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

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 decade. Key databases such as IEEE Xplore, ScienceDirect, and Google Scholar were utilized to ensure a broad and comprehensive collection of sources. The focus was on identifying factors influencing AI acceptance, current AI application trends, and challenges faced by the construction industry in integrating AI technologies. This involved systematically analyzing and synthesizing findings from various studies to provide a coherent overview of the existing research landscape. To gain deeper insights into the relationships between key concepts in the literature, a co-occurrence analysis was performed. This analysis identified and examined the frequency and patterns of co-occurrence of significant keywords within the collected literature. Using bibliometric tools VOSviewer, the co-occurrence analysis revealed the most commonly discussed themes and their interconnections. This analysis provided a visual representation of the key topics and their associations, highlighting prominent research areas and gaps needing further exploration. This step was crucial in understanding the primary focus areas and emerging trends in AI acceptance and implementation within the construction industry.

Cluster analysis was conducted to categorize the identified themes and topics into meaningful groups, providing a clearer understanding of the distinct areas of research and practice in AI adoption in the construction industry. By applying clustering algorithms to the co-occurrence data, the analysis grouped related concepts and themes into clusters. These clusters represented major domains of interest and the specific challenges and factors associated with each domain. The cluster analysis facilitated the identification of key factors influencing AI acceptance, such as technological readiness, organizational culture, and regulatory environment, as well as challenges and trends within each cluster. This approach enabled a structured and comprehensive examination of the complex landscape of AI implementation in the construction industry.

4.3 Results and discussions

Co-occurrence and cluster analysis of the keywords

In the examined co-occurrence network (Fig. 4.1), central keywords such as "artificial intelligence," "construction industry," "machine learning," "deep learning," "internet of things," "BIM," "construction projects," and "decision support systems" emerge, indicating their prominence in the research landscape. The prominence of "artificial intelligence" and "construction industry" underscores the research focus on integrating AI into construction to enhance efficiency, safety, and decision-making. The strong link between these keywords highlights growing interest and efforts to address industry-specific challenges through AI. Frequently appearing with AI, "machine learning" and "deep learning" are essential subsets that involve training algorithms to learn from data and make predictions or decisions. Their centrality in the network shows their extensive application in the construction industry for predictive maintenance, safety monitoring, and project management. "Internet of things" and "BIM" are also crucial, reflecting their significance in construction. IoT refers to interconnected devices that collect and exchange data, vital for real-time monitoring and automation in construction. BIM, involving digital representations of building characteristics, facilitates better collaboration and decision-making. Their co-occurrence with AI indicates a trend towards integrating these technologies to create smart, efficient construction processes. These keywords relate to the practical applications of AI in managing construction projects. Decision support systems utilize AI to assist in planning, scheduling, resource allocation, and risk management. The frequent co-occurrence suggests AI's role in optimizing project outcomes and enhancing decision-making in construction.

Fig. 4.1 Co-occurrence analysis of the keywords in literature

Cluster 1: AI Technologies and Methods (Yellow Cluster) 

This cluster includes "machine learning," "deep learning," "neural networks," "support vector machines," "data mining," "genetic algorithms," and "optimization." These terms represent the various AI techniques being explored and applied in construction, highlighting the technical foundation of AI research focused on algorithm development to solve industry problems.

Cluster 2: Safety and Risk Management (Blue Cluster) 

Including keywords like "construction safety," "safety," "accidents," "decision trees," and "forecasting," this cluster emphasizes AI's role in enhancing safety and risk management. AI is used to predict hazards, monitor safety compliance, and analyze accident data, improving industry safety standards.

Cluster 3: Smart Construction and IoT (Green Cluster) 

Containing "internet of things," "BIM," "virtual reality," "industry 4.0," "big data," "energy efficiency," "energy utilization," and "engineering education," this cluster focuses on creating smart construction environments through IoT and BIM integration. These keywords indicate efforts to leverage data and digital tools to improve construction processes, energy efficiency, and innovation in engineering education.

Cluster 4: Project Management and Decision-Making (Red Cluster) 

With keywords like "construction projects," "decision support systems," "planning," "costs," "contractors," "computer simulation," "expert systems," and "decision making," this cluster highlights AI's application in project management. AI-driven systems assist project managers in making informed decisions, optimizing resources, and managing timelines and costs.

Cluster 5: Knowledge Management and Education (Purple Cluster) 

Keywords such as "knowledge management," "engineering education," and "construction companies" underscore the importance of knowledge management and education in adopting AI in construction. Effective practices and initiatives are crucial for equipping professionals with the necessary skills to implement AI solutions.

The co-occurrence and cluster analysis provide insights into trends and challenges in adopting AI in construction. The centrality of AI, machine learning, deep learning, IoT, and BIM reflects a focus on leveraging advanced technologies to tackle industry-specific challenges and enhance efficiency. The frequent co-occurrence of AI with IoT and BIM suggests a trend towards smart construction environments. AI integration with IoT devices enables real-time monitoring, predictive maintenance, and automation, improving project outcomes and reducing costs. The prominence of safety-related keywords indicates a growing emphasis on using AI to enhance construction safety standards. AI algorithms predict and prevent accidents, monitor compliance, and analyze accident data for better risk management. AI-driven decision support systems optimize project management processes, aiding in data-driven decision-making, resource allocation, and effective timeline and cost management.

Effective data management and integration are crucial for AI implementation in construction. IoT and BIM generate vast amounts of data that need efficient collection, storage, and analysis to derive meaningful insights. Adopting AI in construction is hindered by a lack of skilled professionals. Educational initiatives and training programs are essential to bridge this gap and equip professionals with the necessary knowledge. AI implementation requires significant investment in infrastructure, software, and training. Construction companies need to assess the cost-benefit ratio and secure funding for AI adoption. AI use in construction raises regulatory and ethical concerns, such as data privacy, security, and accountability. Addressing these concerns is crucial for responsible and ethical AI use.

Models and theories of technology acceptance in construction industry

The construction industry, often characterized as traditional and slow to adapt to change, has recently experienced a significant influx of artificial intelligence (AI) technologies. The adoption and integration of AI in this sector are guided by various theoretical models and frameworks that elucidate the dynamics of technology acceptance and the factors influencing stakeholders' willingness to embrace these innovations.

Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM), formulated by Davis in 1989, is a widely utilized framework for understanding technology acceptance. According to TAM, two primary factors influence users' acceptance of technology: perceived usefulness and perceived ease of use. In the construction industry, perceived usefulness pertains to the extent to which AI technology enhances construction processes, improves project outcomes, or provides competitive advantages. Perceived ease of use refers to how effortlessly construction professionals can learn and use AI tools. For example, AI-driven project management software must demonstrate tangible benefits such as time savings, cost reductions, and enhanced safety to be perceived as useful. Additionally, these tools must be user-friendly to ensure broad adoption among construction workers, who may lack advanced technical skills. Table 4.1 shows the models and theories of technology acceptance in construction industry.

Table 4.1 Models and theories of technology acceptance in construction industry

Sr. No.

Model/Theory

Overview

Key Factors

Application in Construction

1

Technology Acceptance Model (TAM)

Examines user acceptance of technology based on perceived usefulness and ease of use.

Perceived Ease of Use, Perceived Usefulness, Behavioral Intention

Evaluating the adoption of construction management software.

2

Unified Theory of Acceptance and Use of Technology (UTAUT)

Combines elements from multiple models to explain user intentions and technology usage.

Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions

Assessing BIM adoption among construction professionals.

3

Diffusion of Innovations (DOI)

Explains the process and factors influencing the spread of new technologies.

Innovation Characteristics, Communication Channels, Time, Social System

Understanding the adoption of new construction technologies like drones.

4

Theory of Planned Behavior (TPB)

Predicts planned behavior based on attitudes, subjective norms, and perceived control.

Attitude Toward Behavior, Subjective Norm, Perceived Behavioral Control

Analyzing intentions to adopt sustainable construction practices.

5

Task-Technology Fit (TTF)

Suggests that technology adoption is more likely when the technology supports task requirements.

Task Characteristics, Technology Characteristics, Task-Technology Fit

Matching construction tools to specific project needs.

6

Social Cognitive Theory (SCT)

Describes how people learn and maintain behavioral patterns, considering observational learning and self-efficacy.

Observational Learning, Self-Efficacy, Outcome Expectations

Training construction workers in new safety technologies through modeling.

7

Model of Innovation Resistance

Focuses on understanding resistance to adopting new technologies.

Usage Barrier, Value Barrier, Risk Barrier, Tradition Barrier, Image Barrier

Identifying reasons for resistance to new construction techniques.

8

Expectation-Confirmation Model (ECM)

Explains user satisfaction and continued use of technology based on initial expectations and subsequent confirmation.

Expectation, Confirmation, Perceived Usefulness, Satisfaction

Evaluating post-adoption satisfaction with construction management tools.

9

Theory of Reasoned Action (TRA)

Focuses on the relationship between attitudes, intentions, and behaviors to predict actions.

Attitude Toward Behavior, Subjective Norm

Predicting the adoption of green building technologies based on attitudes.

10

Motivational Model (MM)

Explains technology adoption based on intrinsic and extrinsic motivations.

Intrinsic Motivation, Extrinsic Motivation

Assessing factors motivating the adoption of construction robotics.

Unified Theory of Acceptance and Use of Technology (UTAUT)

Building on TAM, the Unified Theory of Acceptance and Use of Technology (UTAUT), proposed by Venkatesh et al. in 2003, identifies four core determinants of technology acceptance: performance expectancy, effort expectancy, social influence, and facilitating conditions. In the construction sector, performance expectancy refers to the belief that using AI will lead to performance improvements, such as increased project efficiency or enhanced quality control. Effort expectancy, akin to perceived ease of use in TAM, emphasizes the importance of minimizing the complexity of AI tools. Social influence captures the impact of peer pressure or the opinions of influential figures in the industry, illustrating how endorsements from industry leaders can significantly sway acceptance. Facilitating conditions involve the availability of resources and support, including training programs and technical assistance, which are crucial for the successful implementation of AI technologies in construction.

Diffusion of Innovations (DOI) Theory

Everett Rogers' Diffusion of Innovations (DOI) theory, introduced in 1962, offers a comprehensive framework for understanding how new ideas and technologies spread within a society or industry. According to DOI, the adoption of AI in the construction industry can be examined through five characteristics: relative advantage, compatibility, complexity, trialability, and observability. Relative advantage is the perceived superiority of AI technologies over existing methods. For instance, AI-driven predictive maintenance can offer significant cost savings by preemptively identifying equipment failures, thus presenting a clear advantage. Compatibility assesses how well AI fits within the existing workflows and practices of construction firms. Complexity addresses the perceived difficulty of understanding and using AI technologies. Trialability refers to the extent to which AI can be tested on a limited basis before full-scale implementation. Lastly, observability involves the visibility of AI benefits, such as through pilot projects or case studies demonstrating successful AI applications in construction.

Theory of Planned Behavior (TPB)

The Theory of Planned Behavior (TPB), developed by Ajzen in 1985, emphasizes the role of individual attitudes, subjective norms, and perceived behavioral control in predicting technology acceptance. In the construction industry, attitudes toward AI are shaped by beliefs about its benefits and drawbacks. Positive attitudes are fostered when AI is perceived to enhance job performance, improve safety, and streamline operations. Subjective norms relate to the influence of peers, superiors, and industry standards on an individual's intention to use AI. If key stakeholders and industry leaders advocate for AI adoption, it creates a favorable environment for acceptance. Perceived behavioral control refers to the individual's perception of their ability to use AI technologies effectively. Providing adequate training and resources can enhance this sense of control, making it easier for construction professionals to adopt AI.

Innovation Resistance Theory (IRT)

Innovation Resistance Theory (IRT), introduced by Ram and Sheth in 1989, offers insights into the resistance to new technologies. IRT emphasizes that resistance can stem from functional barriers (practical issues) and psychological barriers (emotional and cognitive issues). In the construction industry, functional barriers might include concerns about the reliability of AI systems, high implementation costs, or potential disruptions to existing workflows. Psychological barriers could involve fear of job displacement, distrust in AI decision-making, or reluctance to change established practices. Understanding these barriers is crucial for developing strategies to mitigate resistance, such as through transparent communication, demonstrating AI reliability, and highlighting the complementary role of AI in augmenting human skills rather than replacing them.

Task-Technology Fit (TTF) Model

The Task-Technology Fit (TTF) model, proposed by Goodhue and Thompson in 1995, posits that technology is more likely to be accepted if it fits well with the tasks it is intended to support. In the construction industry, this means that AI technologies should align with the specific needs and tasks of construction projects. For example, AI applications for project scheduling, site monitoring, and safety management must be tailored to the unique challenges of these tasks. When AI tools are designed with a deep understanding of construction workflows and requirements, they are more likely to be perceived as valuable and adopted by industry professionals.

Human-Organization-Technology Fit (HOT-fit) Framework

The Human-Organization-Technology fit (HOT-fit) framework, developed by Yusof et al. in 2008, extends the TTF model by considering the interplay between human, organizational, and technological factors. In the construction industry, human factors include the skills, attitudes, and behaviors of construction workers and managers. Organizational factors encompass the culture, structure, and processes of construction firms. Technological factors involve the capabilities, usability, and integration of AI systems. Successful AI adoption in construction requires a holistic approach that addresses these interconnected elements. For instance, fostering a culture of innovation, providing comprehensive training programs, and ensuring seamless integration of AI tools with existing systems can enhance the fit and acceptance of AI technologies.

Factors influencing artificial intelligence acceptance in construction industry

Technological Advancements and Innovation

The rapid advancement of technology is a primary driver in the acceptance of AI in the construction industry (Mohapatra et al., 2023; Oluleye et al., 2023). Innovations such as Building Information Modeling (BIM), robotics, and the Internet of Things (IoT) have laid the groundwork for AI integration. These technologies enable the collection of vast amounts of data, which AI systems can analyze to optimize construction processes. For instance, BIM combined with AI can predict potential project risks and delays, enhancing project management efficiency. The continuous development and refinement of AI algorithms further bolster confidence in AI’s capability to deliver accurate and actionable insights, fostering greater acceptance within the industry.

Economic and Financial Considerations

Economic benefits are crucial in the acceptance of AI in construction. AI can significantly reduce costs associated with labor, material waste, and project overruns. By automating routine tasks, AI allows construction companies to allocate resources more efficiently, leading to substantial cost savings. Additionally, AI-driven predictive maintenance can extend the lifespan of equipment, reducing downtime and repair costs. The potential for improved profitability through these economic efficiencies makes AI an attractive investment for construction firms, driving its acceptance.

Workforce Dynamics and Skills Development

The impact of AI on the workforce presents both opportunities and challenges. On one hand, AI can alleviate labor shortages by automating repetitive and physically demanding tasks, allowing workers to focus on higher-value activities. On the other hand, there is apprehension regarding job displacement and the need for new skill sets. Successful acceptance of AI hinges on addressing these workforce concerns. Providing training and upskilling opportunities for workers to operate and collaborate with AI systems can ease the transition. Moreover, demonstrating how AI can enhance job safety and create new job categories can mitigate fears of job loss and promote a more positive outlook towards AI adoption.

Regulatory and Legal Frameworks

Regulatory and legal considerations play a significant role in AI acceptance in construction. The industry is heavily regulated, with stringent safety and quality standards. AI technologies must comply with these regulations to be deemed viable. Clear guidelines and standards for AI implementation can provide a framework that ensures safety, reliability, and accountability. Governments and industry bodies need to establish and enforce these regulations to build trust in AI systems. Additionally, addressing legal issues related to data privacy, intellectual property, and liability in AI-driven construction projects is essential for fostering acceptance.

Cultural and Organizational Factors

Organizational culture and management attitudes towards innovation greatly influence AI acceptance. Companies with a culture that embraces change and values technological advancement are more likely to adopt AI solutions. Leadership plays a pivotal role in championing AI adoption by demonstrating its benefits and integrating it into the company’s strategic vision. Furthermore, fostering a culture of continuous improvement and innovation encourages employees to embrace new technologies. Resistance to change is a natural barrier, but strong leadership and clear communication of AI’s advantages can help overcome it.

Project Complexity and Scale

The complexity and scale of construction projects also influence AI acceptance. Large-scale projects with high levels of complexity benefit the most from AI integration due to the significant amount of data generated and the need for precise coordination. AI can enhance project management by optimizing schedules, resource allocation, and risk management. For smaller projects, the return on investment in AI might not be as immediately apparent, posing a challenge to acceptance. Demonstrating the scalability and adaptability of AI solutions to various project sizes can help in gaining broader acceptance across the industry.

Collaboration and Ecosystem Development

The construction industry is inherently collaborative, involving multiple stakeholders such as architects, engineers, contractors, and suppliers. The acceptance of AI depends on its ability to facilitate better collaboration and communication among these parties. AI-powered platforms that integrate seamlessly with existing systems and promote data sharing can enhance collaboration. Developing a robust ecosystem that includes AI solution providers, construction firms, and industry associations is crucial for driving AI adoption. Industry-wide initiatives and partnerships can accelerate the development and acceptance of AI technologies.

Trust and Transparency

Trust in AI systems is fundamental to their acceptance. Construction professionals need to trust that AI solutions are reliable, accurate, and transparent in their operations. Building this trust requires transparency in how AI algorithms make decisions and ensuring that AI systems are explainable. Providing clear documentation and demonstrations of AI’s capabilities and limitations can build confidence among users. Additionally, establishing best practices and standards for AI implementation in construction can help in creating a trustworthy environment for AI adoption.

Interoperability and Integration with Existing Systems

A primary challenge in the construction sector involves integrating AI technologies with current systems and workflows. The industry relies heavily on diverse software and hardware systems for project management, design, and execution. For AI solutions to be adopted successfully, they must seamlessly integrate with these existing systems. Without effective interoperability, there is a risk of creating data silos, inefficiencies, and operational disruptions. Hence, AI solutions must be designed to ensure compatibility and smooth integration with established tools, minimizing disruption and promoting widespread adoption.

Scalability and Flexibility of AI Solutions

The scalability and flexibility of AI technologies are essential for their acceptance within the construction industry. Given the wide variation in project size, complexity, and requirements, AI solutions must be adaptable to different contexts. They need to cater to a range of projects, from small residential builds to large-scale infrastructure developments. Customizable and flexible AI technologies that can be tailored to meet specific project needs are more likely to be embraced. Moreover, the ability to scale AI solutions in line with project growth ensures their continued relevance and utility throughout the project lifecycle.

Environmental Impact and Sustainability

With a growing emphasis on sustainability, the construction industry seeks to minimize environmental impacts. AI has the potential to enhance these efforts by optimizing resource utilization, reducing waste, and improving energy efficiency. However, for AI technologies to gain acceptance, they must align with sustainability objectives. AI solutions that support green construction practices—such as the use of sustainable materials, reduction of carbon footprints, and enhancement of energy efficiency—are likely to be more favorably received. Highlighting the environmental benefits of AI can attract support from stakeholders who prioritize sustainability.

Ethical Considerations

Ethical considerations play a crucial role in the adoption of AI across industries, including construction. Issues such as privacy, data security, and fairness are paramount. In construction, this translates to ensuring fair treatment of workers, unbiased decision-making, and responsible data management. AI systems must be designed to avoid perpetuating biases and to safeguard worker safety. Transparent and proactive handling of ethical concerns can foster trust in AI technologies. Establishing ethical guidelines and standards for AI use in construction can further mitigate these concerns and encourage acceptance.

User Experience and Interface Design

The design of AI technologies, particularly their user experience (UX) and interface, significantly affects their acceptance. Construction professionals vary in their technical expertise, and complex or unintuitive systems can hinder adoption. Therefore, AI solutions must feature user-friendly interfaces that are accessible and easy to use. Providing comprehensive training and support is also critical to ensure effective utilization of AI technologies. A well-designed, intuitive user interface can enhance overall user experience and promote broader acceptance of AI in the construction industry. Fig. 4.2 shows the factors influencing artificial intelligence acceptance in construction industry.

Fig. 4.2 Factors influencing artificial intelligence acceptance in construction industry

Current trends in artificial intelligence adoption in construction industry

AI-Powered Project Management

The accelerating adoption of AI in construction is driven by the industry's quest for improved 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 management. AI algorithms analyze vast datasets from previous projects to forecast potential delays and cost overruns. By utilizing historical and real-time data, AI provides project managers with actionable insights, enabling informed decision-making and optimized resource allocation. Tools such as predictive analytics and machine learning models are becoming essential in risk identification and project timeline enhancement, leading to more successful project outcomes.

Building Information Modeling (BIM) Integration

Building Information Modeling (BIM) has transformed construction planning and execution. Integrating AI with BIM is an emerging trend that amplifies the capabilities of both technologies. AI-enhanced BIM platforms automate the design process, detect design conflicts, and optimize building performance. This integration facilitates more precise and efficient planning, minimizing costly errors and rework. Additionally, AI analyzes BIM data to generate insights on material usage, energy consumption, and sustainability, promoting greener and more efficient construction practices.

Robotics and Automation

Robotics and automation adoption in construction is significantly influenced by AI. AI-powered robots perform tasks such as bricklaying, concrete pouring, and site inspection with high precision, reducing the reliance on human labor and minimizing accident risks. Autonomous construction equipment, guided by AI, operates in hazardous environments, enhancing worker safety and project efficiency.

AI-Driven Design Optimization

AI is increasingly used to optimize architectural and structural designs. Generative design algorithms allow AI to create multiple design iterations based on specified parameters and constraints. This process enables architects and engineers to explore various design options and select the most efficient and cost-effective solutions. AI-driven design optimization enhances creativity and ensures that final designs are structurally sound and compliant with regulatory standards, particularly beneficial in the early stages of project development.

Construction Site Monitoring and Safety

AI significantly enhances site monitoring and safety protocols. AI-powered cameras and drones monitor construction sites in real-time, identifying potential hazards and ensuring safety regulation compliance. These systems detect unsafe behaviors and immediately alert site managers. Additionally, AI analyzes data from wearable devices to monitor workers' health and fatigue levels, contributing to a safer working environment.

Predictive Maintenance

Predictive maintenance is a growing trend in AI adoption within the construction industry. AI algorithms analyze data from sensors embedded in construction equipment to predict maintenance needs, preventing costly breakdowns and minimizing downtime. Predictive maintenance helps extend equipment lifespan and ensures project schedules are maintained, particularly valuable for large-scale projects.

AI in Supply Chain Management

The construction industry's complex supply chain involves coordinating numerous stakeholders and materials. AI streamlines supply chain management, enhancing efficiency and reducing costs. AI algorithms forecast material demand, optimize inventory levels, and manage logistics more effectively. Providing real-time supply chain visibility, AI aids construction companies in making better decisions, reducing waste, and ensuring timely material delivery, preventing delays and cost overruns.

Sustainability and Green Building Practices

Sustainability is a growing focus in construction, and AI plays a crucial role in promoting green building practices. AI analyzes data on energy usage, material consumption, and waste generation to identify opportunities for reducing the environmental impact of construction projects. For instance, AI optimizes building designs for energy efficiency, suggests sustainable materials, and monitors construction processes to minimize waste. Integrating AI into sustainability initiatives enables construction companies to achieve environmental goals and meet regulatory requirements more effectively.

Enhanced Decision-Making with AI

AI revolutionizes decision-making processes in the construction industry by providing data-driven insights and recommendations. AI-powered analytics platforms process vast amounts of data from various sources, including project management software, financial systems, and IoT devices, to generate actionable insights. These insights enable construction managers to make informed decisions regarding project planning, resource allocation, and risk management. Leveraging AI for enhanced decision-making improves project outcomes, reduces costs, and increases overall efficiency.

The Sankey diagram (Fig. 4.3), starting with a base of 100 units symbolizing the entire construction industry, the diagram shows AI adoption increasing to 230 units, reflecting substantial integration. This adoption is divided among several key areas. AI-driven design optimization leads with 50 units, enhancing generative design, Building Information Modeling (BIM), and structural analysis. Predictive maintenance follows with 30 units, focusing on equipment diagnostics, failure prediction, and resource allocation. Construction planning and scheduling account for 40 units, improving timeline optimization and resource management. Safety monitoring and risk management contribute 20 units, aiding in hazard detection and compliance monitoring. Quality control, with 35 units, ensures defect detection and standards compliance. Autonomous equipment and supply chain management each hold 25 units, emphasizing robotics, drones, and inventory optimization. Construction site monitoring and energy efficiency, although smaller at 10 and 5 units respectively, highlight the role of real-time progress tracking and energy usage optimization. The diagram effectively captures the diverse impact of AI across various facets of the construction industry, highlighting its growing significance and wide-ranging applications.

 

Fig. 4.3 Sankey diagram to illustrate the current trends in AI adoption in the construction industry

Challenges of artificial intelligence adoption in construction industry

1. Data Quality and Availability

A major obstacle to AI adoption in construction is the quality and availability of data. AI systems depend on extensive datasets to train and operate effectively. In the construction industry, data collection is often inconsistent and fragmented due to the diverse range of activities and environments involved. For instance, data may be recorded in various formats by different contractors, lacking standardization across the industry. This inconsistency can significantly impede the development and deployment of AI solutions, which rely on uniform data for machine learning algorithms.

1. Integration with Existing Systems

The construction industry utilizes a wide array of systems and technologies, from project management software and Building Information Modeling (BIM) to various on-site machinery and tools. Integrating AI solutions with these existing systems can be complex and costly. These systems are often not designed to interact with one another, nor to support the advanced data analytics required for AI. Retrofitting AI into existing infrastructures without disrupting ongoing operations presents a significant challenge that demands careful planning and execution.

1. High Initial Costs

Implementing AI technology involves substantial initial costs. These include not only the AI technology itself but also the infrastructure required to support AI systems, such as advanced sensors and data processing units. Additionally, significant investments may be needed in training personnel and modifying existing processes to accommodate new AI-driven methods. For many construction companies, particularly small to medium-sized enterprises, these costs can be prohibitive, limiting AI adoption.

1. Skill Gap

The construction industry faces a notable skill gap in digital literacy and technical expertise needed to operate and manage AI systems. The current workforce is often more familiar with traditional methods and may lack the necessary skills to effectively integrate and utilize AI technologies. Bridging this gap requires substantial investment in training and hiring new talent with expertise in AI, data science, and related fields, which can be a slow and costly process.

1. Regulatory and Legal Issues

Integrating AI into construction also raises various regulatory and legal issues. For example, using AI for monitoring and managing construction sites prompts concerns about privacy, data security, and compliance with local and international regulations. Furthermore, as AI systems become involved in critical decision-making processes, questions of liability and accountability arise. Determining responsibility when an AI-driven system fails or causes harm is complex and not yet fully addressed by existing laws.

1. Cultural Resistance

Resistance to change is a significant barrier in many industries, including construction. There can be skepticism and resistance from teams accustomed to traditional methods. This cultural resistance can slow the adoption of new technologies, as employees may be reluctant to trust or rely on AI solutions. Overcoming this challenge requires clear communication about the benefits of AI, alongside training programs that facilitate the transition for the workforce.

1. Safety and Reliability Concerns

 

Safety is paramount in construction. Any new technology, including AI, must demonstrate that it can enhance or at least not compromise safety standards. Concerns about the reliability of AI systems, especially in critical tasks that could endanger lives if performed incorrectly, are significant. Ensuring that AI systems are thoroughly tested and proven reliable under various conditions is essential before these systems can be widely adopted.

1. Ethical Considerations

Ethical considerations are crucial in the adoption of AI in construction. AI-driven decisions can have significant implications, from job displacement due to automation to project management decisions that might favour certain outcomes. Ensuring that AI systems operate transparently and ethically is vital for gaining trust and acceptance from all stakeholders.

4.4 Conclusions

The integration of artificial intelligence (AI) within the construction industry represents a multifaceted phenomenon influenced by various technology acceptance models and theories. Key theoretical frameworks such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) have been instrumental in elucidating the factors driving AI adoption in construction. These models identify perceived usefulness, ease of use, and social influence as crucial determinants of AI acceptance. Moreover, the Diffusion of Innovations (DOI) theory highlights the importance of innovation characteristics—relative advantage, compatibility, complexity, trialability, and observability—in shaping AI adoption decisions. These theoretical insights offer valuable perspectives on how construction professionals perceive and integrate AI technologies. The factors influencing AI acceptance in the construction industry are diverse and multifaceted. Prominent factors include the perceived benefits of AI in boosting productivity, enhancing safety, and reducing costs. Organizational readiness, encompassing the availability of skilled personnel and supportive leadership, plays a vital role. Additionally, external factors such as regulatory support and competitive pressures significantly affect AI adoption. The interplay of these factors creates a complex environment where AI acceptance and implementation depend on both intrinsic and extrinsic motivators.

Current trends in AI adoption within the construction industry reveal a growing recognition of AI technologies' transformative potential. Increasingly, AI-powered tools are being utilized for project management, predictive maintenance, and design optimization. The integration of AI with Building Information Modeling (BIM) and the Internet of Things (IoT) stands out, as it facilitates real-time data analysis and decision-making, thereby improving project efficiency and accuracy. Additionally, AI-driven automation and robotics are revolutionizing construction processes, enhancing precision and reducing labour costs. These trends indicate a progressive shift toward more intelligent and connected construction practices. Despite these promising trends, AI adoption in the construction industry faces several challenges. Resistance to change, lack of understanding and awareness about AI capabilities, and concerns about data security and privacy are significant barriers. Furthermore, the high initial investment costs and the necessity for continuous training and upskilling of the workforce present considerable challenges. The construction industry's fragmented nature, with its diverse stakeholders and varying levels of technological maturity, further complicates the adoption process. However, addressing challenges related to resistance to change, data security concerns, and substantial investment requirements will be critical to fully realizing AI's potential in the construction industry. Embracing these trends and overcoming the challenges will pave the way for a more innovative and sustainable future in construction.

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