Automated Real-Time Tracking and 3D Visualization of Construction Equipment Operation Using Hybrid LiDAR System Chao Wang, Ph.D. Candidate; Advisor: Dr. Yong K. Cho, Associate Professor Robotics & Intelligent Construction Automation Lab (R.I.C.A.L), School of Civil and Environmental Engineering
1. Introduction
3. Field Tests and Results
PROBLEM STATEMENT: • Unstructured work areas like construction sites are difficult to graphically visualize because they involve highly unpredictable activities and change rapidly
• Low data collection speed and low object recognition rates. • Difficult to rapidly extract the target area from background scattered noises in a large and complex 3D point cloud.
RESEARCH OBJECTIVES: The main objective of this research is to design, develop, and validate a 3D visualization framework to collect and process dynamic spatial information rapidly at a cluttered construction job site for safe and effective construction equipment operations.
2. Methodology
Surface Modeling Process (a) Pre-scanned point cloud of static site environment; (b) (b) A crane model embedded with the site environment
Total processing time with different α value for the case study
4. Findings and Conclusions
Framework of the proposed method
This research proposes fundamental research on 3D workspace modeling to foster a breakthrough innovation in robotic manufacturing and automated construction site operations which will greatly benefit the future U.S. construction industry and society in general. The knowledge obtained will enrich the literature in the important area of construction engineering and management and provide solutions to industry-driven problems. This research also proposes vigorous goals for integrating research and education.
Data transmission network configuration
Information Extraction and Automated Reasoning for Automated Regulatory Compliance Checking in the Construction Domain Jiansong Zhang, jzhang70@illinois.edu, Advisor: Nora El-Gohary, University of Illinois at Urbana-Champaign 1. Problem Statement
4. Preliminary Results
Manual checking of construction regulatory compliance is timeconsuming, costly, and error-prone. Automating the process of compliance checking is expected to reduce time, cost and error of the process. Previous research and development efforts in automated compliance checking (ACC) have paved the way, but they have been limited because 1) they require manual understanding, extraction, and formulation of regulatory requirements, and 2) they lack the level of knowledge representation and reasoning that is needed for compliance analysis and checking.
4.1 Implementation Method
4.4 Automated Reasoning Results
Information Extraction
Information Transformation
Automated Reasoning
Implementation Method of the Main Algorithms
4.2 Information Extraction Methodology and Results
2. Research Objective Preprocessing
Feature Generation
Target Information Analysis
Tokenization Sentence Splitting Morphological Analysis
POS Tagging Phrase Structure Analysis Gazetteer Compiling Ontology-Based Semantic Analysis
Target Information Identification Extraction Sequence Resolution
Evaluation
Theoretically/empirically developing methodologies/algorithms and corresponding knowledge representations that 1) automatically extract regulatory and project information from textual regulatory documents and BIM models, respectively, 2) encode the information in a semantic format, and 3) automatically reason about the information for compliance assessment.
Information Representation
Partial Automated Compliance Checking Results of International Building Code 2006
(1) Project BIM Model
(2) Regulatory Documents
(5) IE Algorithms
(6) ITr Algorithms
(7) AR Algorithms
5.1 Scientific Contributions
Development of Information Extraction Rules Extraction Execution
3. Research Method Utilizing a theoretically-based, empirically-driven methodology that leverages knowledge and techniques in natural language processing (NLP), semantic modeling and automated reasoning (AR) to develop methodologies/algorithms and corresponding knowledge representations for automated information extraction (IE), automated information transformation (ITr), and automated compliance reasoning for regulatory information and project information in the construction domain.
5. Scientific Contributions and Practical Significance
Development of Extraction Rules for Extracting Single Information Elements (Matching Pattern Construction; Feature Selection; Semantic Mapping;) Development of Rules for Resolving Conflicts in Extraction
Proposed Information Extraction Methodology
Information Extraction Results for Chapter 19 of International Building Code 2009 Semantic Compliance Deontic Quantity Subject Quantitative Comparative Quantity Quantity Information Subject Checking Operator Unit/ Total Restriction Relation Relation Value Restriction Element Attribute Indicator Reference Precision 0.941 1.000 0.935 0.979 0.947 1.000 0.976 0.976 1.000 0.969 Recall
0.941
0.833
0.956
0.958
0.931
0.935
0.976
0.953
0.900
0.944
F-Measure 0.941
0.909
0.945
0.968
0.939
0.967
0.976
0.964
0.947
0.956
4.3 Information Transformation Results Information Transformation Results for Chapter 12 of International Building Code 2006 Performance Measures
(8) Extracting Project Information (Logic Facts)
(9) Extracting Rules (Logic Rules)
(10) Testing Compliance
(11) Reporting Compliance
Concept Logic Clause Elements
Relation Logic Clause Elements
(3) Semantic Model
(4) NLP Techniques
Automated Compliance Checking Framework
Total
Precision Recall F-Measure Precision Recall F-Measure Precision Recall F-Measure
Top-Down Method
Bottom-Up Method
0.961 0.923 0.941 0.948 0.927 0.938 0.954 0.925 0.939
0.997 0.976 0.986 0.938 0.951 0.944 0.962 0.961 0.962
• Offer efficient-to-develop, semantic rule-based NLP methods for IE and ITr that can help capture domain-specific meaning; • Combine domain knowledge and NLP knowledge to achieve deep NLP; • Pioneer work in utilizing semantically deep NLP approach in the construction domain; • Provide benchmark semantic methods/algorithms for IE and ITr in the construction domain; • Offer a new mechanism for processing and transforming complex regulatory text into logic clauses; • Pioneer work in utilizing logic programming (LP) for the automated reasoning functionality in ACC in the construction domain. 5.2 Practical Significance • Reduce the time and cost of compliance checking in the construction domain; • Improve the accuracy of compliance checking; • Support other applications of automated information processing in the construction domain.
Modeling and Visualizing the Flow of Trade Crews Using Agents and Building Information Models (BIM) Lola Ben-Alon, slola@tx.technion.ac.il
The Faculty of Civil and Environmental Engineering
VClab
SeskinVirtual Construction Laboratory
Step #1 - Simulating LEAPCON(TM) construction game
Problem
First step was to develop of an agent based model to simulate the process incorporated in the LEAPCON game. The model was calibrated according to ďŹ eld observations of LEAPCON players, with respect to the players' motivations and production rate variability. This step has been implemented using the STARLOGO TNG tool for multi-agent simulations which provides a development environment within a 3D visual context. The agent-based simulation of the 6 Descrete Event Simulation LEAPCON game was developed with 18000 ABS with 4 agents 5 agents for the four independent specialty ABS with 5 agents 14000 4 ABS with 3 agents subcontractors, the client representative 3 10000 and the quality controller, And for each of Descrete Event Simulation ABS with 4 agents 2 the 32 apartments considered. The 6000 ABS with 3 agents 1 results show good calibration with ABS with 5 agents 2000 0 existing observed ďŹ eld data, and to the -2000 0 500 1000 1500 0 500 1000 1500 existing DES. The effects are shown by Time [sec] Time [sec] -6000 Fig. 5: LEAPCON Agent Based Simulation measuring Work In Progress (WIP), Cycle Fig. 3,4: Results of 1000 runs of the DES and the ABS Time (CT), cash ow patterns and efďŹ ciency of the operations.
Objective The main objective is to develop a visual simulation for studying and improving production control in construction processes, which accounts for individuals' decision making process and acquired knowledge. The goal is to make the model robust by attempting to calibrate it with ďŹ eld observations. Unlike the few existing research models, the simulation is situated in a realistic virtual environment modeled using BIM, allowing future experimental setups that can incorporate human subjects.
Cash Flow [$]
Simulation methods are useful for experimentation of production in construction because ďŹ eld experiments suffer difďŹ culties with isolating cause and effect. However, existing methods such as DES (Discrete Event Simulation) are limited in terms of their ability to model decision-making by individuals who have distinct behavior, context and knowledge representation.
WIP [units]
Assoc. Prof. Rafael Sacks, cvsacks@tx.technion.ac.il
Step #2 - Simulating ow of trade crews in Unity 3D An Agent-Based model developed in UNITY 3D game engine was created to simulate production control of a process in a full-scale building project. This step is being pursued in collaboration with a construction company. Data on workers' motivations, behavior and performance was collected using interviews and observations of a crew performing ďŹ nishing works in a high-rise residential tower project. This step presented the following challenges: • Correct observation and formulation of the variables and target function (motivations) of the agents. • Modeling the behavior of the agents while classifying professions. • Validation by calibration with actual performance.
Method The method employs agent-based simulation (ABS), with a “bottom-upâ€? approach to model the interactions between individual agents. It uses BIM models to deďŹ ne the physical and the process environment for the simulation. We apply agents programmed with decision making rules and utility functions to a to-be-built environment represented as a BIM. By varying parameters such as reliability between workers, thresholds for information gathering and approach to making-do in terms of risk, it is possible to generate aggregate system performance similar to those found in an actual building context in the construction
SigniďŹ cance
General Model The agents’ behavior was modeled using Behavior Trees (BT), an AI technique for modeling decision making used in commercial games, integrated with utility function. Moreover, the model reects the reliability of information, and distinguish between reality and perceived reality. SigniďŹ cantly, this allows the experimentation of uncertainty, lack of and ignored information
Lean construciton and BIM research has revealed the potential of novel ways to organize production on site that exploit the beneďŹ ts of pull ow and thorough yet exible planning. The simlation platform developed in this work is uniqly capable of testing their impact because it models the complex, emergent patterns of production behavior that result from the interaction of the myriad subcontracting teams and suppliers that perform construction work on and off site. In particular, the inuence of each participants' knowledge, context and motivations on their day to day decisions about resource allocation and work sequence can be modeled in the ABS, while they could not be modeled using DES. To-date, there has been no simple and reliable way to test different ideas for production control paradigms in construction. Employment
Formula 1: The subcontractor’s utility fuction
Work
S elect Where to Work
References Wait
G ather Info
Low U tility in Low C ertainty ? Is In S elected Location?
đ?‘˜ đ?‘– đ?‘Š đ?‘ˆ đ??ś đ?‘– đ?‘˜ đ?‘Š đ?‘Š
Maturity factor of apartment i Actual work performed on project i during any period T Unit price for the works at apartment i Unit cost of the materials for the works at apartment i Ratio of resources supplied to resources demanded Work promised/demanded by general contractor of project I in period T Work actually made available in period T, according to the sub-contractor Cost/unit of time for one unit of resources allocated by the subcontractor to đ??śđ?‘– project I, assumed constant over period T r (S, k m i Wi ) Work rate (units/time) for a single unit of resource R, which varies over time. đ?‘† đ?‘† Resource amount of units đ?‘‡ ,đ?‘— Transference time from apartment I to apartment j. đ?‘? Waste factor for materials that remain unused at the end of any period T đ?‘ž Brings matertials? đ?‘Š Project manager reliability đ?‘Š
During Day /Week?
Work N ot C ompleted?
M aterials A v ailable or E xpected?
S pace?
E quipment?
A bandon
Wait
Low U tility in Low C ertainty ?
A bandon
Work High U tility in Low C ertainty ?
Agent i A ccording to sub
S elect P otential A ctiv ity
A ccording to plan
S elect C ommunication M ethod
G ather Info
Legend
S elector
S equence Collision
Physiological Needs
Threshold for Rest/Food > Ô? ?
Is Other Agent on Sight?
Is Reliability OK?
Collide and Exchange Info
Rest/ Eat!
Employment
Reduce W orking Rate for T=t
Fig. 6,7: Visualizing the agents and the BIM in Unity 3D.
Random/Ratio
Esteem S elect N ext A ctiv ity in P lan
Is Manager on Sight?
Enhance W orking Rate
C ompute U is,C is
Threshold for Work O K? High U i, High C i
S elect Best A ctiv ity
G o to Workplace Location
G o to Workplace Lcation
Fig 1,2: The BT describing the individual’s behavior
C ondition
A ction
[1] Brodetskaia, I., Sacks, R., & Shapira, A. (2012). Stabilizing production ow of interior and ďŹ nishing works with reentrant ow in building construction. Journal of Construction Engineering and Management, 139(6), 665-674. [2] LEAPCON (2005), Technion LEAPCON management simulation game http://www.technion.ac.il/~cvsacks/tech-leap.htm , November 21, 2005. [3] Sacks, R., Esquenazi, A., & Goldin, M. (2007). "LEAPCON: Simulation of lean construction of high-rise apartment buildings. Journal of Construction Engineering and management", 133(7), 529-539. [4] Sawhney, A., Bashford, H., Walsh, K., and Mulky, A. (2003). “Agent based modeling and simulation in construction.â€? Proc., 35th Winter Conf. on Simulation, ACM, New York, Vol. 2, 1541–1547. [5] Watkins, M., Mukherjee, A., Onder, N., & Mattila, K. (2009). "Using agent-based modeling to study construction labor productivity as an emergent property of individual and crew interactions." Journal of Construction Engineering and Management, 135(7), 657-667.
Construction Research Congress (CRC) 2014
A quantitative investigation of building micro-level power management through energy harvesting from occupant mobility Neda Mohammadi, Tanyel Bulbul, John E. Taylor | {neda, tanyel, jet}@vt.edu
Motivation:
Research Questions:
Future Prospects:
Climate change mitigation strategies: min 3billion metric tones carbon pollution reduction by 2030¹ Interplay between energy efficiency strategies and energy harvesting from emerging renewable energy resources Macro-scale power management Power Grid - Wind Turbines - Solar Panels - Hydro-electric generators Is micro-scale power management possible at the building level?
Can energy harvesting from occupant mobility offset meaningful carbon emissions? Can accumulated energy harvested from building occupants’ mobility contribute to the electrical demand, and thus offset CO₂ emissions of buildings? Male Female Steps hrs. (88.7kg)⁶ (75.4kg)⁶ Sedentary⁷ Low active Somewhat active Active Highly active
Heel Strike ~2W
2500
31
15
19.167
4,753,857.50
483,000.00
6250
118
27
151.04
38,288,600.00
2,718,750.00
8750
302
94
577.5
145,239,062.50
10,395,000.00
11250
148
55
380.63
95,226,232.50
6,851,250.00
15000
32
11
107.5
26,958,330.00
1,935,000.00
631
202
1235.8
310,466,082.50
22,245,000.00
37075
9,313,982,475.00
667,350,000.00
KJ/month
9,313,982.48
667,350.00
kWh/month
2,587.22
185.38
1.83
0.131
tons/month
Initial Findings:
Harvesting the energy dissipated from occupants’ mobility through wearable technology
tons/month
Total CO2e:
1.956
1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
Patton Hall + Occupants⁵ Floor space: 52,750 ft² Construction: 1926-1929 South Atlantic Floors: 4 Wearable activity tracker device
1.956
Space Heating, 1.78 Cooling, 4.98
Computers, 3.55 Office Equipment, 1.42
Accumulated CO₂ offset
CO₂e - October 2013 CO₂e (tons)
Off the grid micro-level power management at building level
Individual CO₂ offset form occupants’ mobility
Pilot Study:
Other, 4.62
tons
Recent advances in bio-mechanical energy harvesting: Susp.-load backpack² ~15 watts Knee-mounted biomechanical energy harvester³ ~5 watts Piezoelectric energy scavenger shoes⁴ ~2 watts
Knee Bend ~5W
Refrigeration, 1.78
3.3%
Cooking, 0.04
Lighting, 13.86
Ventilation, 3.2 Water Heating, 0.36
CO₂ [m³ ton]⁹ Occupants Patton Hall
REFERENCES:
Potential disaggregated CO₂ emission offsets due to occupants’ mobility
¹ The President’s Climate Action Plan (2013). < http://www.whitehouse.gov/sites/default/files/image/president27sclimateactionplan.pdf > (accessed on April, 2014) ² Rome, L. C., Flynn, L., Goldman, E. M., & Yoo, T. D. (2005). Generating electricity while walking with loads. Science, 309(5741), 1725-1728. ³ Donelan, J. M., Li, Q., Naing, V., Hoffer, J. A., Weber, D. J., & Kuo, A. D. (2008). Biomechanical energy harvesting: generating electricity during walking with minimal user effort. Science, 319(5864), 807-810. ⁴ Krupenkin, T., & Taylor, J. A. (2011). Reverse electrowetting as a new approach to high-power energy harvesting. Nature communications, 2, 448. ⁵ COLLEGE OF ENGINEERING DATA BOOK 2012-2013, Virginia Tech (accessed on December 2013) ⁶ Anthropometric Reference Data for Children and Adults: United States 2007-2010 .< http://www.cdc.gov/> (accessed December 2013) ⁷ Tudor-Locke, C., Johnson, W. D., & Katzmarzyk, P. T. (2009). Accelerometer-determined steps per day in US adults. Medicine and science in sports and exercise, 41(7), 1384-1391. ⁸ Electricity Consumption (kWh) by End Use for Non-Mall Buildings, 2003, < www.eia.gov/ > (accessed December 2013)
Energy harvesting, conversion and exchange in a building-occupant system from occupants’ locomotion: as an alternative micro-scale energy system can be employed in buildings in support of off the grid micro-level power management. Public Infrastructure with high human mobility: in aggregate could offset a meaningful portion of building CO₂ emissions Harvesting energy generated by the human mobility of building occupants would take advantage of a vast and untapped renewable energy resource.
Ubiquitous adoption accumulated energy in large scale
Development of an Automated Progress Monitoring and Control System P.hD. Student: Reza Maalek (rmaalek@ucalgary.ca); Supervisors: Dr. Janaka Ruwanpura, Dr. Derek Lichti
1. Introduction/ Problem Statement TQM
a) Monitoring in practice
Construction Projects
(Golparvar-Fard et al. 2009a, Golparvar-Fard et al. 2011a, Maalek et al. 2014)
Manual
Time Consuming To Labor Intensive Justify Prone to Errors
Non-systematic Untimely identification of the causes of delays and cost overruns
Collection of Limited data
Inaccurate Performance Measure Increase chances of Claims Increase chances of Rework
b) Controlling in practice: (Golparvar-Fard et al. 2011b, McCullouch, B. 1997)
Highly dependent on the accuracy of the Monitoring stage 30-50% of site supervisor’s working time spent controlling
If Improved
Allocating More Safety Time to Improve Productivity and Communication
2. Background /Literature Review a) State-Of-the-Art Automated Monitoring: (Maalek et al. 2014) 3D coordinates of Structural Elements
Identification of “Scope of Work Performed”
b) How LiDAR works?
Camera (Photogrammetric Bundle Adjustment) 3D Range Imaging LiDAR
LiDAR Point Clouds
Object of Desire
3. Objectives and Methodology: Developing a “Fully Automated” Progress Monitoring and Control System comprised of the following stages: Feature Extraction
Monitoring
Controlling Scope of Work Performed
LiDAR
As-built Model Generation
Compare to As-planned
Addressing Three Major Concerns:
On Schedule
Ahead of Schedule
Behind Schedule
Rework due to poor quality
Scan #1 Scan observing the Maximum # of Facets: Assume Scan #n
1. Minimum # of Scan-stations:
Scan-station 1=Scan #n
?
Simulate LiDAR Point Clouds for the Scan-stations
:
Scan #n
As-Planned Model
Erin Dvorak
New As-planned Model
2. Automated Feature Extraction: Until a “Point Cloud” is generated for each Facet
As-built Model
?
:
As-built Point Cloud
Robustly Estimate Principal the Covariance Components
Define a neighborhood for each point
Eigenvectors Eigenvalues Smoothness Continuity Surface Shape
3. Automate Registration of As-built Model:
(X,Y,Z) (ω,φ,κ)
:
Minimum Requirements for Rigid body Transformation
As-planned Model
As-built Model
4. Experiments & Results:
Two convergent linear features (Preferably Orthogonal lines)
Three non-collinear points
Initial ICP Rigidbody Registration Transformation EOPs
Assign Points to Structural Elements Until EOP Stable
Closest points in the Planned and As-built Perform Rigidbody Transformation
Translation: (X,Y,Z) Rotation: (ω,φ,κ)
5. Contributions and Significance: Development of an automated Monitoring and Control System Construction Industry Benefits: 9 cm Point to Point a. Reduction of the project managers’ travel time (Giretti et al. 2009) b. Improving time, cost and quality of onsite data (Golparvar-Fard et al. 2009a) c. Reduction of preparation of Progress Reports (Golparvar-Fard et al. 2009b) Wall a) 2.4 cm Vertical d. Timely identification of work progress deviations (Maalek et al. 2014) Dimension b) 7.5 cm Horizontal e. Reducing Rework due to poor quality (Golparvar-Fard et al. 2011a) Compliance f. Reducing construction claims (Semple et al. 1994) g. Structural stability monitoring and control (Park et al. 2007) MRSE Accuracy
As-Built Generation 26 Walls 22 in the LOS 9.4
13.5
As-Planned Model
As-Built Model
Quantifying Human Mobility Perturbation
Qi Ryan Wang wangqi@vt.edu Advisor: Prof. John E. Taylor
Under the Influence of Tropical Cyclones BACKGROUND
RESULTS
• Tropical cyclones are becoming more frequent1,2 and intensified (2%-80%)3. Number of Trips
Frequency
• Hurricane Sandy: 202 deaths
(i) Travel
• Several events have caused significant fatalities, injuries, and damage in recent years.
Sandy (Cat. 3) in NYC r1=1-100m r2=100-500m r3=500m-1km
Oct. 29, 2012
• Typhoon Haiyan: 6000+ deaths
• How can human mobility perturbation be quantified?
Data Sample
Figure 1.
DATA COLLECTION
Oct. 29, 2012
Haiyan (Cat. 5) in Tacloban r1=1-100m r2=100-500m r3=500m-1km
r4=1-5km r5=5-10km r6=>10km
Other days
Average value from Oct. 30 to Nov.9, 2012
Nov. 5-7, 2013
Nov. 8-10, 2013
Average value from Nov. 11 to Nov. 28, 2013
Range of Δr
r4=1-5km r5=5-10km r6=>10km
Other days
Range of Δr
P(Δr )
Patterns
Oct. 29, 2012
Other days
Average value from Oct. 30 to Nov.9, 2012
Quantification
• Do tropical cyclones change human mobility patterns?
(ii) Movement
• Do tropical cyclones perturb human travel frequencies?
r1=1-100m r2=100-500m r3=500m-1km
Range of Δr
(iii) Perturb.
KEY QUESTIONS
Other days
Average value from Oct. 30 to Nov.9, 2012
• Typhoon Wipha: 39 deaths • Human mobility plays a key role in disaster evacuation, response and relief.
r4=1-5km r5=5-10km r6=>10km
Wipha (Cat. 4) in Tokyo
Δr(/m)
JSDS = 0.022 TS = ~24 hr
Oct. 29, 2012
Other days
Average value from Oct. 30 to Nov.9, 2012
Δr(/m)
JSDW = 0.063 TW = ~48 hr
Nov. 5-7, 2013
Nov. 8-10, 2013
Average value from Nov. 11 to Nov. 28, 2013
Other days
Δr(/m)
JSDH = 0.162 TH > 3 weeks
Figure 2. (i) Short-distance trips increased while long-distance trips reduced in Sandy and Wipha, but the changes are more chaotic during Haiyan. (ii) Movement data fit to truncated power-law distribution well for all three tropical cyclones; (iii) Quantifying both strength and duration of human mobility perturbation.
CONCLUSIONS • Tropical cyclones can cause human movement perturbation4,5. • Power-law still governs human mobility6,7 unless the storms are extremely powerful. • Quantification shows categories are associated with perturbation strength and duration. 4pm Oct. 29 to 4pm Oct. 30
4pm Oct. 29 to 4pm Oct. 30
REFERENCES: 1. Ferris, E., D. Petz, and C. Stark, The Year of Recurring Disaster: A Review of Natural Disasters in 2012, 2013, The Brokings Institution. 2. Landsea, C.W., et al., Impact of Duration Thresholds on Atlantic Tropical Cyclone Counts*. Journal of Climate, 2010. 23(10): p. 2508-2519. 3. Knutson, T.R., et al., Tropical cyclones and climate change. Nature Geoscience, 2010. 3(3): p. 157-163. 4. Bagrow, J.P., D. Wang, and A.-L. Barabasi, Collective response of human populations to large-scale emergencies. PloS one, 2011. 6(3): p. e17680. 5. Horanont, T., et al., Weather Effects on the Patterns of People's Everyday Activities: A Study Using GPS Traces of Mobile Phone Users. PloS one, 2013. 8(12): p. e81153. 6. Brockmann, D., L. Hufnagel, and T. Geisel, The scaling laws of human travel. Nature, 2006. 439(7075): p. 462-465. 7. Gonzalez, M.C., C.A. Hidalgo, and A.-L. Barabasi, Understanding individual human mobility patterns. Nature, 2008. 453(7196): p. 779-782.