How can the civil construction industry benefit from AI?

The international company, Deloitte, estimates that the world construction market will reach approximately $10.5 trillion and will be growing by 4.2% annually from 2018 to 2023. What makes the field move forth is greater dwellings and infrastructure which are the outcomes of urbanisation and an increase in the population.

One noticeable impact on this sector: the increasing integration of AI.

As AI continues to play an important role in the construction sector, its significance is set to grow further. 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 artificial intelligence (AI).

AI in construction has the potential to help players realise value throughout project life cycles, 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 the toughest challenges faced, including safety of labourers, shortages in manpower, as well as cost and schedule overruns.

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

What is artificial intelligence and machine learning in construction?

Artificial intelligence (AI) is a term that is associated with a machine that can perform and act like humans in areas such as problem-solving, pattern-recognition as well as learning. Machine learning is an artificial intelligence branch that utilises statistical methods to provide systems with the capability to develop “learn” from data, without a programmer having to write the code for it.

The construction sector, in turn, where the algorithms ‘interact’ and processing becomes more complex than usual. For example, an AI model can be trained to monitor and analyse the progress of a grading plan to detect schedule risks at an early stage.

It may ‘toss questions’ like the volume of cut and fill, machine uptime or downtime, weather patterns, and projects in the past before it issues a risk score and decides if the project manager needs to be notified.

AI and machine learning for smart construction

The potential applications of machine learning and AI in construction are wide. Requests for information, open issues, and change orders are standard in the industry. Machine learning is like a person that can take on a great amount of data.

The AI machine 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.

construction worker uses controller for robotic arm on jobsite

10 benefits of AI in construction

1. Prevent cost overruns: most big projects tend to go over budget despite employing the best project teams. AIs can be used on such 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.
2. Helps with design of buildings: building information modelling 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.There is software that uses machine learning algorithms to explore all the variations of a solution and generates design alternatives.
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. There are AI and machine learning solutions today that general contractors use to monitor and prioritise 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.
4. Project planning: using robots helps to autonomously capture 3D scans of construction sites and then feed 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 aids in project planning since it optimises the best path and corrects itself over time.
5. More productive job sites: there are some self-driving construction machinery to perform repetitive tasks more efficiently than 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.
6. Safe construction: the risk of injuries for construction workers on the job are five times more often to occur than any other job. The leading causes of private sector deaths (excluding highway collisions) in the construction industry were falls, followed by being struck by an object, electrocution, and caught-in/between. By using an AI machine, one is able to create an algorithm that analyses 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.
7. Labour shortages: a 2017 McKinsey report says that construction firms could boost productivity by as much as 50% through real-time analysis of data. Construction companies are starting to use AI and machine learning to better plan for distribution of labour 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 labour could be deployed.
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 detailed work like plumbing, HVAC and electrical systems when the structure is fitted together.
9. AI and big data in construction: every job site becomes a potential data source for AI. Data generated from images captured from mobile devices, drone videos, security sensors, building information modelling (BIM), and others have become a pool of information. This presents an opportunity for construction industry professionals and customers to analyse 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 behaviour for optimal security and safety.

The future of AI in construction

Robotics and AI can reduce building costs by up to 20%. 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 deploying the route of the electrical and plumbing systems used in the present-day buildings.

AI is a technology that companies are using to manufacture the safety project for sites of work. Through the use of AI, the real-time cooperation between workers, equipment, and materials is being monitored and hence is becoming the first virtual assistant to forewarn superiors of possible dangers, construction mistakes, and to a lesser degree production problems.

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 prioritise investment based on areas where AI can have the most impact on the company’s unique needs.

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