Investigating the Role of Artificial Intelligence Technologies in the Construction Industry Using a Delphi-ANP-TOPSIS Hybrid MCDM Concept under a Fuzzy Environment

Abstract

The construction business is always changing, and with the introduction of artificial intelligence (AI) technology it is undergoing substantial modifications in a variety of areas. The purpose of this research paper is to investigate the function of AI tools in the construction industry using a hybrid multi-criteria decision-making (MCDM) framework based on the Delphi method, analytic network process (ANP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under a fuzzy scenario. The ANP framework offers a systematic approach to quantifying the relative importance of AI technologies based on expert opinions gathered during the Delphi process, whereas the fuzzy TOPSIS methodology is used to rank and select the most appropriate AI technologies for the construction industry. The final results from the ANP revealed that the technological factors are the most crucial, followed by the environmental factors, which highly influence the AI environment. In addition, TOPSIS identified robotics and automation as the best AI alternative among the three options, followed by building information modeling (BIM), whereas computer vision was the least preferred among the list. The proposed hybrid MCDM framework enables a comprehensive evaluation and selection process that takes into account the interdependencies between AI technologies and uncertainties in decision-making.

Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

“…These factors encompass different AI technologies, methodologies, strategies, or considerations that affect how cost management is conducted, optimized, and improved. The importance of these critical AI parameters in cost management lies in their ability to enhance decision-making, optimize resource allocation, and increase project efficiency [41,45]. After several in-person meetings, the panel identified 17 AI factors that significantly influence cost management in civil engineering, which were further categorized into eight groups, as shown in Figure 1.…”

“…Machine learning models can optimize energy usage, reduce waste, and lower environmental impact, leading to more sustainable civil engineering practices. AI-driven sustainability efforts allow organizations to meet environmental goals while maintaining their project's cost-effectiveness and efficiency [36][37][38][39]45,46].…”

“…Environmental impact (F51): Considering environmental impact in civil engineering cost management is crucial for compliance, minimizing ecological harm, and avoiding costly penalties. Addressing environmental impact fosters long-term project viability, community goodwill, and reduces the financial risks associated with environmental liabilities [36,37,45].…”

“…The social implications cover broader issues like job displacement, inequality, and autonomy concerns due to AI's adoption. Ethical AI frameworks, guidelines, and governance mechanisms are crucial for managing risks, fostering trust, and promoting ethical practices in civil engineering projects [43,45,47,49].…”

“…• Equity and fairness (F81): Equity and fairness ensure transparent decisionmaking, foster stakeholder trust, and reduce potential conflicts. Emphasizing these values in cost management encourages accountability and stakeholder participation and supports the social responsibility of civil engineering projects [45,46,49].…”