Trimble is a global technology company that connects the physical and digital worlds, transforming the ways work gets done. With relentless innovation in precise positioning, modeling and data analytics, Trimble enables essential industries including construction, geospatial and transportation. Whether it's helping customers build and maintain infrastructure, design and construct buildings, optimize global supply chains or map the world, Trimble is at the forefront, driving productivity and progress.
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Globally, individuals and companies spend more than US$10 trillion a year on construction-related activities -- and this is expected to continue growing by 4.2% until 2023. Part of this enormous amount of spending is made possible by rapidly evolving technological advances that touch all areas of the ecosystem. In its 2020 report, The Next Normal in Construction: How Disruption is Shaping the World's Largest Ecosystem,
AI in construction has the potential to help stakeholders realize value throughout the life of the project, including: design, bidding and financing; procurement and construction; operations and asset management; and, business model transformation. AI in construction helps the industry as a whole overcome some of our toughest challenges, including safety concerns, labor shortages and cost overruns and schedule delays.
As the barriers to entry steadily lower, and advances in AI, machine learning (ML) and analytics accelerate, you can expect AI (and the allocation of resources channeled towards AI) to play a more significant role in construction in the coming years.
Read on to understand how AI is used in construction and the top 10 benefits of using AI in construction.
What is Artificial Intelligence and Machine Learning in Construction?
Artificial intelligence (AI) is an aggregate term to describe when a machine imitates human cognitive functions, such as problem solving, pattern recognition and learning. Machine learning is a subset of AI. Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" from data, without being explicitly programmed. A machine becomes better at understanding and providing insights as it is exposed to more data.
As the machine learning engineer at Trimble, Bob Banfield, said when we asked him about deep learning in construction:
"Machine learning includes many algorithms. Here's a quick example: if you were looking to find out whether or not you're liable to get some kind of disease, one type of learning algorithm might work through a tree of questions like, "how old are you?" Then, "ok, do you exercise?" And so on. If you say yes, you go down one branch, and if you say no, then you go to another. It's a perfectly valid machine learning algorithm. It's like the game 20 Questions that you might have played as a child, except in machine learning those questions are generated automatically."
When applied to construction, the "questions" and algorithms become significantly more complex. For example, a machine learning program can track and evaluate progress on a grading plan to identify schedule risks early. The algorithms can "ask questions" about cut and fill volume measures, machine uptime and downtime, weather patterns, previous projects or any number of inputs to generate a risk score and determine whether notifications need to be made.
AI and Machine Learning for Intelligent Construction
The potential applications of machine learning and AI in construction are vast. Requests for information, open issues and change orders are standard in the sector. Machine learning is like an intelligent assistant that can sift through this mountain of data. It then alerts project managers to the critical things that need their attention. Several applications already use AI in this way. Its benefits range from mundane spam email filtering to advanced security monitoring.
10 examples of AI in construction
1. Avoiding cost overruns
Most megaprojects go over budget, despite employing the best project teams. Artificial Neural Networks are used in projects to predict cost overruns based on factors such as the size of the project, the type of contract and the level of competence of the project managers. Historical data, such as planned start and end dates, is used by predictive models to envision realistic timelines for future projects. AI helps staff access real-life training material remotely, which helps them improve their skills and knowledge quickly. This reduces the learning curve for new project participants. As a result, project delivery is accelerated.
2. Artificial Intelligence for better building design through generative design
Building Information Modeling is a process based on 3D models that provides architecture, engineering and construction professionals with insights to plan, design, construct and manage buildings and infrastructure efficiently. To plan and design the construction of a project, 3D models need to take into account the architectural, structural, installation, electrical, plumbing, fire prevention and air conditioning (MEP) plans and the sequence of activities of the respective teams. The challenge is to ensure that the different models from the different disciplines do not interfere with each other.
The industry uses machine learning in the form of AI-powered generative design to identify and mitigate interferences between the different models generated by different teams to avoid rework. There is software that uses machine learning algorithms to explore all the variations of a solution and generates design alternatives. Once a user establishes requirements in the model, the generative design software creates 3D models optimized for the constraints, learning with each iteration until it comes up with the ideal model.
3. Risk mitigation
Every construction project has some risk that comes in many forms, such as quality, safety, time and cost. The bigger the project, the greater the risk, as there are multiple subcontractors working on different trades in parallel on job sites. There are AI and machine learning solutions today that general contractors use to monitor and prioritize risk on the job site, so that the project team can focus its limited time and resources on the biggest risk factors. AI is used to automatically assign priority to problems. Subcontractors are ranked based on a risk score so that construction managers can work closely with teams to mitigate risk.
4. Project planning
A construction intelligence company was set up in 2017 with the promise that its robots and artificial intelligence hold the key to solving overdue and over-budget construction projects. The company uses robots to autonomously capture 3D scans of construction sites and then feeds this data into a deep neural network that classifies how far along different sub-projects are. If things look off track, the management team can intervene to deal with small issues before they become big problems. Algorithms of the future will use an AI technique known as "reinforcement learning". This technique allows algorithms to learn based on trial and error. It can evaluate infinite combinations and alternatives based on similar projects. It helps with project planning, as it optimizes the best path and corrects itself over time.
5. Artificial Intelligence makes construction sites more productive
Companies are starting to offer autonomous construction machines to carry out repetitive tasks more efficiently than their human counterparts, such as concreting, bricklaying, welding and demolition. Excavation and preparation work is being carried out by autonomous or semi-autonomous excavators, which can prepare a work site with the help of a human programmer to exact specifications. This frees up human workers for the work itself and reduces the overall time needed to complete the project. Project managers can also monitor work on the construction site in real time. They use facial recognition, on-site cameras and similar technologies to assess worker productivity and compliance with procedures.
6.AI for construction safety
Construction workers are killed on the job five times more often than other workers. According to OSHA, the leading causes of death in the private sector (excluding road collisions) in the construction industry were falls, followed by an object, electrocution and workers being pinned. A Boston-based construction technology company creates an algorithm that analyzes photos of its workplaces, checks them for safety risks, such as workers not wearing protective equipment, and correlates the images with its accident records. The company says it can potentially calculate risk ratings for projects so that safety dialogues can be held when a high threat is detected. It even started ranking and releasing safety scores for each US state based on COVID-19 compliance in 2020.
7. Artificial Intelligence will solve the labor shortage
Labor shortages and the desire to increase the sector's low productivity are forcing construction companies to invest in AI and data science. A 2017 McKinsey report says that construction companies can increase productivity by up to 50% through real-time data analysis. Construction companies are starting to use AI and machine learning to better plan the distribution of labor and machines between construction sites.
A robot constantly evaluating work progress and the location of workers and equipment allows project managers to instantly tell which work sites have enough workers and equipment to complete the project on time, and which may be lagging behind where additional work could be deployed.
An AI-powered robot like Spot the Dog can autonomously scan a construction site every night to monitor progress - making it possible for a large contractor like Mortenson to get more work done in remote areas where skilled labor is in short supply.
8. Off-site construction
Construction companies are increasingly relying on factories equipped with autonomous robots that assemble components of a building, which are then transported and assembled by human workers on site. Structures such as walls can be completed assembly-line style by autonomous machines more efficiently than their human counterparts, leaving human workers to finish the detail work such as plumbing, air-conditioning and electrical systems when the structure is installed together.
9. Artificial Intelligence and Big Data in construction
At a time when a huge amount of data is being created every day, AI systems are exposed to an infinite amount of data to learn from and improve every day. Every work site becomes a potential data source for AI. Data generated from images captured from mobile devices, drone videos, security sensors, building information modeling (BIM), among others have become a pool of information. This represents an opportunity for professionals and clients in the construction industry to analyze and benefit from the insights generated from the data with the help of AI and machine learning systems.
10. Artificial Intelligence for post-construction
Managers can use AI long after construction is complete. By collecting information about a structure through sensors, drones and other wireless technologies, advanced analytics and AI-powered algorithms gain valuable insights into the operation and performance of a building, bridge, roads and almost anything in the built environment. This means that AI can be used to monitor development problems, determine when preventive maintenance needs to be done, or even direct human behavior towards optimal safety standards.
The Future of AI in Construction
Robotics, AI and the Internet of Things can reduce construction costs by up to 20%. Engineers can wear virtual reality glasses and send mini-robots into buildings under construction. These robots use cameras to track the work as it progresses. AI is being used to plan the routing of electrical and plumbing systems in modern buildings. Companies are using AI to develop safety systems for construction sites. AI is being used to track the real-time interactions of workers, machines and objects on site and alert supervisors to potential safety issues, construction errors and productivity problems.
Despite predictions of massive job losses, AI is unlikely to replace the human workforce. Instead, it will change business models in the construction industry, reduce costly errors, reduce workplace injuries and make construction operations more efficient.
Construction company leaders should prioritize investment based on areas where AI can have the most impact on their company's unique needs. The pioneers will define the direction of the industry and benefit in the short and long term.
About the author
Sumana Rao is a global leader in product marketing for building content. She has been working with manufacturers and distributors in the Industrial and MEP space for over 15 years. Sumana is responsible for Global Building Content Product Marketing & Analytics.