The construction industry is undergoing an artificial intelligence (AI)-driven digital revolution, with AI-powered solutions being used for waste and resource optimization, activity monitoring, and risk management. AI is optimizing design processes and automating tasks for improved efficiency and reduced project costs, paving the way for an intelligent and data-driven future in the construction sector. This article deliberates on the benefits, risks, and applications of AI in the construction industry.
Image credit: PopTika/Shutterstock
Growing Role of AI in Construction
The growth of the construction industry has been substantially impacted by complex challenges like labor shortages, health and safety, and cost and time overruns, which resulted in extremely low productivity levels compared to other industries. Tackling these existing problems has been difficult as the sector remains one of the least digitized industries. This lack of digitization makes project management more complex and tedious, leading to cost inefficiencies, uninformed decision-making, and poor quality performance.
In recent years, AI and its subfields like natural language processing (NLP), automated planning and scheduling, optimization, computer vision, and machine learning (ML) have been applied to support decision-making and tackle complex problems to improve overall efficiency and productivity. Additionally, AI has been employed to tackle construction-specific challenges, with specific examples including risk prediction, improvements in supply chain and logistics processes, cost estimation, and health and safety monitoring, all utilizing machine learning.
Similarly, robotics has been applied in performance evaluation, offsite assembly, site monitoring, and the management of construction equipment and materials, while knowledge-based systems were used for sustainability assessments, risk and waste management, conflict resolution, and tender evaluation.
Benefits of Using AI
AI reduces human error, assists in repetitive and dangerous jobs, ensures faster and more accurate decisions, prevents cost overruns, increases design options, offers 24/7 support to any construction project, performs predictive analysis, monitors construction progression, increases on-site safety, and allows for scheduled maintenance.
Consumption prediction, process automation and optimization, more reliable as-built documentation for operation, using telemetry to respond to an issue, reduced construction and design time, decrease in cost and usage of construction materials, integration and connection to external services, lesser generation of waste/carbon and use of energy, knowledge capture and knowledge sharing, and increased profit and productivity are the additional benefits of AI in construction.
The additional benefits primarily deal with enhanced compliance between deliverables and requirements, generative design, greater collaboration among various parties, enhanced creativity during design, addressing skills shortages, higher customer expectations, and improved project execution.
Moreover, AI also reduces re-work and fraud, increases bid winning, improves staff wellbeing by quickly completing specific tasks, makes resource reallocation/prediction based on weather patterns, provides schedule certainty, automates interoperability requirements, allows for predictive maintenance, and decreases exposure to hazardous activities.
Applications of AI
The amount of construction and demolition waste (C&DW) generated by the construction sector is increasing every year due to the rapid development of the industry, which is adversely affecting human health and the environment. Effective waste management is achievable by optimizing design for offsite construction, waste-efficient procurement, deconstruction, materials selection, and reuse and recovery using advanced AI techniques with building information modeling (BIM).
AI is also used in BIM-based three-dimensional (3D) models for construction waste quantification, data analytics for waste collection and management, BIM for waste minimization design, and BIM-based construction waste minimization framework. Construction across various domains heavily relies on AI-powered estimation and prediction models. These prediction models are critical for the early prediction of construction duration and cost, which are key to project success.
Unreliable project time and cost estimates could lead to substantial financial and economic implications. The integration of AI techniques like deep learning (DL) with BIM can ensure higher accuracy in cost and time prediction/estimation. DL also makes better predictions of other important factors like carbon efficiency, waste, and energy.
Construction sites are rapidly transforming into smart working environments due to the growing proliferation of Internet of Things (IoT) sensors and other digital technologies. A large quantity of videos, images, and other data forms like reports is generated during real-time site and equipment monitoring, most of which is unstructured. BIM can be used to aggregate this data, which can then be analyzed using AI techniques for optimizing site performance in cost and scheduling, quality, safety, planning, and design. For instance, the digitization of the construction site layout planning process in BIM can be realized using rule-based model checking. Similarly, a construction site AI chatbot can provide real-time updates regarding site activities to project managers and other stakeholders.
The navigation of BIM interfaces improves by combining BIM applications with various AI techniques like NLP. Specifically, the integration of voice interaction with BIM imbues a more natural and realistic experience. AI and BIM have also been combined with other Industry 4.0 tools like IoT, augmented reality (AR), quantum computing, and blockchain for intelligent building energy monitoring, safety warnings on construction sites, and prefabricated construction based on an IoT-enabled BIM platform.
Radio-frequency identification (RFID) combined with blockchain for materials logistics, defect management using AR, BIM, and ontology-based data collection, blockchain-based manufacturing supply chains in the composite materials industry, mobile-based virtual reality (VR) and AR for health and safety education, and integrated IoT, blockchain, and BIM for managing building lifecycle data are other examples of AI and BIM with Industry 4.0 tools in the construction industry.
Health and safety analytics (HSA) leverages advanced data analytics techniques to prevent and predict occupational accidents in workplaces. The construction industry witnesses a substantially higher rate of occupational deaths and injuries compared to other industries as construction work is highly risky owing to on-site dangers like falling objects/tools/equipment, getting trapped while working, dust and toxic materials, working from heights, and noisy working environments.
These risks lead to hearing loss, disability, long-term health issues, or even death for construction workers. Implementing proactive approaches based on digital technologies like AI is necessary to envision health risks or accidents before their occurrence and prevent them. BIM-based fall hazard identification and prevention, wearable technology for construction safety monitoring, and sensor-based technology integration with BIM to improve construction safety are recent AI use cases in health and safety.
Risks of Using AI
Privacy violation, job loss, algorithmic biases due to inaccurate data, output reliability, physical safety and security, inequality, financial performance, and data ownership and licensing are the primary risks of using AI in construction. High implementation costs, data corruption, lack of consistency in updating the AI tools, unexpected data usage, and availability of reliable data as input to AI solutions are the additional risks of AI.
Moreover, overreliance on AI can encourage humans to make decisions without interrogating/analyzing results. Specifically, it leads to loss of control over automation, alienation of applied human knowledge, and lack of emotions in decision-making. This results in risks regarding changing mindset and culture, intellectual property risks, and issues related to liability and responsibility for the inaccuracies/errors by AI-powered solutions.
Another major risk is increased cost when the upper management fails to properly use the AI tools within the correct departments and workflow. For instance, establishing new departments to implement new tools will create a bloated company structure, leading to greater inter-department rivalry and mistrust and reduced profits. Low data literacy and inexperience of senior staff responsible for providing sufficient governance and oversight, and shortage of AI experts are other key risks of AI in construction.
Additionally, unfair competition due to the initial overselling of AI benefits in an immature market is disadvantageous for small and medium enterprises (SMEs) that lack resources or competence for implementing AI, which results in the monopolization of the sector. Ethical risks include the risks of nefarious companies in construction leveraging, appropriating, and programming AI solutions for malicious purposes.
Overall, AI is transforming construction, addressing challenges like labor shortages and safety concerns. It improves efficiency through design optimization, automation, and real-time monitoring while reducing costs and waste. However, challenges like data privacy and talent gaps must be addressed for successful adoption.
References and Further Reading
Bolpagni, M., Bartoletti, I. (2021). Artificial Intelligence in the Construction Industry: Adoption, Benefits and Risks. https://www.researchgate.net/publication/355192584_Artificial_Intelligence_in_the_Construction_Industry_Adoption_Benefits_and_Risks
Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Davila Delgado, J. M., Bilal, M., Akinade, O. O., Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities, and future challenges. Journal of Building Engineering, 44, 103299. https://doi.org/10.1016/j.jobe.2021.103299
Regona, M., Yigitcanlar, T., Xia, B., Li, R. Y. M. (2022). Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 45. https://doi.org/10.3390/joitmc8010045