With Artificial Intelligence (AI) making strides in adjacent industries such as transportation and manufacturing, the engineering and construction (E&C) sector faces significant challenges from the evolving technological landscape. How is AI in construction and engineering transforming an industry long dominated by traditional capital project players?
Technology, especially AI, is now breaking down barriers between industries and operating more as ecosystems. Solutions, tools, and algorithms specific to industries like manufacturing and transportation are becoming more effective across various sectors. AI in construction, although in its early stages, is already revolutionising the industry with its implementation.
The E&C sector is worth more than $10 trillion a year. Despite having increasingly sophisticated customers, it remains severely underdigitised. In the immediate future, the proliferation of AI in the E&C sector is expected to be modest. Despite a high return on investment (ROI) and widespread management interest in AI solutions, few E&C firms or owners currently possess the necessary capabilities, including personnel, processes, and tools, to implement them. However, a shift is imminent. Stakeholders across the project lifecycle—contractors, operators, owners, and service providers—can no longer afford to view AI as a technology relevant only to other industries. Adjacent sectors like transportation and manufacturing are already breaking down barriers and operating more as ecosystems. This increases the threat of competition from market entrants not traditionally seen as capital project players.
These lowered market barriers are compounded by AI methods’ increasing ability to work across industries. These advances will be seen in the mid- to long-term. To play a role in future ecosystems and compete with incoming market entrants, the E&C sector needs to catch up in its adoption of AI applications and techniques. This effort will likely lead to more resources being allocated to building the necessary capabilities and AI playing a more significant role in construction in the coming years.
Where should E&C leaders begin? Here is how to realise AI in construction: examining where AI solutions are emerging in construction today, exploring AI-powered applications and use cases that have made an impact in other sectors, and assessing additional machine learning algorithms and their potential E&C applications.
The current state of AI in construction and engineering
AI use cases in construction are still in early stages though a small number of start-ups are gaining traction and attention in the market for their AI-focused strategies. Early-stage examples construction firms can evaluate include:
- Project schedule optimisers that consider millions of alternatives for project delivery and continuously enhance overall project planning.
- Image recognition and classification that assess video data collected on worksites to identify unsafe worker behaviour and aggregate this data to inform future training and education priorities.
- Enhanced analytics platforms that collect and analyse data from sensors to understand signals and patterns, deploying real-time solutions to cut costs, prioritise preventative maintenance, and prevent unplanned downtime.
However, adoption of AI solutions remains low in E&C, particularly compared with other industries. In a McKinsey research study, the construction and building materials industries were compared to 12 other businesses; ten of those industries have adopted AI more advanced than the building and construction sector, and all 12 are projected to spend more on AI over the next three years.
Of course, any AI algorithm learns from the past, requiring a critical mass of data to deliver on its promise. Firms will need significant amounts of data (projects) to train an AI algorithm, benefiting larger companies more, particularly in the short term. It’s possible that an external third party might leverage E&C data to train its models, improving the industry as a whole but limiting individual firms’ competitive advantage. However, this is unlikely given the significant restrictions on data sharing and ownership.
AI in Construction: Applications from other industries
AI encompasses a vast universe of possibilities and use cases, including machine learning, natural language processing, and robotics. Here are some AI applications that are used in other industries but have direct applications in the construction sector:
- Transportation Route Optimisation Algorithms for Project Planning Optimisation: Current technology offers transportation companies the ability to optimise routes and improve traffic navigation. For instance, UPS uses ORION (On-Road Integrated Optimisation and Navigation) to optimise delivery routes, saving millions of miles and gallons of fuel each year. In the future, reinforcement learning—a technique allowing algorithms to learn through trial and error—could provide even more effective optimisation and solve objective functions like duration or cost of fuel. This technology is directly applicable to E&C project planning and scheduling, potentially assessing endless combinations and alternatives based on similar projects and optimising the best path over time.
- Pharmaceutical Outcomes Prediction for Constructability Issues: The pharmaceutical industry invests heavily in predictive AI solutions to lower R&D costs by forecasting medical trial outcomes. IBM Watson for Drug Discovery helps pharmaceutical companies predict the outcomes of drug trials, reducing the time and cost of R&D. These applications can forecast project risks, constructability, and structural stability, providing insights during the decision-making phase and potentially saving millions. They can also enable material testing, reducing downtime during inspections.
- Retail Supply Chain Optimisation for Materials and Inventory Management: AI has transformed the retail supply chain by reducing downtime, oversupply, and shipment predictability, leading to significant cost reductions and logistical efficiency. For example, Amazon uses AI for inventory management and demand forecasting, reducing storage costs and improving delivery times. Supervised learning applications will become crucial in E&C as modularisation and prefabrication grow, necessitating enhanced supply chain coordination to control costs and cash flows.
- Robotics for Modular or Prefabrication Construction and 3-D Printing: While modularisation and 3-D printing are advancing in construction, longer-term opportunities exist to maximise these approaches through machine learning. In the automotive industry, companies like Tesla use robotics and machine learning for precision manufacturing and assembly. Robotics industry researchers have successfully trained robotic arms through simulations, a technique potentially applicable to prefabrication and maintenance operations in E&C and other industrial sectors.
- Healthcare Image Recognition for Risk and Safety Management: Machine learning in healthcare is creating breakthroughs in image recognition for diagnosing illnesses. For instance, Google‘s DeepMind uses AI to analyse medical images and detect diseases like diabetic retinopathy. This technology could be applied to drone imagery and 3-D models in construction to assess quality control issues, such as structural defects, and detect critical events like bridge failure early on. These techniques could help compare developing and final products against initial designs and train algorithms to identify safety risks based on millions of drone-collected images.
Machine Learning and AI in construction
- Refining Quality Control and Claims Management: Deep-learning techniques can significantly enhance quality control by assessing drone-collected images to compare construction defects against existing drawings. For instance, Skanska, a leading construction and development company, uses drones equipped with AI to monitor construction sites. The AI analyses the collected images, compares them with the project plans, and identifies any discrepancies or defects. This helps in maintaining high standards of quality and ensures that any deviations are corrected promptly. Neural networks can also predict the likelihood of claims, enabling companies to develop proactive mitigation plans. For example, a system could analyse historical data on claims and identify patterns that suggest potential future claims, allowing firms to allocate contingencies and deploy targeted mitigation strategies effectively.
- Increasing Talent Retention and Development: Machine learning algorithms can be utilised to segment employees based on their likelihood of attrition and develop targeted retention plans. For example, IBM’s AI platform Watson Analytics helps HR departments to predict employee turnover and identify at-risk employees. This allows companies to create personalised retention strategies and training programmes to keep their talent engaged. AI can also forecast labour shortages by analysing market trends and workforce data. Construction firms can use these insights to plan hiring initiatives in advance, ensuring that they have the necessary workforce to meet project demands, thus limiting costs and project delays.
- Boosting Project Monitoring and Risk Management: Neural networks, utilising drone-generated images and laser data, can teach AI to create 3-D twin models that match BIM-generated models. This approach has been adopted by companies like Boston Dynamics, which uses its Spot robot equipped with 3D scanning capabilities to capture detailed images of construction sites. The data is then processed to create accurate digital twins, allowing for real-time project monitoring. This reduces decision-making cycles from monthly to daily through full automation of project scheduling and budgeting. This technology helps in identifying and mitigating risks promptly, thereby enhancing project efficiency and safety.
- Constant Design Optimisation: Recommender systems can use cluster behaviour to identify important data for making informed recommendations. Autodesk’s generative design tool, for example, uses AI to explore thousands of design options based on specific parameters like cost, material efficiency, and structural integrity. This allows architects and engineers to optimise their designs, resulting in more efficient and effective structures. Such systems provide owners and contractors with detailed insights into the best design choices based on various criteria, helping them to make better-informed decisions and avoid costly mistakes.
Future of AI in Construction
The future of AI in construction looks promising, with the market poised to reach $407 billion by 2027. AI-driven robotics and geospatial analysis will enhance resource allocation and environmental impact assessments. Companies implementing AI today will gain a competitive edge in the evolving market landscape.
With a driving passion to create a relatable content, Pallavi progressed from writing as a freelancer to full-time professional. Science, innovation, technology, economics are very few (but not limiting) fields she zealous about. Reading, writing, and teaching are the other activities she loves to get involved beyond content writing for intelligenthq.com, citiesabc.com, and openbusinesscouncil.org
