Acceptance and integration of Artificial intelligence and machine learning in the construction industry: Factors, current trends, and challenges

Abstract

The construction industry, historically hesitant in adopting new technologies, is undergoing significant transformation with the integration of artificial intelligence (AI). This research delves into the various elements influencing AI acceptance and implementation within this sector. The study applies well-established models and theories of technology acceptance, including the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Innovation Diffusion Theory (IDT), specifically adapted to the unique context of the construction industry. Critical factors driving AI acceptance encompass perceived usefulness, ease of use, organizational readiness, top management support, and external pressures. Furthermore, the research highlights essential elements such as workforce skills, data availability, and cybersecurity concerns that considerably affect AI adoption. Current trends reveal an increasing utilization of AI in project management, predictive maintenance, and design optimization, with a notable surge in the adoption of AI-powered Building Information Modeling (BIM) and robotics. Despite these advancements, the construction industry encounters significant challenges, including high implementation costs, resistance to change, and a lack of standardization. This research offers a comprehensive review of the current state of AI in the construction industry, providing insights into evolving trends and ongoing challenges. Keywords: Construction Industry, Artificial Intelligence, Project Management, Decision Support Systems, Decision Making, Machine Learning, Construction Projects. Citation: Rane, N. L., Desai, P., & Rane, J. (2024). Acceptance and integration of Artificial intelligence and machine learning in the construction industry: Factors, current trends, and challenges. In Trustworthy Artificial Intelligence in Industry and Society (pp. 134-155). Deep Science Publishing. https://doi.org/10.70593/978-81-981367-4-9_4 4.1 Introduction The construction industry, one of the oldest and most essential sectors worldwide, has continually adapted to technological progress (Irani & Kamal, 2014; Oprach et al., 2019; Whitlock-Glave et al., 2019). Recently, artificial intelligence (AI) has emerged as a transformative element, poised to significantly enhance efficiency, safety, and overall project outcomes in construction (Mohammadpour et al., 2019; Patil, 2019; Akinosho et al., 2020). The adoption and integration of AI in this industry, however, are shaped by various factors and face notable challenges. The acceptance of AI in the construction industry is driven by several pivotal factors. A major incentive is the potential for substantial cost savings and efficiency improvements (Mohammadpour et al., 2019; Patil, 2019). AI technologies, including machine learning algorithms and predictive analytics, can optimize resource allocation, reduce waste, and streamline project management processes, resulting in considerable cost reductions. This is particularly appealing to construction firms operating in a highly competitive market with narrow profit margins. Enhancing safety on construction sites is another critical factor. AI-powered systems can monitor site conditions in real-time, predict potential hazards, and alert workers to dangerous situations (Darko et al., 2020; Sacks et al., 2020; Abioye et al., 2021). For instance, AI can analyze data from wearable devices to detect signs of worker fatigue or stress, thus preventing accidents. This proactive approach not only protects workers but also minimizes project delays and financial losses due to accidents. The increasing complexity of construction projects further drives AI adoption. Modern construction often involves intricate designs and sophisticated engineering requirements (Mohamed & Mohamad, 2021; Heo et al., 2021; Bolpagni & Bartoletti, 2021). AI can assist in managing these complexities through advanced modeling and simulation capabilities. Integrating Building Information Modeling (BIM) with AI enhances the accuracy of project planning and execution, ensuring all project elements are well-coordinated and executed as planned (Momade et al., 2021; Chen & Ying, 2022; Saeed et al., 2022). Several trends illustrate the growing presence of AI in the construction industry. One significant trend is AI’s role in project management (Regona et al., 2022; Mendoza et al., 2022; Regona et al., 2024). AI algorithms can analyze historical project data to provide insights into project timelines, budget forecasts, and resource needs. This data-driven approach enables construction managers to make informed decisions, anticipate potential issues, and adjust plans proactively. Another trend is AI’s application in design and engineering. Generative design, an AI-driven approach, allows engineers to input design parameters and constraints, with the AI generating multiple design alternatives. This accelerates the design phase and often results in innovative and optimized solutions that might not emerge from traditional methods. AI is also advancing in construction robotics. Autonomous machines, such as drones and robots, are increasingly used for tasks like site surveys, bricklaying, and concrete pouring. These AI-driven machines can work continuously without fatigue, boosting productivity and ensuring consistent quality. Drones equipped with AI capabilities are particularly valuable for conducting aerial site inspections and monitoring progress, providing real-time data to keep projects on track. Predictive maintenance powered by AI is becoming a standard practice. By analyzing data from equipment sensors, AI can predict when machinery is likely to fail or require maintenance, allowing for timely interventions (Regona et al., 2022; Regona et al., 2024). This not only extends equipment lifespan but also reduces downtime and maintenance costs. Despite the promising benefits and trends, AI adoption in the construction industry faces significant challenges. One major barrier is the high initial cost of implementing AI technologies. Integrating AI requires substantial investments in hardware, software, and training, which can be prohibitive for many construction firms, particularly small and medium-sized enterprises (SMEs) (Liang et al., 2024; Liu et al., 2024; Adeloye et al., 2023). The industry also confronts a skills gap. Successful AI implementation necessitates a workforce proficient in both construction practices and advanced technological solutions. There is a growing need for training programs to equip construction professionals with the necessary AI-related skills. Without such training, the full potential of AI cannot be realized. Data management presents another critical challenge (Mohapatra et al., 2023; Oluleye et al., 2023). AI systems depend on large volumes of high-quality data to function effectively. In construction, data is often fragmented and stored in disparate systems. Integrating these data sources to create a cohesive and accessible data environment is a complex task that many firms struggle with. Additionally, the conservative nature of the construction industry can hinder AI adoption. The industry has traditionally been slow to embrace new technologies, and there is often resistance to change. This cultural barrier can impede AI implementation, as stakeholders may be reluctant to deviate from established practices and workflows. 4.2 Methodology A thorough literature review was conducted to compile existing knowledge and insights on the acceptance and implementation of AI in the construction industry. The review encompassed academic journals, conference papers, industry reports, and other relevant publications from the past such as and to a and comprehensive of The was on factors influencing AI acceptance, current AI application trends, and by the construction industry in AI technologies. This analyzing and from various to provide a of the existing research insights into the in the a was This and the and of of significant within the the the most and This a of the and research and further This was in the and trends in AI acceptance and implementation within the construction industry. was conducted to the and into providing a of the of research and in AI adoption in the construction industry. By algorithms to the the and into These major of and the and factors with The the of factors influencing AI acceptance, such as technological readiness, organizational and as as and trends within This approach a and comprehensive of the complex of AI implementation in the construction industry. and and of the In the such as of and in the research The of and the research on AI into construction to enhance efficiency, safety, and The these highlights growing and to through with and are essential that training algorithms to from data and make or in the application in the construction industry for predictive maintenance, safety and project of and are also in to devices that and for real-time monitoring and in of and with AI a trend these to create construction These to the of AI in managing construction Decision systems AI to assist in resource allocation, and The role in project outcomes and in 4.1 of the in literature AI and This and These the various AI and in construction, the of AI research on to industry and like and this role in safety and AI is used to predict hazards, monitor safety and analyze industry safety Construction and of and this on construction through and These to data and to construction processes, efficiency, and in engineering Project and like and this highlights application in project AI-driven systems assist project managers in informed decisions, and managing and costs. and such as and the of knowledge management and in adopting AI in practices and are for professionals with the necessary skills to AI solutions. The and provide insights into trends and in adopting AI in The of machine and a on advanced to and enhance The of AI with and a trend construction AI integration with devices enables real-time predictive maintenance, and project outcomes and costs. The of a growing on AI to enhance construction safety AI algorithms predict and monitor and analyze data for AI-driven systems optimize project management processes, in data-driven resource allocation, and and cost data management and integration are for AI implementation in and of data that need and to AI in construction is by a lack of and training programs are essential to this and equip professionals with the necessary AI implementation requires significant in software, and Construction need to the and for AI adoption. AI in construction and such as data and these concerns is for and AI and theories of technology acceptance in construction industry The construction industry, often as traditional and slow to to change, has a significant of artificial intelligence (AI) technologies. The adoption and integration of AI in this are by various models and that the of technology acceptance and the factors influencing to embrace these Technology Acceptance Model The Technology Acceptance Model (TAM), by in is a for technology to factors acceptance of perceived and perceived ease of In the construction industry, perceived to the to which AI technology enhances construction processes, project or competitive ease of to construction professionals can and AI For AI-driven project management benefits such as cost and safety to be perceived as Additionally, these be to adoption construction may lack advanced skills. 4.1 the models and theories of technology acceptance in construction industry. 4.1 and theories of technology acceptance in construction industry in Construction Technology Acceptance Model acceptance of technology on perceived and ease of of the adoption of construction management Unified Theory of Acceptance and Use of Technology elements from multiple models to and technology adoption construction Diffusion of the and factors influencing the of new technologies. Innovation the adoption of new construction like Theory of planned on and perceived to construction that technology adoption is likely when the technology task Technology construction to project needs. Theory and learning and Learning, construction workers in new safety through Model of Innovation on resistance to adopting new technologies. for resistance to new construction Model and of technology on initial and with construction management Theory of on the and to predict the adoption of on Model technology adoption on and factors the adoption of construction robotics. Unified Theory of Acceptance and Use of Technology Building on the Unified Theory of Acceptance and Use of Technology (UTAUT), by et in of technology and In the construction to the that AI to such as project efficiency or to perceived ease of in the of the complexity of AI the of or the of in the industry, from industry can significantly conditions the of and support, including training programs and which are for the implementation of AI in Diffusion of Theory Diffusion of in offers a comprehensive for new and within a or industry. to the adoption of AI in the construction industry can be through and is the perceived of AI existing methods. For instance, AI-driven predictive maintenance can significant cost savings by equipment thus a AI within the existing and practices of construction the perceived of and AI technologies. to the to which AI can be on a involves the of AI such as through projects or AI in Theory of The Theory of by in the role of and perceived in technology In the construction industry, AI are shaped by benefits and are when AI is perceived to enhance safety, and streamline to the of and industry on an to stakeholders and industry for AI a environment for to the of to AI effectively. training and can enhance this of for construction professionals to Innovation Theory Innovation Theory by and in offers insights into the resistance to new technologies. that resistance can from and and In the construction industry, might concerns the of AI high implementation costs, or potential to existing workflows. of in AI or to established these is for to such as through AI and the role of AI in skills Model The by and in that technology is likely to be with the tasks is to In the construction industry, this that AI with the and tasks of construction For AI for project site and safety management be to the unique of these AI are with a of construction and are likely to be perceived as valuable and by industry The by et in extends the by the and technological factors. In the construction industry, factors the skills, and of construction workers and factors encompass the and of construction factors the and integration of AI systems. Successful AI adoption in construction requires a approach that these For instance, a of providing comprehensive training and ensuring integration of AI with existing systems can enhance the and acceptance of AI technologies. influencing artificial intelligence acceptance in construction industry and Innovation The of technology is a in the acceptance of AI in the construction industry (Mohapatra et al., 2023; Oluleye et al., 2023). such as Building Information Modeling and the of the for AI These the of of which AI systems can analyze to optimize construction For instance, with AI can predict potential project and project management The and of AI algorithms further in AI’s to and acceptance within the industry. and benefits are in the acceptance of AI in AI can significantly reduce with waste, and project By AI allows construction to to substantial cost Additionally, AI-driven predictive maintenance can the lifespan of downtime and costs. The potential for through these AI an for construction firms, driving and The of AI on the workforce presents both and challenges. one AI can by and allowing workers to on the other there is and the need for new Successful acceptance of AI on these workforce training and for workers to and with AI systems can ease the AI can enhance safety and create new can of and a AI adoption. and and a significant role in AI acceptance in The industry is with safety and AI with these to be and for AI implementation can provide a that safety, and and industry need to and these to in AI systems. Additionally, to data and in AI-driven construction projects is essential for and and management AI with a that and technological are likely to AI solutions. a pivotal role in AI adoption by benefits and into the Furthermore, a of and to embrace new technologies. to is a but and of AI’s can Project and The complexity and of construction projects also AI projects with high of complexity the most from AI integration due to the significant of data and the need for AI can enhance project management by resource allocation, and For the on in AI might not be as a challenge to the and of AI solutions to various project can in acceptance the industry. and The construction industry is multiple stakeholders such as and The acceptance of AI on to and these AI-powered that with existing systems and data can enhance a that AI construction firms, and industry is for driving AI adoption. and can the and acceptance of AI technologies. and in AI systems is to Construction professionals need to that AI solutions are and in Building this requires in AI algorithms make and ensuring that AI systems are and of AI’s capabilities and can Additionally, practices and for AI implementation in construction can in a environment for AI adoption. and with A challenge in the construction involves AI with current systems and workflows. The industry on and systems for project management, design, and For AI solutions to be with these existing systems. Without there is a of data and AI solutions be to and integration with established and adoption. and of AI The and of AI are essential for acceptance within the construction industry. the in project and AI solutions be to need to to a of from small to and AI that can be to project are likely to be the to AI solutions in with project and the project and a growing on the construction industry to AI has the potential to enhance these by resource waste, and for AI to acceptance, with AI solutions that construction as the of of and of likely to be the benefits of AI can from stakeholders a role in the adoption of AI including such as data and are In construction, this to ensuring of and data AI systems be to and to worker and proactive of concerns can in AI technologies. and for AI in construction can further these concerns and and The design of AI technologies, particularly and significantly Construction professionals in and complex or systems can hinder adoption. AI solutions that are accessible and to comprehensive training and is also critical to utilization of AI technologies. A can enhance overall and acceptance of AI in the construction industry. 4.2 the factors influencing artificial intelligence acceptance in construction industry. 4.2 influencing artificial intelligence acceptance in construction industry Current trends in artificial intelligence adoption in construction industry Project The adoption of AI in construction is driven by the for project management, safety, and cost efficiency (Mohamed & Mohamad, 2021; Heo et al., 2021; Bolpagni & Bartoletti, 2021). A notable trend in the construction industry is the application of AI in project AI algorithms analyze from projects to potential delays and cost By historical and real-time AI project managers with informed and optimized resource such as predictive and machine learning models are becoming essential in and project to project Building Information Modeling (BIM) Building Information Modeling (BIM) has construction planning and Integrating AI with is an trend that the capabilities of both technologies. the design detect design and optimize This integration and and Additionally, AI data to insights on and and construction and and adoption in construction is significantly by AI-powered tasks such as bricklaying, concrete and site with high the on and Autonomous construction by in worker safety and project AI is increasingly used to optimize and Generative design algorithms AI to create multiple design on parameters and This enables and engineers to various design and the most and solutions. AI-driven design enhances and that designs are and with particularly in the of project Construction and AI significantly enhances site monitoring and safety AI-powered and drones monitor construction sites in real-time, potential and ensuring safety These systems detect and alert site Additionally, AI data from wearable devices to monitor and fatigue to a Predictive Predictive maintenance is a growing trend in AI adoption within the construction industry. AI algorithms analyze data from in construction equipment to predict maintenance preventing and Predictive maintenance equipment lifespan and project are particularly valuable for AI in The construction complex involves stakeholders and AI management, efficiency and costs. AI algorithms optimize and effectively. real-time AI construction in decisions, waste, and ensuring timely preventing delays and cost and Building is a growing in construction, and AI a role in AI data on and to for the of construction For instance, AI designs for efficiency, and construction to Integrating AI into enables construction to and requirements effectively. with AI AI in the construction industry by providing data-driven insights and AI-powered of data from various including project management software, financial and to These insights construction managers to make informed project resource allocation, and AI for project reduces costs, and overall The with a of the construction industry, the AI adoption increasing to substantial This adoption is several AI-driven design with design, Building Information Modeling and Predictive maintenance with on equipment and resource Construction planning and for and resource monitoring and management in and with and Autonomous equipment and management and Construction site monitoring and efficiency, and the role of real-time progress and The the of AI various of the construction industry, growing and to illustrate the current trends in AI adoption in the construction industry of artificial intelligence adoption in construction industry Data and A major to AI adoption in construction is the and of AI systems depend on to and effectively. In the construction industry, data is often and fragmented due to the of and For instance, data may be in various by the industry. This can significantly impede the and of AI which on data for machine learning with The construction industry a of systems and technologies, from project management and Building Information Modeling (BIM) to various machinery and Integrating AI solutions with these existing systems can be complex and These systems are often not to with one to the advanced data for AI into existing without ongoing presents a significant challenge that planning and AI technology involves substantial initial costs. These not only the AI technology but also the to AI such as advanced and data Additionally, significant investments may be in training and existing to new AI-driven methods. For many construction particularly small to medium-sized these can be AI adoption. The construction industry faces a notable in and to and AI systems. The current workforce is often with traditional and may lack the necessary skills to and AI technologies. this requires substantial in training and new with in data and which can be a slow and and Integrating AI into construction also various and For AI for monitoring and managing construction sites concerns data and with and Furthermore, as AI systems in critical processes, of and when an AI-driven or is complex and not by existing to is a significant barrier in many including There can be and resistance from to traditional methods. This cultural resistance can slow the adoption of new technologies, as may be reluctant to or on AI solutions. this challenge requires the benefits of training programs that the for the and is in new including that can enhance or not safety the of AI in critical tasks that are that AI systems are and various conditions is essential these systems can be are in the adoption of AI in AI-driven can significant from due to to project management that might that AI systems and is for and acceptance from all The integration of artificial intelligence (AI) within the construction industry a by various technology acceptance models and such as the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology been in the factors driving AI adoption in These models perceived usefulness, ease of use, and as of AI the Diffusion of highlights the of and AI adoption These insights valuable on construction professionals and AI technologies. The factors influencing AI acceptance in the construction industry are and factors the perceived benefits of AI in boosting safety, and costs. readiness, the of and a Additionally, external factors such as and competitive significantly affect AI adoption. The of these factors a complex environment AI acceptance and implementation depend on both and Current trends in AI adoption within the construction industry reveal a growing of AI transformative AI-powered are for project management, predictive maintenance, and design The integration of AI with Building Information Modeling (BIM) and the of as real-time data and project efficiency and Additionally, AI-driven and are construction processes, and costs. These trends a and construction Despite these promising trends, AI adoption in the construction industry faces several challenges. to change, lack of and AI and concerns data and are significant Furthermore, the high initial and the for training and of the workforce considerable challenges. The construction fragmented with stakeholders and of technological further the adoption to resistance to change, data and substantial requirements be critical to potential in the construction industry. these trends and the the for a innovative and in L., J. & Artificial intelligence in the construction industry: A review of and challenges. of Building & of Artificial Intelligence (AI) in the construction industry: A review of & Deep learning in the construction industry: A review of and of Building & Bartoletti, Artificial intelligence in the construction industry: benefits and In of the P., & Ying, Artificial intelligence in the construction industry: and P., & Artificial intelligence in the industry: and of research in construction, & of data the artificial intelligence projects in the engineering and construction industry. & Kamal, systems research in the construction industry. with & J. J. (2024). of artificial intelligence and in the and construction industry. in L., & (2024). Artificial intelligence for and management in construction industry: A literature Information J. & A review on the role of artificial intelligence in the construction industry. & Mohamad, The implementation of artificial intelligence (AI) in the construction industry. In Publishing. & Artificial intelligence to design and In of the on and in Construction & of Artificial Intelligence in the Construction J. & review of application of artificial intelligence in engineering and in and & Artificial Intelligence for the implementation of in the construction industry: A critical and & Building the of the construction industry through artificial intelligence and Patil, of artificial intelligence in construction of in & (2024). Artificial Intelligence and of the Construction and & Artificial for the construction industry: are perceived and in of and & and adoption of AI in the construction industry: A of and & Building artificial intelligence and construction in the & Artificial Intelligence and in Construction L., & The technology in the construction industry: the of artificial

Topics & Concepts