis is highlighted where qualitative and quantitative data may be obtained and analysed concurrently or sequentially. Fourth, the dependency of qualitative data on quantitative data and vice versa is to be defined. For example, the implementation of a qualitative data may be independent or dependent on the results of the quantitative data obtained. Fifth, the point of integration where both qualitative and quantitative data collected are merged is known.

In the field of engineering education, the MMR approach is still an emerging methodology due to variations found from existing MMR literature on its characteristics. Variations include the terminology used and amount of integration required to qualify as a mixedmethods analysis. However, this methodology is still accepted in the field as it is known to be a rigorous process that produces extensive results. For this project, MMR was deemed suitable as it meets the primary characteristics listed above.

First, this study required both quantitative and qualitative methods to obtain additional insight 7

into the number of hours required, quality of work, and adoptability of a solution that improves current workflows for the sanitary discharge system. Second, the research was qualitatively driven where quantitative data was used to support results generated from qualitative analysis and measure productivity and number of errors generated that are quantifiable. Third, data collection and analysis were conducted separately across all phases of the study. Fourth, quantitative data obtained were independent from qualitative data and vice versa. Fifth, both qualitative and quantitative data were merged for data analysis and presented under the results section. The MMR was conducted across four phases with different approaches used for data collection and analysis methods during each phase. The results and discussion would be conducted across the same four phases as well.

3.1

Phase 1 – Understanding workflows and design requirements

Qualitative methods were applied for data collection on the workflow and design requirements of the sanitary discharge system. Firstly, observation was conducted on five BIM professionals and five MEP engineers at consultancy firm X using an objectivist approach. Access to consultancy firm X for the conduct of this research was granted due to connections from a previous internship with them. However, objectivity is maintained where the research participants were neither influenced nor interfered during the observation process. The sample size was determined based on the number of workers who have projects that require the design and modelling of a sanitary discharge system. In the span of one week, the design and modelling process of a sanitary discharge system were observed and documented using notetaking. The qualitative data collected serves as a visual representation of the number of labour hours that might be spent to complete the modelling and design process.

Ideally, quantitative data on the number of labour hours spent would be collected through a laboratory designed experiment. However, it is uncommon to have multiple BIM professionals working on the same project simultaneously and the depth of analysis for an understanding of the workflow would not be justifiable. Such level of analysis would be applied at a later phase of the project instead and the general steps applied the design of the sanitary discharge system across various projects were recorded at this phase.

To examine the reliability of data collected, observations were made under different circumstances such as different times of the day and across three different pairs of BIM 8

professional and MEP design engineer. Secondly, informal discussions involving BIM modellers, the BIM leader from the hydraulics division and the BIM manager were held and documented using notetaking as well to increase the validity of data collected. Thirdly, a semistructured interview was held with a MEP design engineer in a meeting room at consultancy firm X. During the interview, the documented steps, calculations and design requirements were checked and corrected by a qualified MEP design engineer who is experienced in the design of sanitary systems. Only one MEP design engineer was interviewed based on his qualifications as a Qualified Person and also because the presented workflow during the interview was gathered based on the prior observation held with other MEP design engineers and BIM professionals. The BIM Manager of consultancy firm X verified that the results from the interview were accurate as well. The interview was transcribed using a transcription mobile application called Otter Voice Meeting Notes. The interviewee would be referred to as ‘MEP Design Engineer 1’ as permission to mention the name was not received.

The documented notes and transcription from observations, discussions and interviews were coded using a thematic analysis. This method of analysis is used to identify repeating themes across the various responses. An inductive approach was used for the observations and discussions where the themes were determined based on the data collected while a deductive approach was used for the interview where preconceived themes such as the steps required for the design and modelling were identified prior to the interview. Data collected was familiarized with and coded later by highlighting sections of the texts identified to be of the same group.

Themes such as the steps for the design and modelling process of the sanitary discharge system were generated based on identified patterns. The themes were then reviewed, named and organized into steps.

3.2

Phase 2 – Evaluating the performance and user experience of existing workflows

After understanding the workflows and design requirements of existing workflows, a survey was conducted over the span of one and a half weeks in September 2019. Although surveys are generally categorized as quantitative data collection methods, the questions were framed to evaluate their user experience and is recognized as a qualitative data collection method.

However, the qualitative data collected was presented quantitatively to provide statistical comparison that could be useful for continuous improvement to the modelling process of sanitary discharge systems. Thus, the survey is considered to be both a qualitative and 9

quantitative method for data collection. Survey 1 was used as market research to confirm an assumption that long hours are spent, and errors are frequent during the completion of various sanitary discharge system designs using existing methods, supporting the need for a solution. In addition, an understanding of the users’ experience could be gained and correlated to the importance of adoptability of a solution as a measurement of its effectiveness. Additional feedback on their thoughts on the existing methods and possible areas for improvements could be simultaneously received.

Though the end users of the developed solution include competent Revit users in relevant working industries and educational institutions, only BIM professionals in consultancy firm X were familiar with the existing methods at that point. Thus, they were selected as the target population for the survey. In this study, ten BIM professionals were selected as the sample for the survey. Participants were referred to as ‘Participant 1’ to ‘Participant 10’ as their names do not affect the analysis in this study.

The survey was conducted at the consultancy firm X’s BIM office in the form of an online questionnaire as it is easier to distribute, has high flexibility and of low cost. It was developed using an online tool, Google Forms, and the online link was sent to ten BIM professionals selected at random through Skype. Survey participant had to utilize the two existing workflows to complete the sanitary design of project models they are currently working on before completing the survey. Variables across project models such as different levels of complexity were considered during this data collection process. Since the purpose of Survey 1 was to identify the performance and user experience of the existing workflows across various sanitary discharge system designs, the variables identified were neglectable.

The survey consists of a short introduction explaining its purpose and goals followed by a section on ‘Evaluating Existing Workflows for the Sanitary Discharge System’. A user experience questionnaire (UEQ) approach was used to evaluate the user experience of BIM professionals when using existing workflows to complete the sanitary discharge system.

Considering the user experience of existing workflows when developing a solution would increase its adoptability at a later stage. User experience is defined as the perception and response resulted from the use of a product, system, or service. Although there is a lack of present research conducted on BIM using the UEQ approach, it was used more than other 10

approaches such as the technology acceptance model as it is a data analytical approach on other areas of studies. Previous studies do use the UEQ to evaluate the user experience of engineering products and services as well (Santoso et al., 2017). Thus, this approach could be used for the study of current workflows where they would be considered as services.

In the UEQ approach, BIM professionals were asked to indicate their level of agreement through a set of 26 closed-ended questions on a seven-point Likert scale ranging from strongly disagree to strongly agree. Two set of 26 questions were provided where one was used to analyse the traditional workflow and the other was used to analyse the Dynamo workflow. The list of questions was provided by a given UEQ template found on the UEQ online website. The questions were created and categorized based on six scales (Schrepp et al., 2014). First, attractiveness which refers to whether the user likes or dislikes the product overall. Second, perspicuity which refers to whether it is understandable and easy to use. Third, efficiency which refers to whether little effort is required to complete tasks using the products. Fourth, dependability which refers to whether the products are secure and feel in control when utilizing them. Fifth, stimulation which refers to whether they are motivating or interesting to use.

Sixth, novelty which refers to whether the product is innovative and unconventional. Data collected from the UEQ questions were analysed using a UEQ calculation of a CronbachAlpha coefficient shown below:

α = n*r /1+(n−1)∗r where r is the mean correlation of items in a scale and n is the number of items in a scale.

The mean value of the survey results would then be obtained. Closed-ended questions were provided after the UEQ such as a list of options with a single answer possible on how often they received projects requiring the modelling of the sanitary discharge system, a binary question on whether they foresee themselves using the existing Dynamo workflow over the traditional workflow, lists of options with a single answer possible on how they would rank the suggested features to be added to the existing workflows, and a binary question whether they would be interested in using a developed solution that provides such features.

Data collected from the other closed-ended questions were analysed based on the mean, standard deviation, lower and upper confidence interval (ci) which in this case is 95ci. The data received 11

through the google form were exported onto an excel spreadsheet and were manually calculated to present results in the same table form. Insight on the performance and user experience of existing methods were later used to develop a solution. To ensure validity and reliability, questions were designed to be specific and directly related to achieving the survey’s objective.

Sufficient context on the problem would be provided as well. The survey was designed without any bias to achieving desired results by phrasing questions neutrally.

3.3

Phase 3 – Creation and development of solution

Instead of delving into resolving errors present in the existing Dynamo scripts, other computational tools such as Rhino and Grasshopper available were considered as well. A secondary qualitative analysis was conducted on existing data to share on the thought process behind selecting software or technology to create the solution. Existing journal articles were reviewed and selected based on their relevancy to the problem. In this case, existing data on comparisons of computational tools available was conducted over the course of half a week.

Secondary qualitative analysis was later conducted on existing data that discusses how to utilise the selected computational tools. Over the course of half a week, online guides on Dynamo and Python were studied in depth.

Dynamo Primer was used to understand the user interference, installation, anatomy of a Dynamo script and other functions. Next, online tutorial videos on how to use Python Scripting for Dynamo were studied. Topics covered include accessing the Revit application programming interface (API) and basic operators such as variables, loops and functions. A list of common nodes, Dynamo functions and Python functions were compiled in a Dynamo dyn file for reference. At this stage, the existing Dynamo scripts were improved on based on the knowledge obtained through the secondary qualitative analysis conducted. Detailed steps to develop the solution were documented using note-taking and presented under the results section. The steps represent the new workflow for the design and modelling of the sanitary discharge system.

An experiment, a quantitative method, was conducted to assess the quality of work produced by existing Dynamo scripts. An existing project’s Revit file was retrieved from consultancy firm X and sanitary discharge components were inserted into the project file as Revit families.

A sanitary discharge system detailing the location of components was designed to be used for 12

Experiment 1, 2 and 3. The number of errors that occurred during Experiment 1 was recorded through personal observation. Testing conditions under Experiment 1 were designed according to tasks that the script must fulfil where accuracy would be evaluated using a pass or fail criteria. Quantitative data recorded were analysed and presented in the same table. The modelling of the sanitary discharge system in the existing project’s Revit file and Experiment 1 were completed over a week.

The two scripts were tediously edited to pass the testing conditions and reduce the number of work hours required to complete the modelling process of the same design used in Experiment 1. Steps taken to develop the two scripts were documented under the results section and improvements to the scripts were carried out for two weeks. A second experiment, Experiment 2, was conducted on the developed scripts and the traditional workflow with the same testing conditions, Revit project file and analysis method. Quantitative

data on the

accuracy of the developed scripts and the increase in work productivity were compared to the results from Experiment 1.

3.4

Phase 4 – Evaluating the performance and adoptability of solution

Despite conducting experiments personally to evaluate the effectiveness of the developed scripts compared to the existing Dynamo script, the developed scripts were not compared to both existing methods and it was hard to evaluate their adoptability without the help of test participants. Experiment 3, Survey 2 and Survey 3 were developed and conducted during across the span of two and a half weeks. Qualitative and quantitative data on work productivity, quality of work and adoptability were collected and analysed at this stage.

Experiment 3, a quantitative method, was conducted to measure the number of work hours required by test participants to complete the design provided. The population and sample size is the same as Survey 1 for consistency. Due to limitations such as time constraint and lack of access to BIM professionals during their non-working hours, a sample size of 5 BIM professionals and 5 students from Singapore Institute of Technology (SIT) were selected to participate in the survey and experiment. Since the end users of the developed solution include competent Revit users in both relevant working industries and educational institutions, the sample was deemed to be appropriate as students were still end users of the products. Purposive sampling, a non-probability sampling method, was used to remain consistent with the previous 13

sample from the Survey 1 conducted in phase 2. Participants were referred to as ‘Participant 1’ to ‘Participant 10’ as their names do not affect the analysis in this study. In this experiment, ‘Participant 5’ to ‘Participant 10’ were students that replaced BIM professionals from the participants in Survey 1 who were unavailable. The sampling confidence interval and margin of error are 95% and 5% respectively.

The same sanitary discharge system design and existing project Revit’s file used for Experiment 1 and 2 were used for this Experiment to provide consistency and aid in the comparison of results. A stopwatch was used to record the time number of work hours required.

Second, the number of errors that occurred during each test was recorded through personal observation. It was expected that the experiment might be prone to human error as mistakes made might be missed after facilitating multiple experiments per day. Screen recordings were captured to remove the possibility of human error by reviewing the recordings to spot the occurrences.

Survey 3 was conducted after the participants completed their experiments to re- evaluate their user experience. The structure and organization of Survey 3 is the same as Survey 1 and the only difference was the inclusion of a final closed-ended question. The survey was conducted in the form of an online questionnaire on Google Forms where the link was provided after their experiment was completed. The UEQ approach used for Survey 1 was used for Survey 3 as well where qualitative data on user experience were keyed into the UEQ data analysis tool. The final closed-ended question was included after the list of questions from the UEQ. Participants would rank the two existing workflows and developed solution from first to last in terms of their preference of usage using lists of questions with only one possible answer. Results were analysed based on the mean, standard deviation, lower and upper confidence interval (ci) which in this case is 95ci. The data received was exported onto an excel spreadsheet and manually calculated to present results in the same table form.

To ensure validity and reliability, questions were designed to be specific and directly related to achieving the survey’s objective. Sufficient context on the problem would be provided as well. The survey was designed without any bias to achieving desired results by phrasing questions neutrally. Variables were eliminated by conducting Experiment 3 in the same environment using the same tools. Experiment 3 was conducted on a laptop at an empty room 14

with no distractions. Test participants were informed to place their mobile phones away during the estimated 20 minutes experiment.

Prior to the experiment, a guide on the sanitary discharge system was developed to ensure that test participants understood the problem background, design requirements and were made aware of existing workflows with the inclusion of screen recordings. Test participants had to read a second guide on the steps to follow during Experiment 3 and complete Survey 2 which includes only one closed-ended binary question on whether they have fully understood what they are to complete for the experiment or uncertainties are still present. They would only proceed with the experiment after confirming that there are no further uncertainties. The guide and Survey 2 were used to ensure eliminate factors such as inexperience from students who are introduced to the sanitary discharge system during the experiment that might affect the test results. Survey 2 was designed without any bias to increase its reliability as well.

A method to measure improvements to labour productivity when existing Dynamo workflow and new Dynamo workflow were used. Previous studies in the construction industry discovered a lack of a standardized definition for productivity. where labour productivity was measured by dividing the number of work hours over the quantity for one scientific paper while it was measured by dividing quantity per man hour for others. It was difficult to apply the calculations provided as not only did they vary but it was difficult to measure modelling in the form of quantity. In addition, the time spent on the development of the solution would not be considered. Through further research, work productivity would be quantified using the formula shown: Number of work hours saved or wasted per year = [Number of times tool is used] x [Time saved with each run compared to the manual method] – [Development time]. The calculation of work productivity would be used as an insight and not a full representation as multiple assumptions must be made.

4.0

RESULTS

Adopting the methods and approaches stated, results from the data collection and analysis across the same phases were presented.

4.1

Phase 1 – Understanding workflows and design requirements 15

After coding the documented notes on the workflow for the sanitary discharge system, four themes repeated across the various observations were identified. They are the design process, modelling process using a traditional workflow, modelling process using existing Dynamo scripts and the design requirements. A full transcription of the interview of the design process and modelling process using a traditional workflow is shown in Figures 2 and 3 below.

Figure 2: Transcript of Interview with MEP Design Engineer – Part 1

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Figure 3: Transcript of Interview with MEP Design Engineer – Part 2

MEP Design Engineer X commented that there were no issues regarding the two processes documented based on the observations and informal discussions. The comment provided evidence that the workflows on the design process and modelling processes were accurate and valid. However, multiple corrections were made regarding the design requirements observed and detailed under 4.1.4.

4.1.1 Design process The design process for the sanitary discharge system are as follows. Step one, MEP engineers would locate all components of the system and provided this information through comments 17

on a portable document format (PDF) file of a basement or ground level floorplan. On the same file, the location of components such as inspection chambers and waste sumps were shown. Only the depth of the first inspection chamber or waste sump, designed according to design requirements from the COP, was provided as the depth of connecting inspection chambers or waste sumps would be calculated based on the first component. Step two, pipe routings, pipe size and slope gradient of the pipes would be provided and designed according to design requirements from the COP. Step three, the design and comments would be provided to the building information modelling (BIM) engineers and modellers to model the design using software such as Revit.

4.1.2 Modelling process – Traditional workflow After receiving the PDF comments from the MEP engineers, BIM professionals would open the model in the software that the architecture layout was designed in. Since the sanitary drainage system involves multiple components, only the connections for inspection chambers, waste sumps, gully traps, grease traps and manholes were analysed in detail. Waste sump to waste sump connections modelled in Revit are shown and annotated in Figure 4 to aid in the discussion on the traditional workflow at consultancy firm X.

Sanitary Pipe

WS1

WS1

WS2

TL1 -1.47

TL1 -1.47

Floor Slab

WS2

Sanitary Pipe

Figure 4: Waste sump to waste sump connections on Revit

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Step one, the right version of the software, in this case Revit, would be opened and the first waste sump or inspection chamber would be placed at the specified location in Revit as an element. The first waste sump is annotated as WS1 in Figure 4. Step two, the BIM professional would check if the internal depth of the first inspection chamber or waste sump complied to the design requirements from the COP. If the internal depth provided is incorrect, he would ask the MEP Engineer if changes can be made. Step three, a section view shown on the right in Figure 4 would be cut using the section tool in Revit to check on the alignment of the element as there are times when it is above or below the floor slab. The element’s alignment has to be adjusted accordingly if it is incorrect.

Step four, the second waste sump or inspection chamber would be placed at the designated location as an element. The second waste sump is annotated as WS2 in Figure 4. The third step is repeated for the second element or the previous section view could be extended to display the section of the second element if possible. Step five, the distance from the internal edges of waste sump to waste sump connections or inspection chamber to inspection chamber connections would be measured. The Revit aligned dimension tool is used to draw a dimension where the measured distance signifies the pipe length. If the distance does not meet the design requirements from the COP, adjustments would have to be made. Step six, the internal depth of second waste sump or inspection chamber would be calculated manually. The formula used for calculating the internal depth of inspection chambers, waste sumps, gully traps, grease traps and manholes are presented below:

IL2 = IL1 – L/G Internal Depth of First Element = TL2 – IL2

IL2 > IL1 Internal Depth of Next Element = TL1 – IL1

where IL1 refers to the internal level of the first element, IL2 refers to the internal level of the next element that is deeper than the first. L refers to the distance from centre to centre of the two elements measured, G refers to the gradient of slope, TL1 refers to the top level of the first element and TL2 refers to the top level of the next element. TL and IL are obtained after annotating the elements with spot elevation on Revit and are shown in Figure 4.

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The formula is repeatedly used to calculate each internal depth of the next element as the TL of the next element is only adjusted after the calculated internal depth value is keyed in as an input under the element’s properties. Depending on the number of elements in the design, calculations may be used from 10 to 100 times for a single design. The second waste sump or inspection chamber’s internal depth would be edited on Revit based on the calculated value.

The BIM professional would check if the adjusted element’s internal depth meets the design requirements according to COP and consult the MEP engineer if changes are to be made. Step seven, a section view would be cut to prepare for the alignment of pipes.

Step eight, the BIM professional would align the starting point of the pipe to the bottom of the first element. The pipe gradient, size and material would have to be adjusted according to the PDF comments before modelling. Design requirements for the pipe gradient, size and material varies depending on the type of elements for connection. For example, an IC to IC connection compared to an IC to manhole connection has different design requirements. If they do not comply with the design requirements of the COP, the MEP engineer has to be notified of the error again. Pipe connections would be modelled to start from the internal edge of the first element to the next. The size, discipline, gradient and pipe length would be annotated.

Step nine, the component connected to the second element would be placed at the specified location in Revit as an element. Steps two to nine would be repeated until all connections are completed. The next element would always be the second element unless the pipe connecting to it is sloping in an upwards direction. If that is the case, the deeper element would be the second element and calculations have to be made accordingly. The seventh step has to be repeated for the next element or the previous section view could be extended if possible.

The elements could be grease traps, gully traps or manholes as well. Step ten, a final check must be made to check the inspection chamber to inspection chamber connections and inspection chamber to manhole connections. A hydraulic drop is present when the height difference of the wastewater entering the element from exit point of the pipe to the bottom of the element is insufficient or too large. If the hydraulic drop does not meet or exceeds the design requirements, a backdrop or tumbling bay connection is required where additional fittings are connected to the pipe entering the element after the downwards sloping pipe. If rework on the model is notified at any point during this workflow or after review, the steps have to be repeated accordingly.

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4.1.3 Modelling process – Existing Dynamo workflow Through informal discussions held with the BIM Management at the consultancy firm X, it was discovered that a second existing method used for modelling the sanitary discharge system was recently developed. Two scripts were developed using Dynamo where the first script generates pipes connected in a network from one component to another while the second script directs all pipes to the component with the lowest depth. The input form generated by the scripts and the Dynamo interface are shown in Figure 5 to aid in the discussion on the traditional workflow at consultancy firm X.

Dynamo

Input Form

Inspection Chambers

Manhole

Figure 5: Waste sump to waste sump connections on Revit Step one to six of the existing Dynamo workflow are the same as the traditional modelling workflow. They would be repeated until all components are placed and adjusted accordingly on Revit. Step seven, Dynamo displayed on the Manage tab is selected to open its interface.

Step eight, the respective script is selected and opened as shown in Figure 5. The BIM professional would click the ‘Run’ button to run the script and an input form as shown in Figure 5 would be displayed. Step nine, the BIM professional would input the design requirements provided by the MEP engineer for pipe creation. This includes pipe type, pipe system type, pipe slope and pipe diameter. Then, there is a need to check for compliance with the design requirements from the COP and ask the MEP engineer if changes could be made if necessary.

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Step ten, the ‘Select’ button on the input form would be clicked and the BIM professional would highlight the elements to generate pipe connections between. When he is done, he would click ‘Finish’ displayed on Revit and ‘Create Pipe’ on the input form. Inspection chambers and manhole were highlighted in Figure 5. The connections generated would depend on the selected Dynamo script. Step eleven, the final check on inspection chamber to inspection chamber connections and inspection chamber to manhole connections would be conducted. Similar to the traditional workflow, a backdrop or tumbling bay connection is required if the design requirements for hydraulic drop are not met. Step eleven, the size, discipline, gradient and pipe length of the generated pipes would be annotated. Similar to the traditional workflow, if rework on the model is notified at any point during this workflow or after review, the steps have to be repeated accordingly. The design considerations are consistent regardless of what workflows are used.

4.1.4 Design requirements The interview with the MEP Design Engineer revealed incorrect design requirements gathered through observations and informal discussions to be corrected. The first design requirement where the last inspection chamber should be located close to the manhole was verified by the MEP Design Engineer 1 to be correct. The second design requirement was documented as follows. The internal depth of the first waste sump connected with a floor trap should be more or equal to 750mm for the fitting to run under slab as the floor trap with P trap fitting height and basement floor slab were 600mm. If the first waste sump is connected to a floor waste or is not connected to a floor trap, the internal depth should be more or equal to 450mm.

The internal depth of the first inspection chamber should be more or equal to 1000mm and if these are not met. MEP Design Engineer commented that the internal depth of the first waste sumo and adjusted waste sumps should be more or equal to 450mm instead. Also, the internal depth of the first IC and adjusted ones should be more or equal to 750mm.

It was documented that pipe gradient of inspection chamber to inspection chamber connections are either 1 is to 60 or 1 is to 90 while the pipe gradient of waste sump to waste sump connections are either 1 is to 90 or 1:120 or 1:200. MEP Design Engineer 1 commented that inspection chamber to inspection chamber pipe gradients should range from 1 is to 30 to 1 is to 90 instead. Waste sump to waste sump connections would generally follow the same 22

requirements and inspection chamber to manholes would use a gradient of 1 is to 120.

The fourth design requirement was documented as follows. Pipe length of waste sump to waste sump connections should be less or equal to 20m while that of inspection chamber to inspection chamber connections are less or equal to 50m. Since MEP Design Engineer 1 did not comment on the statement during a pause for his response, the interview was resumed, and the design requirement was interpreted to be correct. The fifth design requirement was documented as follows. Waste sump to waste sump pipe connections should have a diameter of 150mm, inspection chamber to inspection chamber pipe connections should have a diameter of 200mm and inspection chamber to manhole connections should have a diameter of more or equal to 200mm and are made of vitrified clay. However, MEP Design Engineer 1 commented that both waste sump to waste sump and pipe connections and inspection chamber to inspection chamber pipe connections should be all have a diameter of more or equal to 150mm. This is applicable for connections to grease trap, carpark waste sumps, gully traps and ejector pits too. Inspection chambers to manhole connections instead should have a diameter of more or equal to 200mm.

The sixth design requirement was documented as follows. Waste sump to waste sump and inspection chamber to inspection chambers pipe connections are made of cast iron while Inspection chamber to manhole connections and are made of vitrified clay. MEP Design Engineer 1 commented that inspection chamber to manhole connections were correct, but all other connections should be made of ductile iron instead. The seventh design requirement was that a tumbling bay connection to the manhole is required for inspection chambers with an internal depth of more or equal to 500mm and less or equal to 1500mm while a backdrop connection is needed for inspection chambers with an internal depth exceeding 1500mm. MEP Design Engineer 1 highlighted that according to the new 2019 COP for sanitary and sewerage works, a tumbling bay or backdrop connection can be used for a hydraulic drop of more or equal to 500mm not exceeding 6000mm for IC to IC or IC to manhole.

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4.2

Phase 2 – Evaluating the performance and user experience of existing workflows

The mean value per item analysed from UEQ questions found in Survey 1 for the traditional workflow are shown in Figure 6.

Mean value per Item -3

-2

-1

1

2

3

not understandable/understandable difficult to learn/easy to learn not interesting/interesting slow/fast conventional/inventive obstructive/supportive

usual/leading edge not secure/secure does not meet expectations/meets expectations

Figure 6: Mean value per item of traditional workflow from Survey 1 Results would either have a “negative evaluation” where the mean value is less than 0.8, “neutral evaluation” where the mean value between -0.8 and 0.8 or a “positive evaluation” where the mean value is more than 0.8. The minimum and maximum result of the mean value is between -3 and +3. Figure 6 shows that there were more negative evaluations regarding the traditional workflow with an average mean value of -1. The top three negative evaluations were that it was found to be dull, boring, and conventional. The top three positive evaluations were that it was understandable, meets their user expectations and easy to learn.

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The mean value per item analysed from UEQ questions found Survey 1 for the existing Dynamo workflow are shown in Figure 7.

Mean value per Item -3

0

1

2

3

not understandable/understandable difficult to learn/easy to learn not interesting/interesting slow/fast conventional/inventive obstructive/supportive

usual/leading edge not secure/secure does not meet expectations/meets expectations

Figure 7: Mean value per item of existing Dynamo workflow from Survey 1 Figure 7 shows that there were more positive evaluations towards the existing Dynamo workflow. However, the evaluations were only slightly positive with an average mean value of 1. The top three positive evaluations were that it was found to be enjoyable, understandable and fast while the bottom three negative evaluations were that it was not secure, demotivating and unattractive. UEQ benchmarks analysed from UEQ questions found in Survey 1 for the traditional workflow are shown in Figure 8. Survey results were categorized according to the six scales: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty.

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Excellent Good Above Average Below Average Bad Mean

Figure 8: UEQ benchmark of traditional workflow from Survey 1 Based on the benchmark found in Figure 8, the attractiveness, efficiency, dependability, stimulation and novelty of the traditional workflow were found to be bad while the perspicuity of the traditional workflow was found to be below average. The stimulation of the traditional workflow had a mean value of -1 while its novelty had a mean value below -1. UEQ benchmarks analysed from UEQ questions found in Survey 1 for the existing Dynamo workflow are shown in Figure 9.

Excellent Good Above Average Below Average Bad Mean

Figure 9: UEQ benchmark of existing Dynamo workflow from Survey 1 Based on the benchmark found in Figure 9, the attractiveness, perspicuity, dependability and stimulation were found to be bad. Novelty of the existing Dynamo workflow was below average while the efficiency of the existing Dynamo workflow was found to be above average. Survey results on the frequency of projects which require the modelling of sanitary discharge system, likelihood of using the existing Dynamo workflow and features to integrate into the solution were tabulated into Table 1.

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Table 1: Results from Survey 1 Survey Questions

N/10 Response Mean

SD

95 ci

Rate upper lower Q1. How often do you receive projects from MEP Design Engineers that require the modelling of sanitary discharge system?

Daily 0

0

0

0

0

Once a week 2 0.2 0.42 0.46 -0.05 More than once a week 7 0.7 0.48 1.00 0.40 100% Once a year 1 0.1 0.32 0.30 -0.20 Never 0 0 0 0 0 Q2. Do you foresee yourself using the existing Dynamo scripts instead of the traditional method to generate pipe connections for the sanitary discharge system?

Yes 3 0.3 0.48 0.59 0.01 100% No 7 0.7 0.48 1.00 0.40 Q3. Rank the features would you like to see added to the existing Dynamo scripts and traditional method.

A. Generated Calculations First Choice 6 0.6 0.52 0.92 0.28 Second Choice 1 0.1 0.32 0.30 -0.20 100% Third Choice 3 0.3 0.48 0.59 0.01 B. Automatic Alignment of Pipes First Choice 1 0.1 0.32 0.30 -0.20 Second Choice 3 0.3 0.48 0.59 0.01 100% Third Choice 6 0.6 0.52 0.92 0.28 C. Automatic Checks for Compliance First Choice 3 0.3 0.48 0.59 0.01 Second Choice 6 0.6 0.52 0.92 0.28 100% Third Choice 1 0.1 0.32 0.30 -0.20 Q4. We have the ability to develop a solution that adds the features listed in Q5. Would you be interested in using the developed solution to model the sanitary discharge Yes 10 100% No 0 0 0 0

0

Survey participants feedbacked that projects that required the modelling of sanitary discharge systems were received more than once a week. That response selected had a mean value of 0.7, upper confidence interval of 1 and lower confidence interval of 0.4. The next response 27

with a mean value of 0.2 was that projects were received once a week. When asked if they foresee themselves using the existing Dynamo scripts instead of the traditional method to generate pipe connections for the sanitary discharge system, the ‘No’ response had a mean value of 0.7, upper confidence interval of 1 and lower confidence interval of 0.4.

When analysing the responses to whether they would like to see generated calculations added to the existing workflows, ‘First Choice’ response obtained a mean value of 0.6, upper confidence interval of 0.92 and lower confidence interval of 0.28. This was followed by ‘Third Choice’ with a mean value of 0.3 and ‘Second Choice’ with a mean value of 0.1. When asked if they would like to see automatic alignment of pipes added to the existing workflows, ‘Third Choice’ response obtained a mean value of 0.6, upper confidence interval of 0.92 and lower confidence interval of 0.28. This was followed by ‘Second Choice’ with a mean value of 0.3 and ‘First Choice’ with a mean value of 0.1. When asked if they would like to see automatic checks for compliance added to the existing workflows, ‘Second Choice’ response obtained a mean value of 0.6, upper confidence interval of 0.92 and lower confidence interval of 0.28.

This was followed by ‘First Choice’ with a mean value of 0.3 and ‘Third Choice’ with a mean value of 0.1.

4.3

Phase 3 – Creation and development of solution

The creation and development of the solution involved multiple such as considerations made to utilize other methods and tools, findings from Experiment 1, creation of solution, new Dynamo workflow and calculated labour productivity. Results were thus presented under the following six sections.

4.3.1 Consideration on utilising other methods and computational BIM tools A solution that reduces the number of labour hours required, improves the quality of work produced and fulfils the needs of the users was required. The first idea was to create an Excel spreadsheet to be used as a calculator for the internal depth of the elements. However, it only tackled the issue of manual calculations and the repetitive process such as placing of elements into Revit, editing element properties and aligning pipe connections remained unchanged. Limitations such as time constraint on researching for other solutions, the level of programming skills required by all competent users to utilize the solution and my personal level of programming skills for the development of the solution had to be considered. In 28

addition, the development cost and adoptability of the software are to be considered as some firms currently do not own such software or utilize them for their projects. After much deliberation, it was decided that existing Dynamo scripts would be improved on for this project.

As it is a visual programming tool, no programming languages would be required for its utilization. Dynamo has the highest adoptability at present as it is in-built in Revit software and Python scripting could be integrated into Dynamo to write custom coding through text scripts with basic programming skills.

4.3.2 Understanding the creation of existing Dynamo scripts Two scripts of different uses, existing Dynamo script 1 used to generate pipes connected in a network from one element to another and existing Dynamo script 2 used to directs all pipes to the element with the lowest depth were currently used. After reviewing online tutorials and Dynamo Primer guide, dynamo nodes and common functions were documented under Appendix A. The nodes and functions obtained were used to analyse the creation of the existing scripts. The steps to create the existing Dynamo scripts were documented in Appendix B. 4.3.3 Results from Experiment 1 The first storey floor plan of an existing project’s Revit file was firstly edited to create a clean model to use for Experiment 1, 2 and 3. Figure 10 shows the designed sanitary discharge system indicating the location of components. The first component’s depth and pipe connections are provided as well.

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IC3

IC2

IC1 (First WS) Internal Depth – 750mm

WS6

WS5 WS1 (First WS) Internal Depth – 700mm

WS2

WS3

WS4

Figure 10: Designed sanitary discharge system for experiments

30

Tests were conducted on the model created and their pass and fail results during Experiment 1 were documented under Table 2.

Table 2: Results from Experiment 1

Table 2 shows that a total of 3 errors each were generated from the Dynamo existing scripts.

Pipe connections from one waste sump, WS, to another waste sump of different top levels where the first waste sump with a higher top level’s internal depth was less than the second waste sump with a lower top level’s internal depth were found to be inaccurate for both existing Dynamo scripts. Pipes generated had the wrong sloping directions and started from the lowest point of the wrong waste sump. Multiple waste sump connections to one waste sump from different directions in a network to one waste sump from different directions in a network for components of the same top level had pipe connections that were generated incorrectly and at 31

times not at all for both Dynamo scripts. Lastly, waste sump to grease trap pipe connections were not generated for both existing Dynamo scripts as well.

4.3.4 Creation of the solution Two Dynamo scripts were ultimately developed as the final deliverables of this project. The new Dynamo Script 1 is an improved script from the existing Dynamo script 1 where generated pipes are connected in a network from one component to another. On top of resolving errors generated in Experiment 1, The improved scripts included features such as generated calculations, automatic alignment of pipes and automatic checks for compliance to design requirements. Figure 11 shows the nodal connections for the new Dynamo script 1.

Figure 11: Dynamo Script 1 nodal connections Nodes were grouped based on their functions where green represents the main functions of generating pipes, generating calculations and aligning pipes to the bottom of the prior component while orange represents checks for compliance to design requirements where messages would be displayed on Revit when design requirements were not met. The script was able to check compliance to internal depth, pipe gradient, pipe length, pipe size and pipe 32

material design requirements. Compliance to design requirements such as hydraulic drop and locating the last inspection chamber close to the manhole were not checked when the new Dynamo script 1 was used. Additional features such as filtering only sanitary components for the selection and displaying components in order were included. The new Dynamo Script 2 is an improved script from the existing Dynamo script 2 where generated pipes are directed to the component with the lowest depth. The improved scripts were created by resolving errors and including features as well. Figure 12 shows the nodal connections for the new Dynamo script 2.

Figure 12: Dynamo Script 2 nodal connections Similarly, the nodes were grouped based on their functions with the same colour coding. Due to time constraints, the new Dynamo script 2 integrated only one compliance checks on the internal depth design requirements. Compliance to design requirements such as pipe gradient, pipe length, pipe size, pipe material, hydraulic drop and locating the last inspection chamber close to the manhole were not checked when the new Dynamo script 2 was used.

33

Additional features such as filtering only sanitary components for the selection and displaying components in order were included.

4.3.5 Modelling Process –New Dynamo Workflow After creating the new Dynamo scripts, the new Dynamo workflow where the scripts were used to complete the designed sanitary discharge system was analysed. Step one, the right version of the software, in this case Revit, would be opened and all the waste sumps and inspection chamber would be placed at the specified location in Revit as elements. They are annotated according to the given design as well where the waste sumps are annotated as WS1 to WS 6 and the inspection chambers are annotated as IC1 to IC3. Step two, section views would be cut using the section tool in Revit to check on the alignment of the element as there are times when it is above or below the floor slab. The element’s alignment has to be adjusted accordingly if it is incorrect. Step three, the first waste sump or inspection chamber’s internal depth would be edited. The BIM professional would check if the internal depth of the first inspection chamber or waste sump complied to the design requirements from the COP. If the internal depth provided is incorrect, he would ask the MEP Engineer if changes can be made. Step four, Dynamo Player displayed on the Manage tab is selected to open its interface.

Step five, the respective script is selected and opened as shown in Figure 13.

Dynamo Player Input Form

Waste Sumps

Figure 13: Waste sump to waste sump connections based on design and edited model 34

The BIM professional would click the play icon to run the script and an input form as shown in Figure 13 would be displayed. Step six, messages are displayed to prompt the BIM professional to select the components in their desired orders. When he is done, he would click ‘Finish’ displayed on Revit. Step seven, the BIM professional would input the design requirements provided by the MEP engineer for pipe creation. This includes sloping direction, pipe sloping gradient, pipe type, pipe system type, reference level for pipe and pipe diameter.

The next is to check if they comply to the design requirements from the COP and ask the MEP engineer if changes could be made if necessary.

Step eight, the BIM professional would click ‘Generate’ on the input form. Pipe connections would be generated according to the inputs provided. Step nine, a final check on inspection chamber to inspection chamber connections and inspection chamber to manhole connections would be conducted. Similar to the traditional workflow, a backdrop or tumbling bay connection is required if the design requirements for hydraulic drop are not met. Step ten, section views would be cut using the section tool in Revit to check on the alignment of the generated pipes. Step eleven, the size, discipline, gradient, and pipe length of the generated pipes would be annotated. Similar to the traditional workflow, If rework on the model is notified at any point during this workflow or after review, the steps have to be repeated accordingly.

4.3.6 Findings from Experiment 2 Tests were conducted on the model created and their pass and fail results during Experiment 1 were documented under Table 3.

35

Table 3: Results from Experiment 2

0

36

Table 3 shows that no errors were generated from both new Dynamo scripts. Test conditions 11 to 18 were developed to conduct further testing on the quality of work when the new Dynamo scripts were in use. Certain test conditions such as multiple waste sump connections to 1 waste sump from different directions in a network were deleted to provide flexibility while other test conditions such as multiple waste sump connections from the same direction in a network for components with different top levels and multiple waste sump connections to one waste sump from different directions for components with different top levels were covered in the added test conditions.

4.4

Phase 4 – Evaluating the performance and adoptability of the solution

Survey results on the understanding of the experiment guide provided before Experiment 3 were tabulated into Table 2.

Table 2: Survey 2 results on understanding experiment guide Survey Question on

N/10 Response Mean

Understanding of Experiment Guide

SD

95 ci

Rate upper lower

Q1. Do you have any other uncertainties before proceeding with the experiment?

Yes 2 0.2 0.42 0.46 -0.05 100% No 8 0.8 0.42 1.06 0.54 When asked if they had any other uncertainties before proceeding with the experiment, the ‘No’ response obtained a mean value of 0.8, upper confidence interval of 1.06 and lower confidence interval of 0.54. The ‘Yes’ response obtained a mean value of 0.2, upper confidence interval of 0.46 and lower confidence interval of -0.05.

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The mean value per item analysed from UEQ questions found in Survey 3 for the traditional workflow is shown in Figure 14.

Mean value per Item -3

0

1

2

3

not understandable/understandable difficult to learn/easy to learn not interesting/interesting slow/fast conventional/inventive obstructive/supportive

usual/leading edge not secure/secure does not meet expectations/meets expectations

Figure 14: Mean value per item of traditional workflow from Survey 3 Figure 14 shows that there were more negative evaluations towards the traditional workflow.

The evaluations were found to be highly negative with an average mean value of -1. The bottom three negative evaluations were that it was found to be dull, usual and conservative while the top three positive evaluations were that it met their user expectations, was easy to learn and was secure.

38

The mean value per item analysed from UEQ questions found in Survey 3 for the existing Dynamo workflow is shown in Figure 15.

Mean value per Item -3

0

1

2

3

not understandable/understandable difficult to learn/easy to learn not interesting/interesting slow/fast conventional/inventive obstructive/supportive

usual/leading edge not secure/secure does not meet expectations/meets expectations

Figure 15: Mean value per item of existing Dynamo workflow from Survey 3 Figure 15 shows that there were more negative evaluations towards the existing Dynamo workflow with an average mean value between 0 and -1. The bottom three negative evaluations were that it was found to be unattractive, unfriendly, and dull while the top three positive evaluations were that it was pleasant, organised and efficient.

39

The mean value per item analysed from UEQ questions found in Survey 3 for the new Dynamo workflow is shown in Figure 16.

Mean value per Item -3

0

1

2

3

not understandable/understandable difficult to learn/easy to learn not interesting/interesting slow/fast conventional/inventive obstructive/supportive

usual/leading edge not secure/secure does not meet expectations/meets expectations

Figure 16: Mean value per item of solution from Survey 3 Figure 16 shows that there were no negative evaluations towards the existing Dynamo workflow where all participants had positive evaluations with an average mean value of 2. The top three positive evaluations were that it was found to be clear, innovative, and meeting their expectations.

40

UEQ benchmarks analysed from UEQ questions found in Survey 3 for the traditional workflow is shown in Figure 17.

Excellent Good Above Average Below Average Bad Mean

Figure 17: UEQ benchmark of traditional workflow from Survey 3 Based on the benchmark found in Figure 17, the attractiveness, perspicuity, efficiency, dependability, stimulation and novelty of the traditional workflow were all found to be bad.

Attractiveness of the traditional workflow had a mean value of -1 while factors such as efficiency, stimulation and novelty had a mean value below -1. UEQ benchmarks analysed from UEQ questions found in Survey 3 for the existing Dynamo workflow are shown in Figure 18.

Excellent Good Above Average Below Average Bad Mean

Figure 18: UEQ benchmark of existing Dynamo workflow from Survey 3 Based on the benchmark found in Figure 18, the attractiveness, perspicuity, dependability, stimulation and novelty of the existing Dynamo workflow were found to be bad while its efficiency was evaluated as below average. UEQ benchmarks analysed from UEQ questions found in Survey 3 for the new Dynamo workflow are shown in Figure 19.

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Excellent Good Above Average Below Average Bad Mean

Figure 19: UEQ Benchmark of Solution from Survey 3 Based on the benchmark found in Figure 19, the attractiveness, perspicuity, efficiency, dependability, stimulation and novelty of the traditional workflow were all found to be excellent with an average mean value of 2. The results from Experiment 3 measuring the number of errors, number of queries and time taken to complete the drawings were next tabulated into Table 4.

Table 4: Experiment 3 results Experiment Results

N/10 Response Mean

SD

95 ci

Rate Number of Errors Generated Number of Queries Raised Time Taken to Complete [mins]

10 100% 10 100% 10 100%

1.2 1.5 6.5

upper 1.03 1.84 1.08 2.17 1.08 7.17

lower 0.56 0.83 5.83

A mean value of 1.2 with an upper confidence interval of 1.84 and lower confidence interval of 0.56 was obtained when observing the number of errors generated by each participant during Experiment 3. A mean value of 1.5 with an upper confidence interval of 2.17 and lower confidence interval of 0.83 was obtained when observing the number of queries raised by each participant during the experiment. A mean value of 6.5 with an upper confidence interval of 7.17 and lower confidence interval of 5.83 was obtained when measuring the time taken to complete with a stopwatch. Survey results on the likelihood of using the new Dynamo workflow for the sanitary discharge system were tabulated into Table 5.

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Table 2: Survey 3 results on likelihood of using the solution Survey Questions on

N/10 Response Mean

Adoptability of Solution

SD

95 ci

Rate

upper lower Q1. Do you foresee yourself using the developed Dynamo scripts instead of the existing methods for modelling of the sanitary discharge system?

Yes 10 100% No 0

1 0

1 0

1 0

1 0

A mean value of 1 for the ‘Yes’ response was obtained when participants were asked if they foresee themselves using the developed Dynamo scripts instead of existing methods for modelling of the sanitary discharge system. After conducting the survey and experiments, labour productivity was calculated. To determine the number of work hours saved per year, it was assumed that the projects in each year were all similar to the existing project model used for the experiments in terms of size and complexity of sanitary discharge system design. Other assumptions were made for the number of times the Dynamo scripts would be used per year, consistent number of hours saved with the use of Dynamo and an equal number of hours used to develop the scripts. Users may only utilize the Dynamo scripts occasionally due to preference or whether the design is simple enough to be completed using the manual modelling method. Not to mention, other solutions may be developed within that year where improvements are made to the existing script as such is the case for this project. Calculations using the labour productivity formula based on the listed assumptions are as follows.



Estimated timed spent to complete the manual method = 30 minutes = 0.5 hours



Number of times new Dynamo workflow is used = 720 times if used three times a week by 5 workers



Time saved with each run of new Dynamo workflow (Table 4) = 6.5 minutes = 0.108 hours Development time = 2 weeks



Number of work hours saved or wasted for the first year using the new Dynamo workflow = [720 times x (0.5– 0.108)] - (2 x x 5) = 282 – 112 = 170 hours



Number of work hours saved or wasted for the second and following years using the new Dynamo workflow = 282 hours 43

5.0

DISCUSSION

This section provides an insight into the observed results from the experiment conducted to test the effectiveness and efficiency of the developed solution.

5.1

Phase 1 – Understanding workflows and design requirements

While the design process for the sanitary discharge system consisted of only three steps, the modelling process using existing workflows were found to be tedious. In total, there are ten steps required for the modelling process when the traditional workflow is being used. Although ten steps are listed, many steps are repeated from 10 to 100 times per project. Some of which includes the cutting of section views, measurement of centre to centre for components, manual calculations for the internal depth of components and alignment of pipes to the bottom of prior components. Analysing the existing Dynamo workflow, a total of eleven steps were listed.

Although it appeared to have more steps than the traditional workflow, some steps such as the alignment of pipes to the bottom of the prior component were eliminated. The steps to run the existing Dynamo script were much shorter than the process of aligning pipes individually.

Overall, the existing Dynamo script managed to generate pipe connections but did not manage to eliminate many other repetitive steps as listed. The documented workflow was verified by MEP Design Engineer 1 to be correct during the interview.

In total, there are seven design requirements for the sanitary drainage system that BIM professionals must be aware of. They are internal depth, pipe gradient, pipe length, pipe size, pipe material, hydraulic drop and that the last inspection chamber has to be close to the manhole. Recorded design requirements from observations conducted on five BIM professionals and MEP engineers working on projects with sanitary discharge systems were proven to be incorrect through an interview held with MEP Design Engineer 1. This highlights a concern where although BIM professionals are familiar with the workflow of a sanitary discharge system, design requirements were not effectively communicated between MEP engineers and BIM professionals. This ultimately affects the quality of works as non-compliant projects may be submitted.

5.2

Phase 2 – Evaluating the performance and user experience of existing workflows

Positive evaluations and negative evaluations are reflections of the users’ experience when 44

utilizing the existing workflows. When data collected from UEQ questions in Survey 1 were found to have more positive evaluations, it means that the survey participants feel that there are more advantages than disadvantages brought about by utilizing the workflows.

Likewise, if more negative evaluations are present, it means that the survey participants feel that there are more disadvantages than advantages when using such workflows.

From the results of the analysis on mean value per item and UEQ benchmark for the traditional workflow, BIM professionals felt that the traditional workflow was disadvantageous for certain characteristics while advantageous for others when compared to the existing Dynamo workflow. Perspicuity and dependability of the traditional workflow was found tot be better than the existing Dynamo workflow while efficiency, stimulation and novelty of the Dynamo workflow was found to be better than the other. This shows that while BIM professionals felt that the existing Dynamo workflow requires lesser effort to complete the sanitary discharge system, is more interesting to use and is innovative, there are still chances that they may continue to utilize the traditional workflow as it is understandable, easy to use and they feel more in control when using the conventional method that they are familiar with. From this interpretation, it is possible that the existing Dynamo workflow may not be adopted as adoptability and user experience are correlated.

From the results gathered from other questions in Survey 1, it was discovered that BIM professionals often receive projects requiring the modelling of sanitary discharge systems thrice a week. Its frequency of use supports the need for this study to produce a innovative solution increases the effectiveness of the process. Another survey question discovered that most BIM professionals did not want to use the existing Dynamo scripts despite its capabilities in improving efficiency. Qualitative data gathered from the UEQ questions were supporting evidence as other characteristics such as its dependability and ease of use are considered when selecting a workflow to use as well. When analysing features to integrate into the solution, most BIM professionals preferred generated calculations, followed by automated checks for compliance and lastly alignment of pipes. Their preference of features to be integrated could signify that the present workflows for such functions may be problematic and are to be improved on.

5.3

Phase 3 – Creation and development of solution 45

Dynamo was selected as the desired BIM tool to be used for the creation and development of solution in this project. This was as previous research have supported that Dynamo is highly adoptable at present as it is in-built in Revit software and allows integration with other programming languages. The two new Dynamo scripts developed were then tested

in

Experiment 1 to identify present errors to resolve. Although the existing Dynamo script was developed to reduce the time spent on the modelling process and improve the quality of work, it was found to be not as effective as imagined. The script focused only on pipe generations and the steps for calculation of each element’s internal depth still had to be done manually. In addition, there were occurrences when multiple errors were present such as incorrect sloping direction, pipes generated that do not meet the design requirements and pipes that were not generated at all. This would ultimately affect the quality of work delivered to the clients.

After developing the solution, the new Dynamo workflow consisted of eleven steps. However, many repetitive steps such as cutting of section views, measurement of centre to centre for components, manual calculations for the internal depth of components and alignment of pipes to the bottom of prior components were eliminated. Simply steps remained such as placing the components into Revit, selecting the components, inputting their properties, generating pipe connecting and automatic adjustments of internal depth. Despite adding more

testing

conditions for the new Dynamo scripts, no errors were generated when running the scripts.

This shows that not only do the new Dynamo scripts resolve errors, the quality of work produced is also improved by integrating additional features that eliminate repetitive work and human error.

5.4

Phase 4 – Evaluating the performance and adoptability of solution

When providing participants with the experiment guide, most of them did not have any questions. However, some did have queries and this highlighted a need to improve on the clarity of the provided guide. From the results of the analysis on mean value per item and UEQ benchmark for the three workflows, BIM professionals felt that the existing workflows were disadvantageous. While the existing Dynamo workflow was still found to be more advantageous than the traditional workflow, poorer user experiences were recorded when compared to the previous UEQ benchmarks from Survey 1. Both existing workflows scored lower in terms of their ease of usage and effort required to complete the modelling of the sanitary discharge system. The felt less in control of the processes and felt that the existing 46

workflows were more uninteresting and conventional than before. This shows that when in comparison with the new Dynamo workflow, users would have a different user experience as the new workflow influences their perceptions.

BIM professionals now had the greatest number of positive evaluations for the new Dynamo workflow. When compared to the other two workflows, the new Dynamo scripts were found to be exceedingly advantageous, obtaining excellent across all six scales. The survey participants felt that the solution designed was easy to use, required little effort to complete the modelling of the sanitary discharge system, interesting, innovative and were still able to still feel a sense of control while using the scripts. Quantitative data such as the number of errors, number of queries raised and time taken to complete the modelling of the sanitary discharge system are useful indicators of the labour productivity of existing and new workflows. During the experiment, one to two errors and were generated on average with a mean value of 1.2. One to two queries were raised during the experiment as well. In the construction industry, such queries are also known as ‘Request for Information’ which is a factor contributing largely to project delay. Fortunately, there were few errors and requests for information during Experiment 3, highlighting that the new Dynamo solution was accurate and clear. On average, participants took 6.5 minutes to complete the given task which is fairly low compared to the estimated 10 minutes allocated for them.

All of the participants for Experiment 3 agreed that they could foresee themselves using the developed Dynamo scripts instead of existing methods for modelling of the sanitary discharge system. The new Dynamo workflow proved to not only excel in its user experience but was equally effective in improving the quality of work and time spent on completion of the system. When translating the results to labour productivity, multiple assumptions had to be made on the number of projects with sanitary discharge systems received per year, complexity of design according to the size of project and frequency of use. After making the assumptions stated, the new Dynamo workflow as found to save up to 170 labour hours during its first year of use and up to 282 labour hours during the following years as development time no longer has to be considered. The calculated labour hours are significant on an organizational scale as they reflect the cost savings that could be generated.

6.0 CONCLUSION 47

The modelling of sanitary discharge system is a crucial area to be improved on. When analysing existing workflows used a consultancy firm, the traditional modelling workflow was observed to be tedious and inaccurate as discrepancies such as incorrect calculations or misalignments during the repetitive work resulted in multiple revisions. Adjustments of element properties and calculations must then be redone with every revision as well. While the existing Dynamo workflow was proven to be more efficient to use as compared to the traditional workflow, it had a low adoptability where the existing scripts were barely utilized as users perceived them to be hard to use and ineffective in increasing their work productivity.

The new Dynamo workflow developed in this study provided users a better work experience than the existing modelling workflows. The new Dynamo workflow significantly reduced labour hours required for completing the given modelling tasks accurately.

ACKNOWLEDGEMENT The support of the Singapore Institute of Technology in carrying out this applied research study is gratefully acknowledged. Ms. Chua Jing Ting Jamie did the work and contents of this paper as part of her BEng final year design project in the Sustainable Infrastructure Engineering (Building Services) programme. Dr. Moshood Olawale Fadeyi guided the development of the prototype solution and experimental design to test the effectiveness of the developed solution.

Dr. Fadeyi also contributed to the development of this article.

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Baldwin, A. N., Shen, L. Y., Poon, C. S., Austin, S. A., and Wong, I. (2008). Modelling design information to evaluate prefabricated and pre-cast design solutions for reducing construction waste in high rise residential buildings. Automation in Construction, 17(3), 333-341.

Batarseh, S.,and Kamardeen, I. (2017). The impact of individual beliefs and expectations on BIM adoption in the AEC Industry. EPiC Series in Education Science, 1, 466-475.

Chen, W., Chen, K., Cheng, J. C., Wang, Q., and Gan, V. J. (2018). BIM-based framework for 48

automatic scheduling of facility maintenance work orders. Automation in Construction, 91, 1530.

Chidiebere, E., and Ebhohimen, I. (2018). Impact of Rework on Building Project and Organisation Performance: A View of Construction Professionals in Nigeria. International Journal of Sustainable Construction Engineering Technology, 9(1), 29-44.

Fan, S. L., Skibniewski, M. J., and Hung, T. W. (2014). Effects of building information modeling during construction. Journal of Applied Science and Engineering, 17(2), 157-166.

Ignatova, E., Zotkin, S., and Zotkina, I. (2018, June). The extraction and processing of BIM data. In IOP Conference Series: Materials Science and Engineering, 365(6), 062033. IOP Publishing.

Khosakitchalert, C., Yabuki, N., and Fukuda, T. (2018). The accuracy enhancement of architectural walls quantity takeoff for schematic BIM models. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, 35, 1-8. IAARC Publications.

Li, J., Hou, L., Wang, X., Wang, J., Guo, J., Zhang, S., and Jiao, Y. (2014). A project-based quantification of BIM benefits. International Journal of Advanced Robotic Systems, 11(8), 123.

Love, P. E. D. (2002a), Auditing the indirect consequences of rework in construction: a case based approach. Managerial Auditing Journal, 17 (3), 138–146.

Love, P. E. D. (2002b), Influence of project type and procurement Method of Rework Costs in Building Construction Projects. Journal of Construction Engineering and Management, 128 (1): 129.

Love, P. E., and Edwards, D. J. (2004). Determinants of rework in building construction projects. Engineering, Construction and Architectural Management 11(4), pp.259 -274.

Nezamaldin, D. (2019). Parametric design with Visual programming in Dynamo with Revit: The conversion from CAD models to BIM and the design of analytical applications [Ebook] 49

(p. 7). Stockholm: KTH Royal Institute of Technology.

Poirier, E. A., Staub-French, S., and Forgues, D. (2015). Measuring the impact of BIM on labor productivity in a small specialty contracting enterprise through action-research. Automation in Construction, 58, 74-84.

Rahman, I. A., Memon, A. H., Karim, A. T. A., and Tarmizi, A. (2013). Significant factors causing cost overruns in large construction projects in Malaysia. Journal of Applied Sciences, 13(2), 286-293.

Reinhardt, J., and Mathews, M. (2017). The automation of BIM for compliance checking: a visual programming approach. CITA BIM Gathering 2017, November 23rd-24th November 2017, Croke Park, Dublin, Ireland.

Röck, M., Hollberg, A., Habert, G., and Passer, A. (2018). LCA and BIM: Visualization of environmental potentials in building construction at early design stages. Building and Environment, 140, 153-161.

Santoso, H. B., Nurrohmah, I., Suci, F., and Goodridge, W. H. (2017). Evaluating and Redesigning the Self-Monitoring Tool. International Journal on Advanced Science, Engineering and Information Technology, 7(1), 228.

Schrepp, M., Hinderks, A., and Thomaschewski, J. (2014). Applying the user experience questionnaire (UEQ) in different evaluation scenarios. In International Conference of Design, User Experience, and Usability, 383-392. Springer, Cham.

Vilutiene, T., Hosseini, M. R., Pellicer, E., and Zavadskas, E. K. (2019). Advanced BIM applications in the construction industry. Hindawi Advances in Civil Engineering, 1-3.

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50

SUPPLEMENTARY INFORMATION (SI) SI- A: Design and modelling workflow of existing methods.

SI- B: Steps to create existing Dynamo scripts SI- C: Presentation slides

51

SI- A: Design and modelling workflow of existing methods.

Common Nodes Node Dictionary Type of Nodes

Input

Output

Point.ByCoordinates

x – default value: 0

point

Form point by 3 given coordinates

y – default value: 0 z – default value: 0

Points.DeconstructPoint

point

x – x coordinate

Returns the X,Y,Z coordinates of a

y – y coordinate

point

z – z coordinate

Number

NIL

>

NIL

>

NIL

>

NIL

>

(Input Node) Create number



Enter value

String (Input Node) Series of characters or text



Enter text

Number Slider (Input Node) Dynamic numeric value for whole and decimal numbers



Slide to number, edit min max and step if needed.

Integer Slider (Input Node) Dynamic numeric value for whole numbers Slide to number, edit min max and step if needed.

52

Range

start – number / letter

Create a sequence of numbers or

to start sequence at

letters in the specified range

default value: 0

seq – New sequence

end – number / letter to end sequence at default value: 9 step – space between the numbers or letters, default value: 1 Sequence

start – number / letter

Create a sequence of numbers

to start sequence at

seq – New sequence

default value: 0 amount – amount of numbers in the sequence, default value: 10 step – space between the numbers or letters, default value: 1 Watch

>

>

NIL

NIL

+

x – x value

var[]..[]

Add x to y

y – y value

-

x – x value

y subtract x

y – y value

*

x – x value

Multiplies x by y

y – y value

/

x – x value

Divides x by y

y – y value

(Output Node) Visualize output or result of node Boolean Selection between true / false

var[]..[] var[]..[] var[]..[]

53

==

x – x value

var[]..[]

Check if x = y

y – y value

Count

list – list of values

int

NIL

>

startPoint, endPoint

Line

NIL

Element(s) – the

Returns number of items in the specified list s

Code Block (Input Node) Allows for DesignScript code to be authored directly



Number: *value*;



Range: *value*..*value*..*value*;



Sequence: *value*..#*value*..*value*;



List: list[*value*];



Boolean: “true”; or “false”;



List of list: list[*value*][*value*]



Range from list of list: lst[*value*][*value...*value*]



Retrieve [0] listed first on list *x[0]*

Line.ByStartPointEndPoint Create straight line between two input points Select Model Element(s) (Input Node)

selected element(s)

Select model element(s) from document eg. Revit Select single / multiple elements from model

54

Element Types

NIL

(Input Node)

Types – the selected types

All element subtypes Drop selection for element types All Elements of Type

element type

Get all elements of specified type

Elements – all elements in the active document of a given type

All Elements in Active View

NIL

elements – get all

(Input Node)

visible elements in

Select all elements in active view

the active view

Categories

NIL

(Input Node)

Category – the selected category

All built-in categories Drop selection for categories All Elements of Category

Category

Elements – element type

Get all elements of specified category Family.Name

Family –

Name of this family

Revit.Elements.Family

Family Types

NIL

(Input Node)

string Family Type – the selected family types

All family types Drop selection for family types FamilyInstance.Location

familyInstance –

Gets the location of the specific

Revit.Elements.Family

family instance

Instance

All Elements of Family Types

Family Type

Point

Elements– the list of elements matching the query

55

Element.GetParameterValueBy

element –

Name

Revit.Elements.Elemen

Get value of one of the element’s

t

parameter

parameterName –

var[]..[]

name of the parameter to set Element.SetParameterByName

element –

Set one of the element’s

Revit.Elements.Elemen

parameters

t

Element

parameterName – name of the parameter to set value - number Element.TemporarilyIsolateinVi

view, element

view – return value

ew

element – return

Temporarily isolates elements in

value

the given view Element.Parameters

Element –

Obtain all of the Parameters from

Revit.Elements.Elemen

an Element

t

Element.OverrideColorinView

element –

Override the element’s color in the

Revit.Elements.Elemen

active view

t

Parameter[]

element

color – color to apply to a solid fill on the element Color.ByARGB

a – alpha value, default

Construct a color by alpha, red,

value: 255

green and blue components

r – red value, default

color

value: 0 g – green value, default value: 0

56

b – blue value, default value: 0 List.Create

item0 – item index #0

Makes a new list out of the given

item1 – item index #1

list

inputs List.GetItemAtIndex

list – list to fetch an

item – item in the list

Return item from given list at

item from

at the given index

specified index

index – index of the item to be fetched

List.TakeItems

list – list to take from

list – list of extracted

Fetches an amount of items from

amount – amount of

items

start of list

items to take. If negative, items are taken from the end of the list

List.FilterByBoolMask

list – list to filter

in – items whose

Filter a sequence by looking up

mask – list of

mask index is true

corresponding indices in a separate

Booleans representing

out – items whose

list of Booleans

a mask

mask index is false

List.Transpose

Lists – list to be

lists – a list of

Swap rows and columns in a list of

transposed, var[]..[]

transposed lists

List.AddItemtoFront

item – item to be

list – new list

Add item to beginning of list

added, can be an object

lists

or a list list – list to add to List.RemoveItemAtIndex

list – list to remove an

list – list with items

Removes an item from the given

item or items from

removed

list at the specified index

57

indices – index or indices of the item(s) to be removed List.SortByKey

list – list to be sorted,

sorted list, sorted

Sort list based on its keys

var[]..[]

keys

keys – list of keys, var[]..[] List.OfRepeatedItem

item – item to repeat

Creates a list containing given item

amount – number of times to repeat

the given number of times SortIndexByValue

list – list of values,

Sorts a specified list by the values

double[]

of its members in either ascending

ascending – bool

int[]

or descending order Flatten

list – list of values,

Return the flattened 1D list of the

var[]..[]

var[]..[]

multidimensional input list. If the input is a single value, returns that value.

Document.Current

NIL

Document

Document.ActiveView

document –

view

Get the active view for the

Revit.Application.Doc

document

ument

Isolated Pick Model Elements

refresh – bool, default

Elements – return

(From “Rhythm” Package)

value: false

value

Allow multiple selection of

category – var[]..[]

(Input Node) Get the current document

elements of specified category Springs.Collector.CurrentSelecti

refresh – bool, default

element(s) – return

on

value: false

value

58

(From “spring nodes” Package) Gets the currently selected elements. Connect a Boolean switch to refresh the selection Input

NIL

>

>

NIL

String from Object

obj – object to be

str – result of math

Convert an object to a string

serialized

computation

String.Concat

string0 – List of

str – string made of

Concatenates multiple strings into

strings to concatenate

strings

a single string

string1 – List of

(for Custom Nodes) (Input Node) Specify the type and default value for parameter



*x: double*

Double is a fundamental data type used to define numeric variables holding numbers with decimal points and is recognized as a type.

A double type can represent fractional as well as whole values Output (for Custom Nodes) (Input Node) Specify the type and default value for parameter

representation

strings to concatenate Pipe.ByLines

Lines – var[]..[]

Create a pipe by Line, PipeType,

PipeType – var[]..[]

SystemType and Diameter

SystemType – var[]..[]

Pipes – return value

Level – var[]..[] Diameter – var[]..[]

59

UI.SelectModelElements Data

InputName – string,

SelectModelElement

Create a Revit element selection

default value: “Input”

s input – return value

input for UI.MultipleInputForm++

ButtonText – string, default value: “Select Model Element(s)” CategoryFilter – var[], default value: “” SelectSingleElement – bool, default value: false

UI.DropDown Data

InputName – string,

DropDown input –

Create data for DropDown input in

default value: “Input”

return value

UI.MultipleInputForm++

Keys: [] – var[]..[] Values: [] – var[]..[] DefaultValueIndex – int, default value: null

UI.TextBox Data

InputName – string,

TextBox input –

Create data for TextBox input in

default value: “Input”

return value

UI.MultipleInputForm++

DefaultText – string, default value: “Default”

UI.MultipleInputForm++

Description_optional

User Inputs – return

Create a form with multiple inputs

– string, default value:

value

“”

Was Run – return

Logo_optional– var,

value

default value: “”

Was Cancelled –

ButtonText – string,

return value

default value: “Set Values” Inputs – var[]..[] Toggle – bool, default value: “false”

60

LinktoHelp_optional – string, default value: “null” CancelButtonText_op tional – string, default value: null Max Height_optional – int, default value: 0 Width_optional – int, default value:350 Excel Nodes File Path

NIL

>

path – path to the file

file – file object

Excel.ReadFromFile

file – represent the

data – rows of data

Read data from Excel by row and

Microsoft Excel

from the excel

return in a series of lists by row.

spreadsheet

worksheet

Value in cell A1 is [0,0]

sheetName – name of

(Input Node) Select a file on the system to get its filename -

Browse button

File.FromPath Create a file object from a path

worksheet containing data readAsStrings – toggle to switch between reading excel file as strings only or not, bool, default value: fale Excel.WriteToFile

filePath – file path to

data – data written to

excel spreadsheet

spreadsheet

61

Write data to Excel by row with

sheetName – name of

sublists written in successive rows.

workset to write data to

Value in cell A1 is [0,0]

startRow – enter 0 for row 1.1 for row 2 etc. startCol, data, – enter 0 for column A, 1 for column B, etc. data – data written to spreadsheet overWrite – bool, default valuet: false

62

Common Functions No. Function Watch/ List Numbers of Specified

Nodes -

Number > [start] [end] [step] Range > Watch

Range OR Watch/ List Numbers of Specified

-

Code Block > Watch

-

Number > [start] [amount] [step] Sequence > Watch

Sequence OR Select/ Obtain Items on List

-

Code Block > Watch

-

Watch > [list] List.GetItemAtIndex

-

Code Block > [index] List.GetItemAtIndex

-

List.GetItemAtIndex > Watch

OR -

Watch > Code Block > Watch

63

Select/ Obtain Items on List of List

-

Watch > [list] List.GetItemAtIndex

-

Code Block > [index] List.GetItemAtIndex

-

List.GetItemAtIndex > [list] List.GetItemAtIndex

-

Code Block > [index] List.GetitemAtIndex

-

List.GetItemAtIndex > Watch

OR

Lacing



Watch > Code Block > Watch



Code Block > Sequence >

Determines how the input port

Point.ByCoordinates >

should use the data. Bottom right

Line.BystartPointEndPoint

corner of node. Shortest / Longest / Cross Lacing.

-

Change lacing -

Change Lacing



Right click icon > Lacing

64

Search for Custom Nodes (Package Manager) -

Download and install package

-

Download and install package



Packages toolbar > select search for a package > search in search bar / right click sort by number of votes or downloads > click download icon

65

Convert Single / Multiple Nodes to



Select node / multiple nodes > right

Code

click on empty space > select node to

**Cannot revert code block back to

code

node

Create Custom Node – Create



Percentage Custom Node

File in Dynamo > New > Custom Node > Input Percentage for Name > Input Calculate the percentage value of one value in relation to another for Description > Input Core.Math for Category



Input *x:double* > [x] Code Block *(x/y)&100;*



Input *y:double* > [x] Code Block *(x/y)&100;* > String from Object > [string0] String.Concat



Code Block “%” > [string1] > Output “percentage”

66

Convert Existing Nodes to Custom



Nodes

Select node / multiple nodes > right click on empty space > select custom node



Input Name, Description and Category of Custom Node

67

Select All Elements in Project of Specified Category



Categories > All Elements of Category > Watch

68

Select All Elements in Project



System of Specified Element Type

Select All Elements in Project System of Specified Family Type

Element Types > All Elements of Type > Watch



Family Types > All Elements of Family Type > Watch

69

Get and Set Parameters -

Match height of one element to

-

other elements of specified category

Match height of one element to other elements of specified category



Select Model Element > [element] Element.GetParameterValueByName



Code Block “Top Offset”; > [parameterName] Element.GetParameterValueByName > Watch > [value] Element.SetParameterByName



Code Block “Top Offset”; > [parameterName] Element.SetParameterByName



Categories > All Elements of Category > [element] Element.SetParameterByName

-

Setting / Creating an element’s

-

parameter – Parameter comments for element

Setting / Creating an element’s parameter



Categories > All Elements of Category > [element] Element.GetParameterValueByName x 2



Code Block “Column Location Mark”; > [parameterName] > Watch Element.GetParameterValueByName



Code Block “Type”; > [parameterName] Element.GetParameterValueByName > Watch



Code Block “-“; > [B] Code Block *A+B+CC*

70



Watch > [A] [C] Code Block *A+B+C;* > Watch > [value] Element.SetParameterByName



Categories > All Elements of Category > [element] Element.SetParameterByName



Code Block “Comments”; > [parameterName] Element.SetParameterByName

71

Filter Element Selection using Filter



Revit VG > Isolate Walls

by Bool Mask – Isolate Partition



Categories > All Elements of Category

Walls View from All Walls

> [element] Elements.GetParameterValueByName



Code Block “Comments” > [parameterName] Element.GetParameterValueByName > Watch > [x] ==



Code Block “PartitionWall’ > [y] == > Watch > [mask]List.FilterByBoolMask



Categories > All Elements of Category> [list] List.FilterByBoolMask



[in] List.FilterByBoolMask > Watch > [element] Element.TemporarilyIsolateinView



Document.Current > Document.ActiveView > [view] View.TemporarilyIsolateElement

72

Isolate Picked Elements with Color Overlay – Isolate Walls and Overlay Color to Red -

Using Isolated Pick Model

-

Using Isolated Pick Model Elements

Elements



[true] Boolean > [refresh] Isolated Pick Model Elements



Categories > [Category] Isolated Pick Model Elements > [element] Element.OverrideColorInView



Code Block *255;* > [r] Color.ByARGB



Code Block *0;* > [g] Color.ByARGB



Code Block *0;* > [b] Color.ByARGB > [color] Element.OverrideColorInView



Run > Select elements in Revit View > Click Finish in Revit options bar

-

Using Springs.Collector.CurrentSelection

-

Using Springs.Collector.CurrentSelection



[true] Boolean > [refresh] Springs.Collector.CurrentSelection > [element] Element.OverrideColorInView



Code Block *255;* > [r] Color.ByARGB

73



Code Block *0;* > [g] Color.ByARGB



Code Block *0;* > [b] Color.ByARGB > [color] Element.OverrideColorInView



Select elements in Revit View > Hit Run

74

Obtain / Verify Element(s) Parameters



Select Model Element > Element.Parameters > Watch > Hit Run

Excel Functions

75

Load Data from Excel File to Create



Series of Points (Excel to Dynamo)

File Path > File.FromPath > [file] Excel.ReadFromFile



Code Block “Worksheet name in excel”: > [sheetName] Excel.ReadFromFile



[false] Boolean > [readAsStrings] Excel.ReadFromFile



Excel.ReadFromFIle > watch > List.Transpose > Watch > individual Code Blocks (eg. 3 code blocks) to get output > [x/y/z] Point.ByCoordinates

76

Load Data into Excel File (Dynamo



File Path > [filePath] Excel.WriteToFile

to Excel)



Code Block “Worksheet name to create in excel” > [sheetName] Excel.WriteToFile



Code Block > [startRow] Excel.WriteToFile



Code Block > [startCol] Excel.WriteToFile



Code Block “true;” > [overwrite] Excel.WriteToFile



Code Block “string name” > [item] List.AddItemtoFront



Code Block *sequence* > [list] List.AddItemtoFront



List.AddItemtoFront > Watch > [item0] [item 1] [item2] .. List Create > Watch > List.Transpose > Watch > [data] Excel.WriteToFile

77

SI- B: Steps to create existing Dynamo scripts Steps to create Dynamo script 1 1.

Create Page Printed on Revit



Code Block “Pipe Connection for IC or WS”; “Create Pipes”; “True”; “Cancel”; > [Description_optional] [ButtonText_optional] [Toggle] [CancelButtonText_optional] UI.MultipleForm++



List.Create > [Inputs] UI.MultipleForm++

78

2.

Create “Select IC or WS ONLY”



Option

Code Block “Select IC or WS ONLY”; “Select”; > [InputName][ButtonText] UI.SelectModelElements Data > [item0] List.Create

3.

Create “Select Pipe Type:” Option –



Element Type (PipeType) > All Elements of

Choose eg. PS02-

Type > [x] Code Block “Select Pipe Type”;

CatPB_DI_Generic_80 to 200

“x.Name;” > [InputName][Keys:[]] UI.DropDown Data

-

IC-IC/WS-WS: PS02CatPB_DI_Generic_80 to 200





All Elements of Type > [Values:[]] UI.DropDown Data > [item1] List.Create

WS TO WS: “Ø150 DICLP 1:90 _m” ≤20m, Ductile Iron



IC to IC: “Ø200 DICLP 1:90 _m” ≤50m, Ductile Iron

-

IC-Manhole: PS09CatSE_VC_Generic_200 to 300



IC to Manhole: “Ø300 DICLP 1:90 _m” ≥Ø200mm, ≤50m Vitrified Clay

79

➢ Normally Design Engineer request for Ø225mm

4.

Create “Select Pipe System Type:”



Element Type (PipeSystemType) > All

Option – Choose eg.

Elements of Type > [x] Code Block “Select

SANI06_SoilWaste_SWP

Pipe System Type”; “x.Name;” > [InputName][Keys:[]] UI.DropDown Data

-

All IC-IC/WS-WS/WS-Grease Trap/GT-IC/IC-



All Elements of Type > [Values:[]] UI.DropDown Data > [item2] List.Create

Manhole/GreaseTrap-IC/ICEjectorPit pipes: SANI06_SoilWaste_SWP

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5.

Create “Reference Level for Pipes:”



Option – Choose eg. BASEMENT 2

Element Type (Level) > All Elements of Type > [x] Code Block “Reference Level for Pipes”; “x.Name;” > [InputName][Keys:[]] UI.DropDown Data



All Elements of Type > [Values:[]] UI.DropDown Data > [item3] List.Create

6.

Create “Pipe Slope 1/” Text Box



Code Block “Pipe Slope: 1/”: “90”; >

with Default Text as 90

[InputName] [DefaultText] UI.TextBox



IC-IC Gradient: 1:60/ 1:90

Data > [item4] List.Create



WS-WS Gradient: 1:90/ 1:120/ 1:200

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7.

Create Text Box for Input of Pipe



Code Block “Pipe Slope: 1/”: “90”; >

Diameter with Default Text as 150

[InputName] [DefaultText] UI.TextBox



Data > [item5] List.Create

WS TO WS: “Ø150 DICLP 1:90 _m” ≤20m, Ductile Iron



IC to IC: “Ø200 DICLP 1:90 _m” ≤50m, Ductile Iron



IC to Manhole: “Ø300 DICLP 1:90 _m” ≥Ø200mm, ≤50m Vitrified Clay

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8.

Create Individual Lst for item0-



UI.MultipleInputForm++ [UserInputs] >

item5 from User Inputs

Code Block

lst[0] - Selected IC/ WS item0

*lst[0];lst[1];lst[2];lst[3];lst[4];lst[5];*

lst[1] - Selected PipeType item1 lst[2] - Selected PipeSystemType item2 lst[3] - Selected Reference Level for Pipe iem3 lst[4] - Pipe Slope Value Entered item4 lst[5] - Pipe Diameter Value Entered item5

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9.

Add Selected PipeType,



Code Block *lst[1];lst[2];lst[3]; * >

PipeSystemType, Reference Level

[PipeType][SystemType][Level][Diameter]

for Pipe and Entered Slope Value

Pipe.ByLines

and Diameter Value as Inputs for Pipe Creation

10. Obtain “WSP_ME_Internal Level”



Parameter of Selected IC / WS, Sort Elements from Deepest to Most

Code Block *lst[0]* > Flatten > [list] List.SortByKey



Shallow

Code Block “WSP_ME_Internal Level” > [parameterName] Element.GetParameterValueByName

Eg. IC / WS from most shallow to deepest called A-D



Code Block *lst[0]* > [element] Element.GetParameterValueByName > [keys] List.SortByKey

84

-

IC / WS are unsorted in list after selection

-

Obtain their internal levels and sort keys from smallest to biggest value (in this case deepest to most shallow as largest negative value to smallest negative value)

-

Sorted list is rearranged, following sorted keys order

11. Remove Most Shallow IC / WS and



Sort Remaining from 2nd Most Shallow -> Deepest

Code Block *false;* > [ascending] SortIndexByValue



List.SortByKey [sortedkeys] > [list] SortIndexByValue > Code Block *x[0]*; >

-

Sort keys in descending order of 3)

[indices] List.RemoveItemAtIndex >

-> 0) (in this case, most shallow ->

[element]

deepest)

Element.GetParameterValueByName

-

Remove most shallow IC / WS

-

Continue from previous list sorted

[parameterName]

from deepest -> 2nd most shallow

Element.GetParameterValueByName >

now

[list] SortIndexByValue



Code Block “WSP_ME_Internal Level”; >

85

-

Obtain internal levels of remaining



IC / WS, sort in descending order from 2) -> 0) (in this case 2nd most

SortIndexByValue > [keys] List.SortByKey



shallow -> deepest) -

Code Block *false;* > [ascending] List.RemoveItemAtIndex > [list] List.SortByKey

Sort keys from smallest to biggest value (in this case still 2nd most shallow -> deepest)

-

Sorted list is rearranged, following sorted keys order

12. Remove Deepest IC / WS and Sort



Remaining from Most Shallow -> 2nd Deepest

Code Block *true;* > [ascending] SortIndexByValue



List.SortByKey [sortedkeys] > [list] SortIndexByValue > Code Block *x[0]*; >

-

Sort keys in ascending order of 0) -

[indices] List.RemoveItemAtIndex >

> 3) (in this case, deepest -> most

[element]

shallow)

Element.GetParameterValueByName

-

Remove deepest IC / WS

-

Continue from previous list sorted

[parameterName]

from 2nd deepest -> most shallow

Element.GetParameterValueByName >

now

[list] SortIndexByValue

-

Obtain internal levels of remaining





IC / WS, sort in descending order from 2) -> 0) (in this case most nd

shallow -> 2 deepest) -

Code Block “WSP_ME_Internal Level”; >

Code Block *false;* > [ascending] SortIndexByValue > [keys] List.SortByKey



List.RemoveItemAtIndex > [list] List.SortByKey

Sort keys from smallest to biggest value (in this case still most shallow -> 2nd deepest)

-

Sorted list is rearranged, following sorted keys order

86

13. Obtain Point Coordinates, TL and IL of

2nd



Most Shallow -> Deepest

[sortedlist] List.SortByKey > FamilyInstance.Location >

IC / WS

Points.DeconstructPoint [x][y][z] > [x][y][z] Code Block

-

Get (x,y,z) point coordinates for

*Point.ByCoordinates(x,y,z +

nd

2 most shallow -> deepest IC /WS

InternalLevel);



Code Block “WSP_ME_Internal Level” >

-

TL = z coordinates

[parameterName]

-

IL = z coordinates + internal level

Element.GetParameterValueByName



[sortedlist] List.SortByKey > [element] Element.GetParameterValueByName > [internalLevel] Code Block *Point.ByCoordinates(x,y,z + Internal Level);

14. Obtain Point Coordinates, TL and



[sortedlist] List.SortByKey >

IL of Most Shallow -> 2nd Deepest

FamilyInstance.Location >

IC / WS

Points.DeconstructPoint [x][y][z] > [x][y][z] Code Block

-

Get (x,y,z) point coordinates for

*Point.ByCoordinates(x,y,z +

nd

most shallow -> 2 deepest IC /WS

InternalLevel);



Code Block “WSP_ME_Internal Level” >

-

TL = z coordinates

[parameterName]

-

IL = z coordinates + internal level

Element.GetParameterValueByName



[sortedlist] List.SortByKey > [element] Element.GetParameterValueByName >

87

[internalLeve]* Code Block *Point.ByCoordinates(x,y,z + Internal Level);

15. Create Lines Between Each IC / WS



Sloping Upwards

CodeBlock *Point.ByCoordinates(x,y,z + InternalLevel) from 13. > [startPoint] Line.ByStartPointEndPoint

-

Create 3 lines sloping upwards:



nd

CodeBlock *Point.ByCoordinates(x,y,z +

1) 2 Most Shallow -> Most Shallow

InternalLevel) from 14. > [endPoint]

2) 2nd Deepest -> 2nd Most Shallow

Line.ByStartPointEndPoint

3) Deepest -> 2nd Deepest 16. Create Lines Between Each IC / WS



Sloping Downwards

CodeBlock *Point.ByCoordinates(x,y,z + InternalLevel) from 14. > [startPoint] Line.ByStartPointEndPoint

-

Create 3 lines sloping downwards: nd

1) Most Shallow ->2 Most Shallow nd

nd

2) 2 Most Shallow -> 2 Deepest



CodeBlock *Point.ByCoordinates(x,y,z + InternalLevel) from 13. > [endPoint] Line.ByStartPointEndPoint

3) 2nd Deepest -> Deepest

88

89

17. Shorten Lines to Edges of IC /



WS, Obtain Start and End Points of Pipes

Line.ByStartPointEndPoint from 15. > [lst1] Shorten Lines to Edges of IC / WS Edges



CodeBlock *Point.ByCoordinates(x,y,z + InternalLevel) from 13. > [startpoint1] Shorten

-

From 3 lines sloping upwards, -300 to meet edges

Lines to Edges of IC / WS Edges



nd

of most shallow, 2 most nd

shallow and 2 deepest IC / -

Line.ByStartPointEndPoint from 16. > [line3] Shorten Lines to Edges of IC / WS Edges



Shorten Lines to Edges of IC / WS Edges

WS

*lst1.Direction;

Start line from edge of first 3

l1 = lst1.Length;

IC / WS and end at edges of

lst2 =

last 3 IC / WS

Line.ByStartPointDirectionLength(startpoint1, lst1.Direction, l1-300); l2 = lst2.Length; lst3 = Line.ByStartPointDirectionLength(lst2.EndPoint, line3.Direction, l2-300); lst3.StartPoint;

lst3.EndPoint;*

90

91

18. Obtain IL2 of 2nd Most Shallow to



Deepest IC or WS

Shorten Lines to Edges of IC / WS, Obtain Start and End Points of Pipes [lst3.StartPoint][lst.EndPoint] > [PT2][PT1]

IL2 = IL1 – L/G

Obtain IL2 of 2nd Most Shallow to Deepest

Internal Depth = TL2-IL2

IC or WS

 -

Obtain length of lines sloping up

[slope] Obtain IL2 of 2nd Most Shallow to

from 2nd Most Shallow -> Most

Deepest IC or WS

Shallow, 2nd Deepest -> 2nd Most

-

Code Block [lst[4]] > String.ToNumber >



Obtain IL2 of 2nd Most Shallow to Deepest

Shallow and Deepest -> 2nd

IC or WS

Deepest

*x1 = PT1.X;

Take z coordinates of first 3 IC /

y1 = PT1.Y;

WS - l/gradient

x2 = PT2.X;

Obtain new coordinates of edges nd

nd

y2 = PT2.Y;

of 2 most shallow, 2 deepest

z2 = PT2.Z;

and deepest IC / WS

Startpt = Point.ByCoordinates(x1,y1,0); Endpt = Point.ByCoordinates(x2,y2,0); CL

= Line.ByStartPointEndPoint(Startpt,Endpt); l = CL.Length; loverg = l/slope; Point.ByCoordinates(x1,y1,z2 - loverg);*

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SI- C: Presentation slides Click the link below to view the presentation slides. https://www.dropbox.com/s/gq3iyqx4j5y4ctr/Applied%20Research%20Project%20Presentati on.pdf?dl=0

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