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

Author

Listed:
  • Ke Wang

    (Department of Civil and Architectural Engineering, Qingdao University of Technology, Linyi 273400, China)

  • Ziyi Ying

    (College of Architecture and Energy Engineering, Wenzhou University of Technology, Wenzhou 325055, China
    Taishun Research Institute, Wenzhou University of Technology, Wenzhou 325599, China)

  • Shankha Shubhra Goswami

    (Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, India)

  • Yongsheng Yin

    (School of Architecture, Tianjin University, Tianjin 300072, China)

  • Yafei Zhao

    (Solearth Architecture Research Center, Building Information Technology Innovation Laboratory (BITI Lab.), Hong Kong 999077, China)

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.

Suggested Citation

  • Ke Wang & Ziyi Ying & Shankha Shubhra Goswami & Yongsheng Yin & Yafei Zhao, 2023. "Investigating the Role of Artificial Intelligence Technologies in the Construction Industry Using a Delphi-ANP-TOPSIS Hybrid MCDM Concept under a Fuzzy Environment," Sustainability, MDPI, vol. 15(15), pages 1-42, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11848-:d:1208553
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    References listed on IDEAS

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    1. Zaher Mundher Yaseen & Zainab Hasan Ali & Sinan Q. Salih & Nadhir Al-Ansari, 2020. "Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model," Sustainability, MDPI, vol. 12(4), pages 1-14, February.
    2. Shankha Shubhra Goswami & Dhiren Kumar Behera, 2021. "An Analysis for Selecting Best Smartphone Model by AHP-TOPSIS Decision-Making Methodology," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global Scientific Publishing, vol. 12(3), pages 116-137, May.
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    Cited by:

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    2. Mladen Krstić & Snežana Tadić & Pier Paolo Miglietta & Donatella Porrini, 2025. "Enhancing Biodiversity and Environmental Sustainability in Intermodal Transport: A GIS-Based Multi-Criteria Evaluation Framework," Sustainability, MDPI, vol. 17(4), pages 1-26, February.
    3. Natalia E. Lozano-Ramírez & Omar Sánchez & Daniela Carrasco-Beltrán & Sofía Vidal-Méndez & Karen Castañeda, 2023. "Digitalization and Sustainability in Linear Projects Trends: A Bibliometric Analysis," Sustainability, MDPI, vol. 15(22), pages 1-38, November.
    4. Shengxuan Tang & Ming Cai & Yao Xiao, 2024. "A Cross-Citation-Based Model for Technological Advancement Assessment: Methodology and Application," Sustainability, MDPI, vol. 16(1), pages 1-20, January.
    5. Hathal Salamah Alwageed & Ismail Keshta & Rafiq Ahmad Khan & Abdulrahman Alzahrani & Muhammad Usman Tariq & Anwar Ghani, 2024. "An empirical study for mitigating sustainable cloud computing challenges using ISM-ANN," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-34, September.

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