Advantages of Artificial Intelligence in Construction

Worldwide, individuals and businesses spend more than $10 trillion a year on construction-related activity, and that spending is projected to keep growing by 4.2% through 2023. In its 2020 report "The Next Normal in Construction: How Disruption Is Reshaping the World's Largest Ecosystem," McKinsey notes growing attention to solutions based on artificial intelligence.

AI in construction can help players capture value across the entire project lifecycle, including design, bidding and financing, procurement and construction, operations and asset management, and business model transformation. AI helps the industry as a whole tackle some of its most difficult problems, in particular safety, labor shortages, and cost and schedule overruns.

As barriers to market entry steadily fall and advances in AI, machine learning and analytics accelerate, AI can be expected to play a larger role in construction in the coming years.

The potential applications of machine learning and artificial intelligence in construction are huge. Requests for information, open questions and change orders are standard in the industry. Machine learning is like a smart assistant that can carefully study this mountain of data. It then alerts project managers to important matters that need their attention. Some applications already use AI this way. Its uses range from routine spam filtering to advanced safety monitoring. Here are 10 examples of AI in construction. 

Preventing cost overruns

The cost of most megaprojects exceeds the budget, even though they are staffed by the best design teams. Artificial neural networks are used in projects to predict cost overruns based on factors such as project size, contract type and the competence level of managers. 

Historical data, such as planned start and end dates, is used by predictive models to set realistic timelines for future projects. Artificial intelligence helps employees remotely access real training materials, which lets them quickly build their skills and knowledge. This reduces the time needed to bring new resources onto projects. As a result, project delivery speeds up.

Generative building design

Building information modeling is a process based on 3D models that gives architecture, engineering and construction professionals the information they need to efficiently plan, design, build and manage buildings and infrastructure. 

To plan and design a project's construction, 3D models must account for architectural, structural, mechanical, electrical and plumbing plans and the sequence of work by the relevant groups. The challenge is to ensure that the different models from subteams do not conflict with one another.

Risk reduction

Every construction project carries risk, which takes many forms, such as risk related to quality, safety, time and cost. The larger the project, the higher the risk, because several subcontractors in different trades work simultaneously on construction sites. 

Today there are AI and machine learning solutions that general contractors use to track and prioritize risks on the construction site, so the project team can focus its limited time and resources on the biggest risk factors. AI is used to automatically prioritize tasks.

Project planning

One construction intelligence company launched in 2017 with the promise that its robots and artificial intelligence would be the key to solving delayed and over-budget construction projects. The company uses robots to autonomously 3D-scan construction sites, then feeds that data into a deep neural network that classifies the distance between different subprojects. 

If something looks wrong, the management team can step in to resolve small problems before they grow into serious ones. Future algorithms will use an AI technique known as "reinforcement learning." This method lets algorithms learn through trial and error. It can evaluate an endless number of combinations and alternatives based on similar projects. This helps with project planning, because it optimizes the best path and corrects itself over time.

Creating more productive workplaces

Some companies are beginning to offer autonomous construction equipment to perform repetitive tasks more efficiently than human labor, such as pouring concrete, laying bricks, welding and demolition. Earthmoving and site preparation work is carried out by autonomous or semi-autonomous bulldozers that can prepare a work site under a human programmer's direction according to precise specifications. 

This frees workers for the construction work itself and cuts the total time needed to complete a project. Project managers can also monitor work on the construction site in real time. They use facial recognition, local cameras and similar technologies to assess labor productivity and compliance with procedures.

Construction safety

Construction workers die on the job five times more often than other workers. According to OSHA, the main causes of deaths in the construction industry in the private sector (excluding road traffic accidents) are falls, followed by being struck by an object, electrocution and caught-in or between incidents. 

A Boston construction technology company is building an algorithm that analyzes photos from job sites, scans them for safety threats such as workers without protective equipment, and matches the images against its accident records. The company says it could potentially calculate risk scores for projects so that safety briefings can be held when elevated danger is detected.

Addressing labor shortages

A 2017 McKinsey report says construction companies could raise productivity by 50% using real-time data analysis. Construction firms are beginning to use artificial intelligence and machine learning to better plan the allocation of labor and equipment across job sites.

A robot that constantly assesses the progress of work and the location of workers and equipment allows project managers to instantly determine which job sites have enough workers and equipment to keep the project on schedule, and which have places where additional labor could be deployed.

Off-site construction

Construction companies increasingly rely on off-site factories staffed by autonomous robots that assemble building components, which are then put together by people on the construction site. Structures such as walls can be assembled assembly-line style by autonomous machines more efficiently than human workers, leaving human workers to finish detailed work such as plumbing and electrical systems once the structure is put together.

Big data in construction

At a time when enormous amounts of data are created every day, AI systems get an endless supply of data to learn from and improve on every day. Every job site becomes a potential data source for AI. 

Data from images taken with mobile devices, drone footage, safety sensors, building information modeling and other sources has become a pool of information. This lets construction professionals and clients analyze data-driven insights with AI and machine learning systems and benefit from them.

Post-construction

Building managers can use AI long after construction is complete. By collecting information about the structure through sensors, drones and other wireless technologies, advanced analytics and AI-based algorithms provide valuable insight into the performance and characteristics of a building, bridge, roads and nearly everything in the built environment. 

This means AI can be used to track emerging problems, determine when preventive maintenance is needed, or even manage human behavior to ensure optimal safety.