The construction industry lags behind other sectors in digitization, resulting in low productivity, cost inefficiencies, project delays, and safety concerns. This lack of digitization is exacerbated by a resistance to change and a shortage of digital expertise. The challenges are further compounded by existing labor shortages, the COVID-19 pandemic, and the need for sustainable infrastructure. Artificial intelligence (AI), a rapidly advancing digital technology, offers significant potential to revolutionize the construction industry, as it has in other sectors like manufacturing and telecommunications. AI subfields such as machine learning, natural language processing, robotics, computer vision, optimization, and automated planning and scheduling have proven successful in addressing complex problems and improving decision-making. This study addresses the gap in understanding AI applications, opportunities, and barriers to adoption in the construction industry by answering the following research questions: 1) What are the areas of AI application in the construction industry? 2) What are the future opportunities for AI application in the construction industry? 3) What are the challenges to the adoption of AI in the construction industry? The study aims to critically investigate AI applications in construction to understand trends, opportunities, and barriers to adoption.

The authors conducted a literature review spanning six decades (1960–2020) using databases such as Scopus, IEEE, ACM, and ScienceDirect. The search focused on AI subfields (robotics, computer vision, machine learning, expert systems, knowledge-based systems, optimization, natural language processing) and their applications in the construction industry. Over 1800 publications were initially identified, with 1272 deemed relevant. The review revealed that while research on AI in construction has increased over time, with optimization and machine learning being particularly prominent in recent years, significant opportunities remain for broader AI adoption. Several studies showcased the successful application of AI in areas such as health and safety monitoring, cost estimation, supply chain optimization, and risk prediction. The review highlighted the existing gaps in AI adoption due to cultural barriers, high initial costs, trust issues, security concerns, talent shortages, computing power limitations, and inadequate internet connectivity.

This study employed a systematic literature review methodology. The researchers used Scopus as the primary database, supplemented by IEEE, ACM, and ScienceDirect, to identify relevant publications from 1960 to 2020. A total of 29 keywords related to AI subfields and the construction industry were used in the search. After screening over 1800 publications, 1272 were deemed relevant based on the inclusion criteria of describing or evaluating an AI subfield's practical application in the construction industry. Data extracted from each article included the application area in construction, the methodology/techniques used, and the findings. The review process involved examining abstracts, titles, and full-text articles to ensure the relevance of selected studies. The focus was on studies that used specific AI techniques to achieve defined goals, leading to a detailed analysis of various AI subfields' application trends in the construction industry.

The study revealed an increasing trend in AI research publications in the construction industry over the past six decades, with optimization initially being the most researched area and machine learning surpassing knowledge-based systems in the last decade. The emergence of advanced technologies like quantum computing, Internet of Things (IoT), cybersecurity, and blockchain is also impacting AI applications in construction. The analysis identified fourteen sub-domains where AI has shown significant application, including resource and waste optimization, value-driven services (estimation and scheduling, construction site analytics, job creation), supply chain management, health and safety analytics, AI-driven construction contract analytics, voice user interfaces, and AI-driven audit systems for construction financials. Specific findings include the potential of AI for minimizing construction waste using advanced data analytics and BIM; the use of AI in improving cost and time estimations by incorporating deep learning into BIM; the role of AI in transforming construction sites into smart work environments through site analytics; the potential for AI to create new jobs in construction; AI’s potential in improving supply chain management through better communication and trust; AI’s benefits in predicting and preventing health and safety accidents; the use of AI in managing construction contracts more effectively; and AI's potential to aid in financial auditing in construction. The study identified key advantages of AI in construction as increased cost and time savings, improved safety, better accuracy, and increased overall productivity. Limitations included incomplete data, high initial deployment costs, and data and knowledge acquisition challenges.

The findings of this study demonstrate the significant potential of AI to address the multifaceted challenges faced by the construction industry. The increasing availability of data from various digital technologies, combined with advancements in AI subfields, offers opportunities to enhance productivity, safety, and sustainability. The identified applications of AI across different sub-domains, including resource optimization, supply chain management, and safety analytics, highlight the transformative impact of AI in construction. The integration of AI with other industry 4.0 technologies such as BIM, IoT, and blockchain further enhances the potential for improved efficiency, transparency, and collaboration. However, addressing the challenges associated with AI adoption, such as high initial costs, talent shortage, security risks, and ethical concerns, is crucial for widespread implementation. The study's findings provide valuable insights for researchers, practitioners, and policymakers to better understand the current status, opportunities, and challenges of AI in the construction industry.

This study provides a comprehensive overview of AI applications, opportunities, and challenges in the construction industry. It highlights the growing trend of AI adoption, identifies key application areas, and underscores the importance of addressing the associated challenges. Future research should focus on developing robust AI solutions tailored to the specific needs of the construction industry, addressing ethical and security concerns, and promoting wider adoption by smaller firms. Further research into explainable AI, improved data quality, and targeted training programs to address talent shortages will also be crucial. The development of standardized datasets and evaluation metrics specific to the construction industry is also needed to facilitate the comparison and evaluation of different AI models and their applicability across various construction projects.

The study's scope is limited to a literature review, meaning it does not include primary data collection or empirical validation of AI applications. The reliance on existing literature might also introduce biases in the findings. The rapid pace of technological advancements in AI means that some findings may become outdated quickly. The focus on English-language publications may have excluded relevant research from other languages.