Globally, individuals and businesses spend over $10 trillion per year on construction-related activities -- and that's projected to keep growing by 4.2% until 2023. Part of this enormous amount of spending is on, and enabled by, rapidly-moving technological advancements that touch all areas of the ecosystem. In the 2020 report, The Next Normal in Construction: How Disruption is Shaping the World's Largest Ecosystem, McKinsey identified a growing focus on solutions that incorporate artifical intelligence (AI).

AI in construction has the potential to help players realize value throughout project lifecylces, 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 and schedule overruns.

As market barriers to entry steadily lower, and advancements in AI, machine learning (ML), and analytics accelerate, you can expect AI (and allocation of resources funneled towards AI) to play a more significant role in construction in the coming years. 

Keep reading to understand how AI is used in construction and the 10 main benefits of using AI in construction.

 

What is Artificial Intelligence and Machine Learning in Construction?

Artificial intelligence (AI) is an aggregative term for describing when a machine mimics human cognitive functions, like 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 Trimble machine learning engineer Bob Banfield put it when we asked him about deep learning in construction, 

 

 

As applied in construction, the 'questions' and algorithms get significantly more complex. For instance, a machine learning program may track and evaluate progress in a grading plan to identify schedule risks early. The algorithms might 'ask quetions' about cut and fill volume measurements, machine uptime and downtime, weather patterns, previous projects, or any number of inputs to generate a risk score and determine if notifications need to be made.

 

AI and Machine Learning for Smart 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 industry. Machine learning is like a smart assistant that can scrutinize this mountain of data. It then alerts project managers about the critical things that need their attention. Several applications already use AI in this way. Its benefits range from mundane filtering of spam emails to advanced safety monitoring.

 

 

10 Examples of AI in Construction

1. Prevent cost overruns

Most mega projects  go over budget despite employing the best project teams. Artificial Neural Networks are used on projects to predict cost overruns based on factors such as project size, contract type and the competence level of project managers. Historical data such as planned start and end dates are used by predictive models to envision realistic timelines for future projects. AI helps staff remotely access real-life training material which helps them enhance their skills and knowledge quickly. This reduces the time taken to onboard new resources onto projects. As a result, project delivery is expedited.

 

2. AI for better design of buildings through generative design

Building Information Modeling is a 3D model-based process that gives architecture, engineering and construction professionals insights to efficiently plan, design, construct and manage buildings and infrastructure. In order to plan and design the construction of a  project, the 3D models need to take into consideration the architecture, engineering, mechanical, electrical, and plumbing (MEP) plans and the sequence of activities of the respective teams. The challenge is to ensure that the different models from the sub-teams do not clash with each other.

The industry uses machine learning in the form of AI-powered generative design to identify and mitigate clashes between the different models generated by the different teams to prevent rework. There is software that uses machine learning algorithms to explore all the variations of a solution and generates design alternatives. Once a user sets up requirements in the model, the generative design software creates 3D models optimized for the constraints, learning from 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 risk. The larger the project, the more risk, as there are multiple sub-contractors 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 the project team can focus their limited time and resources on the biggest risk factors. AI is used to automatically assign priority to issues. Subcontractors are rated based on a risk score so construction managers can work closely with high-risk teams to mitigate risk.

 

4. Project planning

One construction intelligence company launched in 2017 with the promise that its robots and artificial intelligence hold the key to solving late and over budget construction projects. The company uses robots to autonomously capture 3D scans of construction sites and then feeds that data into a deep neural network that classifies how far along different sub-projects are. If things seem off track, the management team can step in to deal with small problems before they become major issues. 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 assess endless combinations and alternatives based on similar projects. It aids in project planning since it optimizes the best path and corrects itself over time.

 

5. AI makes jobsites more productive

There are companies that are starting to offer self-driving construction machinery to perform repetitive tasks more efficiently than their human counterparts, such as pouring concrete, bricklaying, welding, and demolition. Excavation and prep work is being performed by autonomous or semi-autonomous bulldozers, which can prepare a job site with the help of a human programmer to exact specifications. This frees up human workers for the construction work itself and reduces the overall time required to complete the project. Project managers can also track job site work in real time. They use facial recognition, onsite cameras, and similar technologies to assess worker productivity and conformance to procedures.

 

6. AI for construction safety

Construction workers are killed on the job five times more often than other laborers. According to OSHA, the leading causes of private sector deaths (excluding highway collisions) in the construction industry were falls, followed by struck by an object, electrocution, and caught-in/between. A Boston-based construction technology company create an algorithm that analyzes photos from its job sites, scans them for safety hazards such as workers not wearing protective equipment and correlates the images with its accident records. The company says it can potentially compute risk ratings for projects so safety briefings can be held when an elevated threat is detected. It even began ranking and releasing safety scores for each U.S. state based on COVID-19 compliance in 2020.

 

7. AI will address labor shortages

Labor shortages and a desire to boost the industry’s low productivity are compelling construction firms to invest in AI and data science. A 2017 McKinsey report says that construction firms could boost productivity by as much as 50 percent through real-time analysis of data.  Construction companies are starting to use AI and machine learning to better plan for distribution of labor and machinery across jobs.

A robot constantly evaluating job progress and the location of workers and equipment enables project managers to tell instantly which job sites have enough workers and equipment to complete the project on schedule, and which might be falling behind where additional labor could be deployed.

An AI-powered robot such as Spot the Dog can autonomously scan a jobsite 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 off-site factories staffed by autonomous robots that piece together components of a building, which are then pieced together by human workers on-site. Structures like walls can be completed assembly-line style by autonomous machinery more efficiently than their human counterparts, leaving human workers to finish the detail work like plumbing, HVAC and electrical systems when the structure is fitted together.

 

9. AI and big data in construction

At a time when a massive amount of data is being created every day, AI systems are exposed to an endless amount of data to learn from and improve every day.  Every job site becomes a potential data source for AI. Data generated from images captured from mobile devices, drone videos, security sensors, building information modeling (BIM), and others have become a pool of information. This presents an opportunity for construction industry professionals and customers to analyze and benefit from the insights generated from the data with the help of AI and machine learning systems.

 

10. AI for post-construction

Building 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 about the operation and performance of a building, bridge, roads, and almost anything in the built environment. This means AI can be used to monitor developing problems, determine when preventative maintenance needs to be made, or even direct human behavior for optimal security and safety.

 

 

The Future of AI in Construction

Robotics, AI, and the Internet of Things can reduce building costs by up to 20 percent. Engineers can don virtual reality goggles 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 worksites. AI is being used to track the real-time interactions of workers, machinery, and objects on the site and alert supervisors of potential safety issues, construction errors, and productivity issues.

Despite the predictions of massive job losses, AI is unlikely to replace the human workforce. Instead, it will alter business models in the construction industry, reduce expensive errors, reduce worksite injuries, and make building operations more efficient.

Leaders at construction companies should prioritize investment based on areas where AI can have the most impact on their company’s unique needs. Early movers will set the direction of the industry and benefit in the short and long term.

 

 

About the Author

Sumana Rao is the Global Product Marketing Leader for Buildings Content. She has served manufacturers and distributors in the Industrial and MEP space for over 15 years. Sumana is responsible for Global Product Marketing for Buildings Content & Analytics.