AI In Construction
HOW FAR HAVE WE COME, AND WHAT LIES AHEAD?
Colophon
Published by: ConTech Lab - A Part of Molio
1st Edition: August 25th, 2024
2nd Edition: October 14th, 2024
1st Edition (English): November 1st, 2024
This publication is based on the results of a research project focused on the use of artificial intelligence in construction. The project was enriched with valuable input from the industry, gathered through four workshops held in spring 2024 focusing on the following topics:
1. Vision and inspiration
2. Impact
3. Strategy and implementation
4. Business models and collaboration
Collaborators: DI Byggeri, BLOXHUB, HD Lab and the Danish Authority of Social Services and Housing.
With contributions from: Poul Schmidt Kammeradvokaten and Nordfy
With support from: Realdania and the Danish Industry foundation
Original danish language version written by Rie M. Petersen. English edition translation by Artificial Intelligence with assistance of Chris Adam.
Graphic design and cover photo by Chris Adam.
This scrapbook focuses on generative artificial intelligence (GenAI) and how AI is used in construction today. We summarize experiences and recommendations from some partners with extensive knowledge of the industry’s use of digital solutions and AI. The scrapbook is specially designed for those of you who find AI interesting and ready to take the next steps toward integrating AI into your work.
We start from the point of where AI is today and also look at how it will develop tomorrow. The aim is to give you a clear understanding of what AI can do for the construction industry, how the
technology can be implemented, and how broad cooperation in the industry can promote valuable use of AI. The scrapbook is oriented toward practical application, and it contains an overview and guides based on inputs from four workshops in spring 2024 with over 100 participants.
In some contexts, AI is presented as an foreign and deterministic technology. In this scrapbook, we will simplify the technology basics and accommodate any skepticism. We hope this scrapbook gives you the courage to start using AI in your organization - maybe even on a higher level than email assistance from ChatGPT.
With AI, we have a mature, cheap and accessible technology available that can help solve problems we haven’t been able to solve before. This gives SMEs, in particular, the opportunity to gain access to specialist skills, thus strengthening their competitiveness and ability to innovate.
Ole Berard, ConTech Lab
Generated by Google Deepmind
01.1 The Project 01.2 Workshop Summary
About Artificial Intelligence
02.1 Definition Of AI
02.2 The History Of Generative Artificial Intelligence (GenAI)
02.3 Why Talk About GenAI?
02.4 Types of AI
02.5 AI Cheat Sheet
02.6 AI In Construction
02.7 Construction Phases
AI In The Real World
03.1 Work Processes
03.2 Ethics and Law
03.3.a Threats and Risk
03.3.b Opportunity and Potential
03.4 Intro To Cases
03.4.a Construction Planning
03.4.b Visualizations
03.4.c Design, Project Planning and BIM
03.4.d Facility Management Planning
03.4.e Inspection and Safety
03.4.f AI For Risk Management
03.4.g Information Management
03.4.h Offers and Contracts
03.4.i Administration and Office Tasks
03.4.j Internal Experiments
03.5 AI Cook Book
03.6 What Is Needed To Build An AI? Strategy and Roundup
04.1 Strategy
04.2 A Market For AI In Construction
04.3 Perspectives On AI
04.4 Joint Efforts On the Increased Use Of AI In Construction
04.5.a What Should I Take Away From This Scrapbook?
04.5.b Thanks...
01.1 The Project
We have investigated the industry’s use of AI, i.e., through Byggeriets Modenhedsmåling, interviews, and four different workshops to learn more about the potentials and the work of implementing AI in Danish construction companies and authorities. The four workshops organized by the project group created a framework for competent discussions and contributions from over 100 different representatives from the construction value chain. This scrapbook is built based on the above.
01.2
Workshop Summary
WS 1: Vision and Inspiration
This workshop focused on future uses of AI in construction and presented cases that showed AI’s potential to change design and construction processes.
Participants discussed technologies and shared visions for how AI can be integrated into the construction industry. The workshop showed that there is great potential for AI to optimize design phases and create innovative solutions.
WS 4: Business Models and Collaboration
In this workshop, business models for AI in construction were investigated and the participants discussed how companies can collaborate on AI development.
Based on cases from the AEC Hackathon, the participants got insight into concrete AI solutions, business opportunities, and the possibilities to use AI to support cooperation in the industry.
WS 2: Threats and Risk
The opportunities and risks of implementing AI in construction were reviewed. The discussions included safety aspects, potential job losses and the need to develop ethical AI systems that protect the user’s privacy and data.
The participants acknowledged, that although AI can lead to increased efficiency, there are also significant risks which must be handled carefully.
WS 3: Strategy and Implementation
This workshop was about developing strategies for AI implementation in construction projects. The participants learned to identify AI opportunities, plan and build AI solutions, and to define an AI strategy that can be scaled up over time.
It was emphasized that a successful implementation requires a clear roadmap and continuous adjustment based on learning and feedback.
Photo: Vaea Garrido
02.0 About Artificial Intelligence
02.1 Definition Of AI
Depending on the perspective, descriptions of AI can lead the mind to the deterministic science fiction movies we have seen many times. In addition, AI is a term used widely and for different purposes, which is why it can be difficult to define unambiguously. The purpose of developing AI ten years ago is different from the tools being developed today.
Therefore, AI is a dynamic concept that requires ongoing understanding. It is precisely for this reason that it is crucial to familiarize yourself with how the different types of AI work.
In this scrapbook, we work with a definition of AI, where AI is characterized by two basic properties: autonomy and adaptability.
Autonomous:
The ability of AI systems to perform tasks in complex environments without constant guidance from a user. This means that AI can make decisions and act based on the data and situations it encounters.
Adaptability:
The ability of AI to improve performance by learning from experience. AI systems analyze past actions and results to adjust their future behavior, making them more efficient and accurate over time. These characteristics make AI a powerful technology that can transform the construction industry by automating tasks, optimizing processes, and learning from every interaction to improve performance continuously 1 . University of Helsinki & Reaktor. (n.d.). Introduction to AI. In Elements of AI. Retrieved August 8, 2024 from https://course.elementsofai.com/1/1
Spam filters
AI basically consists of complex codes. Something that is not at all new to our modern society.
Social media algorithms
Detailed route instructions
Image recognition
Language translation
Virtual assistants
Product recommendations
The History Of Generative Artificial Intelligence (GenAI)
Generative artificial intelligence (GenAI) has deep roots in mathematics and algorithms. It all began with the Persian mathematician Muhammad ibn Musa al-Khwarizmi in the 9th century, who founded algebra and algorithm theory 2 . Later in the 18th century, Thomas Bayes introduced Bayes’ learning theorem 3 , an important milestone in statistics and machine learning, followed by Leonhard Euler, who contributed mathematical notation and graph theory 4 , thereby forming the foundation of AI.
Advances in AI began to take off in the mid-20th century. Early versions of GenAI included Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) 5 . These statistical models were designed to generate new data set sequences based on manual input such as speed and time.
In 1947, Alan Turing introduced the idea of ’intelligent machinery’, and later the Turing Test in 1950, which assesses the machine’s ability to exhibit telephone systems that could learn to navigate a maze by itself.
In the 1980s and 1997 came Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, which improved the ability of AI to process sequential data. A major revolution in 2014 with Generative Adversarial Networks (GANs) significantly improved data generation. The 2017 Transformer model paved the way for major language models like GPT. In 2022, OpenAI launched ChatGPT, which is based on data from all over the Internet and thereby quickly gained popularity for its ability for natural conversations and text generation.
These advances show how GenAI has evolved from theoretical concepts to practical applications that transform many aspects of our lives and work processes. By understanding the historical background and purpose behind the development, we can better understand and place GenAI in today’s digital landscape.
Bayt Al Fann. (n.d.). Algorithms, Algebra & Astronomy: Muhammad Ibn Musa Al-Khwarizmi. Retrieved August 8, 2024 from https://www.baytalfann.com/post/ algorithms-algebra-astronomy-muhammad-ibn-musa-al-khwarizmi
Encyclopædia Britannica. (n.d.). Thomas Bayes. In Encyclopædia Britannica. Retrieved August 8, 2024 from https://www.britannica.com/biography/Thomas-Bayes
Wikipedia contributors. (2024, August 8). Leonhard Euler. In Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/wiki/Leonhard_Euler
WeAreBrain. (2023, May 24). The history of generative AI (GenAI). Retrieved August 8, 2024 from https://wearebrain.com/blog/the-history-of-generative-ai-genai/
About Artificial Intelligence
AI becomes accessible to developers and end users
1940s to 1960s
1947: Alan Turing introduced the idea of ”intelligent machinery” and developed the Turing test.
1950: Claude Shannon interconnected telephone circuits to build a machine that can learn.
1956: The field of artificial intelligence was born at the Dartmouth conference.
1961: Joseph Weizenbaum created ELIZA, one of the first chatbots to simulate a psychologist.
1980s to 2010s
1980: Recurrent Neural Networks (RNNs) were introduced.
1997: Long Short-Term Memory (LSTM) networks improved the processing of sequential data.
2014: Generative Adversarial Networks (GANs) are being developed.
2017: The Transformer model is introduced, leading to the development of GPT models.
2020s
2022: OpenAI launches ChatGPT, which reaches one million users in five days.
2022: BLOOM is being launched as a large, open-access, multilingual AI model.
2023: Google launches Gemini and Meta develops Llama, which demonstrates advanced low-power AI capabilities.
2023: DALL-E, Midjourney and Stable Diffusion are revolutionizing text-to-image generation.
Sources: Qualcomm 2024 6 Simonite 20187
Qualcomm. (2024, February). The rise of generative AI: Timeline of breakthrough innovations. Retrieved August 8, 2024 from https://www.qualcomm.com/news/onq/2024/02/the-rise-of-generative-ai-timeline-of-breakthrough-innovations
Simonite, T. (2018, December 19). Mighty mouse. MIT Technology Review. Retrieved August 8, 2024 from https://www.technologyreview.com/2018/12/19/138508/mighty-mouse/amp/
02.3 Why Talk About GenAI?
Generative AI (GenAI) is a type of artificial intelligence that mimics how the brain processes information. Our brains are made up of neurons that communicate and process information. Similarly, AI uses artificial neurons in neural networks that process data through multiple layers of calculations. When AI systems are trained, the connections between these neurons are adjusted, allowing them to learn and recognize patterns. This training process allows AI to perform tasks such as recognizing images or understanding speech.
GenAI specifically focuses on generating new data similar to the data it has been trained on.
This can be text, images, or other forms of data. For example, a GenAI model can be trained on a large number of images and then create new images that look like the original ones. Similarly, it can be trained on text data to generate coherent and meaningful text.
With the introduction of GenAI, AI technology has become more accessible to everyone and has the potential to improve many aspects of the construction industry. GenAI can help automate tasks that previously required much time and work, such as planning and designing buildings. It can also optimize the use of resources, which can lead to more sustainable construction projects. Although GenAI is not a magic solution that can solve all problems, it is a valuable tool that can make construction processes more efficient and innovative.
Therefore, it is GenAI that we choose to highligh here.
Input layer
Hidden layer 1
Hidden layer 2
Hidden layer 3
Output layer
Combinations of edges
Edges
Object models
02.4 Types Of AI
Machine Learning
Predictive Analytics
Using historical data to predict future events.
A branch of AI where computers learn from data and experience without being explicitly programmed for each task. ML algorithms identify patterns and make decisions based on data.
Deep Learning
An advanced form of machine learning that uses neural networks with many layers to analyze complex patterns in data.
A technology that creates new content such as text, images, and sound by analyzing and learning from large amounts of data.
Classification
Sorting data into categories based on their content.
Natural Language Processing
A branch of AI that focuses on enabling computers to understand, interpret, and generate human languagee.g., by seeing patterns in large amounts of text.
Extraction of specific information from large amounts of text data. Data Extraction
Translation
Translation of text from one language to another.
GenAI
Speech
Text To Speech
Technology that converts written text into spoken language.
Speech To Text
Technology that converts spoken language into written text.
A subcategory of AI dealing with technology that can recognize, understand, and generate human speech. It also includes the ability to convert between speech and text.
Vision
The field of AI that gives computers the ability to see, process, and understand the visual content of images or videos, just as humans do.
Machine Vision
Using cameras and AI to mimic the human ability to see and understand the visual environment.
Expert Systems
AI systems that use knowledge and rules to solve complex problems within a specific domain.
Image Recognition
Planning & Optimization
Using AI to plan and optimize resources and processes to achieve the best results.
The ability to identify objects, people, places, and actions in pictures.
Robotics
Integration of AI into robots to perform tasks autonomously or semiautonomously, often in physical environments.
02.5 AI Cheat Sheet
Many overviews and cheat sheets exist, including thousands of AI tools on the market.
If you are looking for a comprehensive industry-specific overview, you can look at one developed by Stjepan Mikulic, Founder & CEO of AI in Architecture, Engineering and Construction (AEC). The overview is a good place to start when you want to explore the many AI tools aimed at the construction industry.
On this page, you get a selection of AI tools that can generally support, guide, and assist with the work in the construction phases. In section 03.4, more specific AI tools are presented through cases from the construction industry.
Video
8
02.6
AI In Construction
AI has an easier time generating cat images than tools for the construction industry, as far more data is available about cats. The Internet is filled with billions of images of cats on which AI models can be trained. This large data set enables the AI to learn and recognize cats with high precision.
AI works best where there is plenty of data. Data is often scattered and inaccessible in the construction industry, which is a hindrance. Therefore, a large part of the effort to spread AI in construction is collecting and structuring data. This enables AI to perform precise analyses of inputs and deliver valuable outputs. Companies investing in data infrastructure and quality will be better equipped to exploit AI’s full potential.
The
construction industry is estimated to use only 4% of available data8 . Generated with MidJourney
For AI to be effective in the construction industry, it is necessary to collect, structure, and standardize data. This requires a significant effort from the companies, who must invest more in data infrastructure and improve data quality.
AN AI WORKS BEST WHEN THERE IS A LOT OF DATA TO TRAIN IT ON. ADDITIONALLY, THE TRAINING DATA MUST BE RELEVANT TO THE DESIRED OUTPUT.
IN THIS WAY, THE AI CAN PROVIDE OUTPUTS WHICH CAN PREDICT BASED ON DATA OR ”FILL IN” GAPS.
IF THE TRAINING DATA IS NOT RELEVANT TO THE DESIRED OUTPUT, YOU END UP WITH AN AI THAT HALLUCINATES AND GIVES INADEQUATE ANSWERS.
Generated with MidJourney
02.7 Construction Phases
AI can play a role in all phases of a construction project. Generally speaking, the implementation of AI is most obvious when automating repetitive processes. Here are a number of suggestions for how AI can be used in the construction phases:
SCOPE AND IDEA
Project Brief
AI can help generate and evaluate design proposals and simulate different scenarios to find the best solution. Identifying potential problems early in the process can save time and money.
PROJECT OUTLINE
Proposal Phase
AI can assist in developing outlines and project proposals by analyzing previous projects, optimizing resources, and suggesting improvements. AIbased tools can also generate visual models, visualizations, and presentations to help communicate proposals to stakeholders.
REGULATORY PROPOSAL AND TENDER PROJECT
Design Phase
AI can optimize the design process by identifying potential problems early and proposing detailed solutions for regulatory processing, tender preparation, and detailed design. Authority processing is optimized, e.g., by automatically ensuring compliance with building regulations. AI will also be able to analyze previous cases to predict challenges and review and highlight relevant document information.
Execution Phase
During execution, AI can be used to monitor construction processes, ensure quality control, and improve site safety. AI-based systems can identify errors and defects in real time, making it possible to correct them quickly and efficiently.
Operational Phase
After construction is completed, AI can help predict maintenance needs, optimize energy use and ensure that the building functions optimally over time. This can extend the life of the building and reduce operating costs.
Demolition
AI can also be used in the demolition phase by optimizing the demolition process, identifying materials for recycling and ensuring compliance with environmental standards. AI-based systems can help plan and execute the demolition in a safe and efficient manner.
03.0 AI In The Real World
Generated by Google Deepmind
03.1 Work Processes
Tiago Pereira from NORDFY, a Performance Design Agency specializing in integrating AI and design technology, highlights that AI can transform the construction industry by improving design processes and optimizing creative work methods. However, it is crucial to retain the human component as machines lack style, vision, and the ability to understand value propositions. Our personal preferences, based on our unique biases, should continue to play a central role in decision-making.
The human component
GenAI is a powerful tool but cannot replace human creativity and intuition. Machines have no personal taste, style, or ethos. Human preferences (and prejudices) make our work unique and valuable. AI should enhance our capabilities and support human decisionmaking and interaction to ensure a harmonious collaboration between technology and people.
A predictive reinforcement tool
GenAI can act as a powerful augmentation tool with predictive capabilities that enable better data-driven decision-making. By analyzing large data sets, AI helps identify patterns and trends that people might overlook. By simulating different design scenarios, AI can evaluate different outcomes, identify problems,
and suggest solutions before costly problems arise in a construction project. This foresight allows for better planning, more time and cost-effective projects, and the ability to make informed decisions based on solid data.
Front-loading models
Effective use of AI involves providing models with valuable data by setting rule-based parameters, styles, and other constraints early in the design process. It ensures that the generated designs meet specific requirements, reducing the need for repeated iterations and saving time and resources, especially in the initial design phases, where fees and risk are typically high.
Reverse-engineering the workflow
The number of expensive man-hours for modeling is reduced by evaluating AI-generated content before manual modeling. High-quality images can be quickly generated to meet specific conditions, enabling reverse-engineering in the design workflow. Once we have evaluated the first draft of AI-generated content and selected material for further development, the manual effort is reduced, as we can model with a targeted output and optimize and validate systems before the labor-intensive production begins. This approach identifies and resolves potential problems early, ensuring high-quality end
products and improved efficiency. Therefore, one of the most significant advantages of AI in the early design phases is the possibility to reduce time and costs by spending fewer expensive hours creating new ideas or products.
Efficiency gains and competitive advantages
It requires investment to front-load models, collect data, and prevalidate designs, but the benefits are also great. It provides efficiency gains and will make companies competitive in a developing industry.
The future of the AEC industry
The future of AI in construction is full of potential despite the industry’s complexity and occasional lack of coordination. By fully harnessing the power of GenAI, we can change our work processes across the entire development spectrum and create more efficient and sustainable buildings without compromising quality and design preferences. It will improve our ability to deliver projects on time and on budget and enable innovative solutions that meet the changing needs of society.
The future of the AEC industry is bright and full of opportunities. But a strong push from the industry’s leaders is essential to remain competitive in the global landscape and maintain the quality of ”Danish design” as a valuable export factor.
Tiago Peraira, NORDFY
Ethics and Law
Several ethical considerations are linked to AI due to AI’s elements of ”autonomy” and ”adaptability.” Nicolaus Falk-Scheibel, lawyer at Poul Smith Kammeradvokaten, dives into these considerations about ethics and which legal regulations affect AI in Denmark.
The focal point for the ethical considerations about AI is humans and AI’s impact on society. The purpose is thus to ensure that humans are equipped to live in a world with AI.
One of the basic ethical principles is that AI is not developed to replace humans but to complement and collaborate with humans. Similarly, it should always be ensured that humans have the last word concerning AI, e.g., when it comes to decisions.
Another important element in connection with the ethical considerations is the principle of transparency. Humans must thus be able to understand and explain the function of an AI and its decisions, i.e., the underlying algorithm.
In addition, several ethical considerations are linked to norms, morality, and bias, with a focus on reducing problematic bias and ensuring that an AI’s actions live up to society’s values.
The above-mentioned ethical principles constitute a principle for reducing the potential societal risks associated with the development and use of AI.
Legal regulation
The legal framework for AI remains limited. This applies, in particular, to the regulation and jurisprudence regarding the purely private law aspects. On a European level, the AI Regulation has established a regulation that primarily aims to ensure the development and use of AI in accordance with European principles. The purpose of the regulation is, in particular, to ensure a sound approach to AI, risk management, and fair competition in the field.
A central element of the regulation is a risk-based approach to AI, i.e., a division of AI into different risk profiles:
• Unacceptable Risk Systems that use sensitive data to categorize people or that are used for surveillance.
• High Risk Systems used to evaluate creditworthiness, health etc. or assessment of job applicants.
• Limited Risk Systems used to improve human-made activities or that record decision patterns or deviations from them.
• Minimal Risk Systems that come up with personalized recommendations for music, advertising, spam filters, etc.)
While AI with an unacceptable risk is generally prohibited in the EU, requirements are also placed on it - depending on an AI’s risk profile.
For AI with a high-risk profile, demands are made for, among other things, prior risk assessments and measures to minimize risks, quality requirements for data in connection with
promting, human supervision/quality assurance, as well as documentation for the system and its purpose.
For AI with a limited risk profile, only a number of requirements for transparency apply, including requirement to disclose that this is AIgenerated content.
The duty subjects, i.e. those who must ensure that an AI meets the requirements of the AI regulation are developers, distributors, dealers and users, i.e., the entire value chain.
Pursuant to the regulation, a European AI office will be established, as well as national supervisory authorities that enforce noncompliance with the AI regulation, which in the extreme could result in fines.
In addition to the AI regulation itself, one should also be aware that the development and use of AI can also have other legal consequences, e.g., in relation to copyright, GDPR or similar.
Threats and Risk
Nothing comes without consequences, and for AI, several risks are worth considering.
First of all, some security risks in connection with data use require several measures, as Nicolaus Falk-Scheibel explained in the previous section. AI is also associated with concerns about incomprehensible complexity, fear of unidirectionality that can outcompete creativity, and technological skepticism regarding how AI will affect society and the labor market in the future. Another negative consequence is that AI can make a person or a deal seem more skilled than they really are, which can complicate, for example, employment situations.
While some job functions are becoming redundant with AI, new opportunities are emerging for those who can utilize the technology effectively. To preserve competitiveness in the labor market, it becomes crucial for employees to adapt and develop their skills in relation to the new technological possibilities. 03.3.a
The biggest risk is the large amount of information that AIs are fed, making it difficult to distinguish between correct and incorrect. This applies both on a world political level and within construction. The constant flow of data and information can lead to misunderstandings and misinformation, which can have severe consequences for the success and safety of projects.
Photo: Flávio Santos
Generated by Google Deepmind
Opportunity and Potential
The biggest benefit of AI is that the technology gives people access to skills, especially for small and medium-sized enterprises (SMEs), which they would not otherwise be able to afford and have access to in the company. Initially, this may be in areas such as text, graphics, and design, but in the future, it will include more specializations.
By automating repetitive tasks, AI frees up time for more creative and strategic tasks, increasing productivity and reducing errors. AI can also improve collaboration by offering better data analytics and predictions, leading to more informed decision-making.
• Reduces manual labor and saves time on routine tasks.
• Delivers early insight, accelerates decisionmaking processes, and assesses consequences quickly.
• Optimizes the use of employees’ skills and competencies.
• Adapts effectively to complex and dynamic work environments.
• Challenges and transforms traditional business models within the industry.
With artificial intelligence, we can make computers act and react like humans. This technology is particularly suitable for construction, where key elements include dialogue, interaction, and knowledge sharing. With generative AIs, we can automate and optimize processes, making it possible to eliminate many routine tasks. This allows the industry to focus on the activities that create value and utilize our skills optimally.
Niels W. Falk, HD Lab
03.4 Intro to Cases
In the following pages, some cases from the construction industry will be presented that illustrate applications of AI in practice. These examples will show how AI can transform various aspects of construction, from design and planning to operation and maintenance.
Overall, AI cases show how technology can transform the construction industry by improving quality, safety, and efficiency throughout the project lifecycle, although there are also challenges to overcome.
In the case examples, you can see how:
• AI can generate and optimize design suggestions
• AI can support and automate the drafting and review of contracts
• AI can identify potential risks and suggest proactive solutions
• AI can support the preparation of competitive offers
• AI can help communicate project plans and designs to stakeholders
• AI can automate administrative tasks
• AI can support facility management and optimize operation and maintenance
The table shows an overview of the cases that are presented on the following pages with a visualization of which types of AI they include, as well as which of the construction phases the case deals with.
Construction Planning
Visualizations
Design, Planning and BIM
Facility Management Planning
Inspection and Safety
AI for Risk Management
Information Management
Offers and Contracts
Administration and Office Tasks
Internal Experiments
Construction Planning
Alice Technologies is revolutionizing construction planning using advanced optimization algorithms to generate schedules that balance cost, construction time, and climate impacts.
Project managers are helped to make informed decisions by evaluating different scenarios, such as minimizing construction time for faster project completion or reducing CO 2 emissions for environmentally friendly construction.
Buildots uses image recognition combined with BIM models to monitor and document progress on the construction site. By comparing realworld images of the construction site with 3D models, the project manager can quickly identify deviations from planned timelines and suggest corrective actions, ensuring projects run more smoothly and efficiently.
GenAI Image Recognition
Photo: Chris Adam
03.4.b
Visualizations
Visual representation is essential in construction projects, and AI tools such as Stable Diffusion (Stability.ai), Midjourney, and DALL·E make it possible to create detailed and realistic visualizations from textual descriptions. These tools help architects and designers quickly generate design concepts, facilitating communication with stakeholders and clients.
Runway is an AI video editing/generation platform that adds to presentation capabilities by creating dynamic videos that give a quick and engaging impression of construction projects. With tools like these, projects can be visualized from different angles and at different design stages, helping to make decisions early in the project and present projects in a compelling way.
GenAI
Images generated with Stability AI and developed by Arkitema
03.4.c
Design, Planning and BIM
Innovative solutions such as Autodesk Forma, Testfit.io, Skema.ai, and Finch 3D use AI to automate design and project processes. These tools can generate everything from early volume models and floor plans to detailed BIM models, reducing the time and resources required to develop and adjust design proposals. AI’s ability to analyze large amounts of data and propose optimized design solutions means that architects and engineers can create more space-efficient and sustainable buildings.
Generative design tools that previously required programming skills are now integrated into commercial software solutions, making them accessible to a wider audience and fundamentally changing how we collaborate and build.
GenAI
03.4.d
Facility Management Planning
Property.ai and Upsite use AI to plan and optimize the maintenance of buildings. By analyzing data from sensors and past maintenance history, these tools can predict maintenance needs and suggest proactive actions that extend the life of buildings and reduce operating costs. AI-based solutions for facility management can identify potential problems before they develop into major
maintenance tasks, helping to maintain buildings’ functionality and value. In addition, AI enables the optimization of energy consumption by analyzing and adjusting operational parameters in real-time, further reducing costs and improving the sustainability of the building.
Inspection and Safety
AI technology for image recognition has many applications in the construction industry. COWI’s virtual inspection system uses AI to analyze images of structures and identify potential damage, making inspections faster and more accurate. This technology can also be used to monitor construction site compliance with safety regulations by automatically recording safety equipment and procedures in use.
With Lobe.ai, users can train their image recognition models without having in-depth technical knowledge. This enables construction companies to adapt AI solutions to specific needs and projects, increasing the efficiency and accuracy of inspections and safety monitoring.
AI For Risk Management
current data, these systems can provide insight into where projects may deviate from the plan, enabling project managers to take proactive steps to mitigate risk and keep projects on track. 03.4.f
Project platforms such as Oracle Construction Intelligence Cloud and Autodesk Construction Cloud leverage access to project information to predict risks, schedules, budgets, and project materials.
These platforms integrate data from various sources and use AI to identify potential problems before they occur. By analyzing past projects and
03.4.g Information Management
Effective information management is essential in complex construction projects, and specific GPT solutions such as ConTech Labs ByggeGPT, bauGPT, and Struct make the total knowledge of construction available to employees. These tools can answer questions, find relevant documents, and organize information to make it easy for users to find the information they need.
Trunk tools ensure that project information is easily accessible and up-to-date, while the research project BIMGPT enables easy access to and manipulation of BIM models. By using AI for information management, construction companies can improve their efficiency and decision-making by ensuring that employees always have access to up-to-date and relevant data.
Offers and Contracts
Companies are experimenting with creating their own GPT models to understand and analyze contracts, prepare tender texts, and respond to offers. Tools such as Togal and Sparkel use image recognition to create quantity summaries from drawings and models, making the bidding process more efficient and accurate.
Codefy analyzes contracts for risks, giving users an in-depth understanding of potential challenges and supporting the drafting of good contracts. Although Codefy currently only works in German and English, its functionality demonstrates the potential of AI in legal and contractual applications in the construction industry.
03.4.i
Administration and Office Tasks
AI tools such as Microsoft’s Copilot and general GPT models are used to automate various administrative tasks. These tools can help you write emails, draft texts, create PowerPoint presentations, and analyze data in spreadsheets. By automating routine tasks, AI frees up time
for employees to focus on more complex and strategic tasks, improving office productivity and efficiency. AI can also help organize and prioritize tasks, making it easier for teams to collaborate and keep track of project progress.
Internal Exsperiments
A sandbox is a protected environment where you can test different AI models without the risk of compromising data or software that goes into production. COWI and Arkitema have been visible with their Playground, where employees can experiment with AI solutions without fear that they will affect production systems. Many
large construction companies offer similar sandboxes that allow employees to explore and develop AI tools in a safe and controlled environment. This promotes innovation and learning by allowing teams to test new ideas and technologies without risk.
Cook Book
As a manager, you can promote AI in your organization by finding employees with an interest and talent for AI and allowing them to test new tools and share their knowledge. It is important to ensure that your team has the necessary skills and is curious about new uses of AI. Furthermore, it is crucial that you draw up guidelines for the use of AI in the organization and protect data carefully.
Identify business areas where AI can make a difference and start experimenting. When AI is to be trained, tested, and improved, it is important that this design process is iterative and that the design improvement requires new training and testing. This ensures a better result and encourages experiments that do not have to be perfect in the first go.
Human insight
AIs need large amounts of data to train on, so collecting and processing this data carefully is important. The larger the dataset, the better and broader tasks the AI can handle.
Collection of training data
Definition of the AI’s task
Find your first idea - the best AI use case
Plan and build your first AI
Define an AI strategy
Build and deploy multilple AIs
Using human insight and experience can help improve the AI’s accuracy and decision-making abilities.
A clear definition of which ”tasks” the AI must perform and the questions it must answer is crucial in making the AI as effective as possible.
4. Selection of models, algorithms and technology
The choice of models, algorithms and technology in AI is decisive for how effective the solution is, and depends on the purpose, performance, scalability, complexity, integration needs, price and resources for the specific solution.
6. Training the AI
5. Data proessing
Data must be processed, quality assured, and formatted to achieve a precise, efficient, and reliable AI.
Once the AI is fully developed and tested, it must be implemented for use with existing systems, processes and workflows.
9. Implementation of the AI
8. Improvement of the AI
7. Test and validation of the AI
Training AI requires large amounts of data and computing power, but is a very important step in the process, as this is where the AI is trained to recognize patterns, find connections and make predictions.
The AI can be improved by training with additional data or by adjustments to the algorithms, both as an iterative step in the training, testing and improvement process or over time while it is in use.
Testing the AI and validating it is a crucial step to ensure that it is accurate, reliable and capable of solving the task it was developed for. It must be ensured that uncertainties, errors and reaction patterns in edge cases are detected and handled correctly.
What Is Needed To Build An AI
Data forms the foundation of any AI solution, and the process of collecting, preparing, and processing data is critical to ensure a successful implementation. AI models, especially machine learning models, require large datasets to learn effective patterns and correlations. For example, an image recognition algorithm will need thousands, if not millions, of images for training. Depending on the AI’s application area, the data types vary considerably, from text, images, and audio to video and sensor data. This data can be collected from many different sources, such as databases (public and private), APIs, sensors, or even through web scraping from the Internet. In some cases, the data is generated directly by the users.
The quality of the data is as important as the quantity. The data must be consistent and free of inconsistencies, which requires a thorough data cleansing process. Removing noise, outliers, and missing values is necessary to ensure the data is as clean as possible. Furthermore, many AI applications, especially in supervised learning, require annotated data. For example, this may be necessary for image recognition, where the images must be labeled accurately to help the model learn patterns.
After the data is collected and cleaned, it must be processed in a way that makes it suitable for use in the AI model. This includes normalization, standardization and any transformations of the data. Processing of data may also involve handling of categorical variables and textual data processing, such as tokenization and removal of stop words. An important part of this process is feature engineering, where relevant properties are derived from raw data, which can significantly improve model performance. Finally, the dataset must be split into training, validation and test datasets. This partitioning is essential to be able to evaluate the performance of the model on data it has not seen before, thus ensuring that the model generalizes well to new and unseen data.
In other words, a comprehensive approach to data management is necessary to ensure that the AI model can be trained efficiently and accurately, which is a prerequisite for achieving successful results in any AI project.
Generated by Google Deepmind
Generated by Google Deepmind
The cost of building an AI ranges widely and includes several elements. The most expensive part is talent and expertise, as salaries for experts in AI are often high. Data collection and preparation, which involves collecting, cleaning, and preparing the data, is a small but significant part of the cost. Computational resources are also a significant expense, as powerful hardware and cloud services are needed to run the AI models efficiently.
Operating costs, which cover the ongoing costs of keeping the system running, as well as costs for regulation and compliance with
legislation, constitute a smaller but necessary part of the overall budget. Miscellaneous expenses and investments in specific software and tools necessary for the development and implementation of AI also contribute to the overall cost.
Developing an AI solution requires a significant initial investment in human and technological resources. The various components, from data collection and quality assurance to computational resources and expertise, are fundamental to creating an efficient and successful AI. Distribution
04.0 Strategy and Roundup
Generated by Google Deepmind
Implementing AI in the construction industry requires a well-defined strategy ensuring the technology is used optimally. First, the company must determine specific goals and KPIs for AI so that efforts can be targeted effectively. It is important to analyze the current situation to understand which tasks and processes AI can best optimize. Areas where AI can create value must be identified, and it must be decided how broad the strategy should be and whether it should focus on specific departments or the entire organization.
Before developing a full strategy, starting with experiential pilot projects makes sense. Pilot projects allow testing AI solutions on a smaller scale, gathering experience, and understanding how AI can best be integrated into the company.
Responsible use of AI requires the development of ethics checklists and policies for data protection and risk management. Employees must be trained and acquire necessary AI skills, and clear roles and structures in the AI team must be defined to ensure effective implementation. At the same time, it is crucial to create a culture that supports AI use and builds trust in the technology across the organization.
The company should prepare an action plan with specific efforts and projects and monitor the results continuously to ensure that the AI implementation proceeds according to the plan. Developing new business models and establishing collaborations that support the AI strategy are also important. Finally, the company must choose the right AI technologies and applications, such as GenAI or Predictive AI. By following this strategy, companies throughout the construction industry can effectively implement AI and achieve improved efficiency, quality, and competitiveness in their projects.
Strategy and Roundup
Vision, value, risk and adoption
Construction Planning
Where do we stand today?
Where will we work with AI?
Where to invest and harvest?
How broad should the AI strategy cover?
Secure AI-ethics
Data and privacy
Performance Competences
Risks
Roles and AI-team
Use Cases
What do we want to achieve with AI? AI Governance AI Competence AI Roadmap, Implementation
Action plan, efforts, projects, experiments
Monitoring, results, performance and progress
New business models and collaborations
Culture and trust in AI
Outsourcing og partnership
and Parameters
What type of AI will we work with?
What technologies will we use?
How will we use AI?
A Market For AI In Construction
The financial benefits of using generative AI in the construction industry are great, and several reports point to concrete applications and the resulting financial benefits. According to the report ”Denmark’s GenAI Paradox: From Lagging to Leading,” generative AI supports innovation and efficiency in the construction industry. Companies that are early adopters of AI technologies have a competitive advantage by being able to deliver projects faster, cheaper, and of higher quality.
71% of companies see a lack of talent as a bottleneck, which underlines the need for strategic competence development9
AI’s popularity is perhaps due to the large amounts of general AI models that can be adapted to the needs of individual companies. This makes AI more accessible to companies without major AI skills.
As many as 86% of companies in the Danish construction industry make use of general models10
The report ”The Economic Potential of Generative AI: The Next Productivity Frontier” emphasizes that generative AI can generate significant economic values across several industries, including construction. Specific applications such as automating planning and optimizing construction processes using AI can lead to significant cost savings and increased productivity.
According to the report, the use of AI technologies can potentially increase productivity in the construction industry by up to 20% and reduce costs by 15-20%11
Boston Consulting Group. (n.d.). Denmark’s GenAI paradox: From lagging to leading. Retrieved August 8, 2024 from https://web-assets.bcg.com/86/ c8/7aadfc1344cc82b599c169942349/denmarks-genai-paradox-from-lagging-to-leading.pdf Molio. (n.d.). SMV-analyse. Retrieved August 8, 2024, from https://molio.dk/nyheder-og-viden/netvaerk/contech-lab/aktiviteter/smv-indsatser/smv-analyse McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. Retrieved August 8, 2024, from https://www.mckinsey. com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
The market potential for AI in the Danish construction industry is large, as AI can streamline work processes, improve decisionmaking, and increase competitiveness.
Companies that adopt AI early have an advantage in delivering projects faster, cheaper, and with higher quality. AI also has the potential to increase compliance security in relation to the extensive and often unmanageable requirements for construction. The technology can help ensure that all rules and standards are complied by AI, e.g., it can help the user find all the regulations that cover a particular task, making it easier to comply with all necessary regulations.
However, implementing AI requires investment in technology and upskilling employees, especially because lacking talent is seen as a bottleneck. Effective use of AI also requires access to large amounts of data, which the construction industry is not yet fully using.
Some of the biggest obstacles to realizing AI’s innovation and efficiency potential are that many of the construction’s underlying processes are not digital and/or that digital systems do not talk to each other or allow easy transfer of data. It can be between companies, but also between companies and public entities. Access to data, including training, can also be an obstacle to optimizing the use of AI. In addition, complex and scattered legislation and regulation, as well as technical requirements that are either not up-to-date or cannot be digitized, can pose a structural barrier to the utilization of AI.
Overall, AI can lead to significant financial gains, but it requires strategic planning and investment.
Perspectives On AI
Danish companies in the construction industry have different approaches and perspectives on integrating AI, which positions them differently in the market. Some companies see AI as an opportunity to gain a competitive advantage by pioneering technology adoption. With a proactive approach to AI, early adopters can differentiate themselves in the market and offer their customers advanced and effective solutions.
Other companies are more cautious and choose to observe the market and learn from the experiences of the early adopters. These companies are gradually integrating AI into their existing processes, enabling them to minimize risks and costs. With a more conservative approach, they can ensure that they use proven technologies and avoid many pitfalls.
Companies that adapt their AI strategy to their unique needs and capabilities will be best positioned to exploit the technology’s full potential. Some approaches may better suit certain companies or authorities depending on their size, resources, and specific business objectives.
For example, larger companies with more resources may be better able to invest in advanced AI projects, while smaller companies may benefit from focusing on more basic AI applications to improve their efficiency and productivity.
Strengthens the individual employee
Overall, the different approaches and perspectives on AI in the Danish construction industry show that there is no ”one-size-fits-all” solution but many sensible ways to get started with AI.
Create sandboxes
Build specific tools and roll them out?
Plans and strategies
Wait and see what happens
Joint Efforts On The Increased Use Of AI In Construction
Creating your own generative AI models from scratch is demanding in terms of time, money, and CO2. For example, the estimated electricity consumption to train GPT-4 corresponds to monthly electricity consumption in Danish cities such as Kolding or Esbjerg. At the same time, it requires enormous amounts of relevant data, and it is estimated that GPT-4 is trained on many terabytes of compressed text.
Denmark is a small country where the construction industry consists of both authorities and many small and medium-sized companies, where even the large companies are not very large compared to global players. It can therefore be advantageous to make a joint effort to create the best possible basis for AI in construction. Based on the spring workshops, we have identified the following opportunities for collaboration.
Joint competence development –application and strategy:
Artificial intelligence requires new skills both on the part of the users and in the strategic layer – the companies in the construction industry express that a lack of skills is the biggest barrier to common AI development.
Common data for training AI:
Developing AI solutions requires lots of data, and there are only a few players who have enough
data to train good solutions. At the same time, technology developers are always looking for new data sources. A shared data repository that makes data available under well-defined licenses can boost the development of useful solutions. In this context, the public sector plays a central role, as it can access large amounts of data in construction cases. The big question is, therefore, whether these are released and how.
General technical common property:
This describes all the knowledge needed to build. This knowledge is spread across several different suppliers, and AI can play a central role in making this knowledge more accessible to the entire industry. The challenge is that different business models underlie how this knowledge is shared and used. Furthermore, regulation within construction is complex, and it is therefore important to consider how the system can be designed so that AI can be more easily used to ensure compliance.
Exchange and structure of data:
GenAI can eat all kinds of data, but the more structured the data, the easier it can be used to train AIs. The EU’s dataspaces are an initiative to ensure the safe, transparent, and sound data exchange within certain subject areas. There is currently no data space for construction - but it could be an interesting perspective.
Would your company be interested in participating in joint AI initiatives within the industry?
Yes No
Undecided
What are the biggest barriers to joint AI development in the construction industry?
What Should I Take With Me From This Scrapbook?
AI has the potential to transform the construction industry, and in all likelihood, the increase in the use of AI, from 5% in 2023 to 21% in 202410, will not stop there. Still, the use will continue, and AI in the industry companies will hit much higher proportions in the near future.
It is important to understand opportunities, implement the technology correctly, and collaborate widely to exploit this potential. Start with small projects, learn from experience, and be open to innovation.
By following the practical advice and strategies in this scrapbook, you can take the first steps towards using AI in your work and create significant value for your organization and the industry as a whole.
The development of AI is going fast, so it is crucial to stay up-to-date. Search for podcasts, scientific articles, analyses, and books to gain new knowledge and inspiration continuously. This scrapbook is not an exhaustive coverage of AI in construction – nor a method manual. But it can hopefully make you curious about the many possibilities and emphasize that new tools are constantly being developed, and the next big breakthrough can happen at any time.
We are happy to hear your feedback and discuss ideas for innovative projects involving AI in the construction industry. So don’t hesitate to contact us at info@contechlab.dk.
Molio. (n.d.). SMV-analyse. Retrieved August 8, 2024, from https://molio.dk/nyheder-og-viden/netvaerk/contech-lab/aktiviteter/smv-indsatser/smv-analyse 10
... to the people and companies who have contributed to the development and content of this scrapbook. To the participants in the workshops and, not least, the project’s collaborators.
Niels W. Falk HD Lab
Tiago Pereira NORDFY
Nicolaus Falk-Scheibel Poul Schmidt Kammeradvokaten
Ditte Emilie Lysholdt & Henning Steensig The Danish Authority of Social Services and Housing
Søren Cajus DI Byggeri
Torben Klitgaard BLOXHUB
Aske Strandberg AEC Hackathon
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