The use of Artificial Intelligence (AI) in the industry to improve product manufacturing began in the 1970s with the introduction of Computer-Assisted Design (CAD) and Computer Numerical Control (CNC), which aided in designing and refining products for better accuracy and optimisation. In the 1990s, AI expanded to capturing worker experiences and gathering data. Today, AI plays a crucial role in enhancing quality control, optimisation, robotics, predictive maintenance, and workplace health and safety.
The construction industry has historically been slower than other sectors in adopting modern methods of working. As a result, construction defects often lead to higher costs, management challenges, delays in project delivery, and issues related to quality, waste, and health and safety.
Recently, there have been strong calls for the construction industry to swiftly embrace digitisation to address these issues. Evidence suggests that implementing AI applications can significantly enhance the construction process. Beyond the potential economic boost of approximately two precent (US $1.6tn) to the global economy, digitisation supports global goals related to quality, sustainability, and well-being. 
For instance, there is a strong push to adopt 3D Concrete Printing (3DCP) in building construction. The UAE has set a goal for 25% of its buildings to be 3D printed by 2030. This initiative highlights the advantages of 3DCP, including greater flexibility in architectural design, reduced material waste, and shorter project timelines. Additionally, 3DCP promotes a safer work environment and appeals to younger generations. It has also been shown to be more cost-effective, time-efficient, and environmentally friendly in reducing CO2 emissions compared to traditional construction methods.
Machine Learning (ML), a subset of AI, is also making significant contributions to the construction industry. It processes large datasets derived from real-case historical experiences to forecast potential issues such as project timelines and resource allocation. ML improves safety by predicting hazards before they arise and enhances quality control by detecting defects using sensors, thereby minimising the need for manual inspections.
Furthermore, building Information Modelling (BIM) is increasingly recognised as a powerful AI tool, providing a 3D virtual representation of a facility before physical construction begins. It allows subcontractors to contribute their input to identify and avoid potential faults, thereby reducing waste generation during construction. Additionally, BIM helps prevent project delays, potentially saving up to 20% of costs. Furthermore, BIM has become essential in Quantity Surveying (QS) for its ability to deliver fast and accurate cost estimates.
Digital twins are a significant application of AI in construction, representing a virtual replica of a physical asset. They enable real-time interaction with the physical property throughout the entire construction lifecycle. Digital twins enhance communication and coordination among project stakeholders, document project progress, and help in reducing costs and time. They also play a crucial role in verifying quality and ensuring worker safety. Given that the construction industry has the highest rate of worker injuries, AI offers promising solutions to address this issue. For instance, integrating computer-connected technologies on-site—such as drones, CCTV cameras, sensors, IoT-enabled wearables, two-way radios, and instant language translation tools can help identify unsafe conditions or behaviours that could lead to accidents.
While AI offers significant advantages to the construction sector, it is, as any new technique, challenging to take advantage of. Firstly, the traditional nature of the construction industry presents a barrier. AI is advancing rapidly, and there is a need to build trust among contractors by demonstrating the success of AI-implemented projects and illustrating its benefits.
Additionally, there are concerns about AI potentially displacing jobs. Due to automation, it is projected that 14% of global workers may need to transition to new professions by 2030. To address these concerns, it is crucial to focus on the social impact of AI and foster public trust in its transformation. Moreover, government strategies should allocate more resources to training and developing AI experts in construction to meet future demands.
Another significant concern is AI security against hackers and cybercrime, which incurs additional costs and demands continuous surveillance. In the construction industry, even minor AI errors can have substantial impacts on quality, costs, and planning. The most critical risk is that such mistakes can result in accidents and loss of life. Therefore, further research is urgently needed to prevent high-level attacks.
One more challenge is the high initial cost of AI solutions in construction. For instance, using 3DCP instead of traditional methods requires additional expenses in terms of construction materials, machinery, and maintenance, which can be unaffordable for small firms. However, as technology becomes more widespread, its costs are expected to decrease.
Another question is the ethics and privacy concerns of integrating AI in the workplace. For example, automated monitoring can negatively impact employees, leading to stress, anxiety, and a loss of trust in supervisors. Therefore, new policies and regulations must be implemented to protect sensitive personal information.
Despite the initial costs, implementing AI in construction is crucial for achieving global goals related to quality, sustainability, and well-being. However, extensive research is still needed to mitigate its drawbacks.
