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Artificial Neural Networks for Sustainable Development of the Construction Industry

Author

Listed:
  • Mohd. Ahmed

    (Civil Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Saeed AlQadhi

    (Civil Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Javed Mallick

    (Civil Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Nabil Ben Kahla

    (Civil Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Hoang Anh Le

    (Faculty of Environmental Sciences, VNU University of Science, Vietnam National University (VNU), 334 Nguyen Trai, Thanh Xuan, Hanoi 100000, Vietnam)

  • Chander Kumar Singh

    (Department of Energy and Environment, TERI School of Advanced Studies, New Delhi 110070, India)

  • Hoang Thi Hang

    (Natural Science, Jamia Millia Islamia, New Delhi 110025, India)

Abstract

Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.

Suggested Citation

  • Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14738-:d:967103
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    References listed on IDEAS

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    Cited by:

    1. Andrzej Pacana & Karolina Czerwińska, 2023. "A Quality Control Improvement Model That Takes into Account the Sustainability Concept and KPIs," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    2. 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.
    3. Tian Zhu & Guangchen Liu, 2022. "A Novel Hybrid Methodology to Study the Risk Management of Prefabricated Building Supply Chains: An Outlook for Sustainability," Sustainability, MDPI, vol. 15(1), pages 1-22, December.

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