IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p14738-d967103.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/14738/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/22/14738/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Méndez Echenagucia, Tomás & Capozzoli, Alfonso & Cascone, Ylenia & Sassone, Mario, 2015. "The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis," Applied Energy, Elsevier, vol. 154(C), pages 577-591.
    2. Jonathan Jingsheng Shi, 1999. "A neural network based system for predicting earthmoving production," Construction Management and Economics, Taylor & Francis Journals, vol. 17(4), pages 463-471.
    3. Huaicheng Chen & Chunxiang Qian & Chengyao Liang & Wence Kang, 2018. "An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-17, January.
    4. Heng Li & Peter Love, 1999. "Combining rule-based expert systems and artificial neural networks for mark-up estimation," Construction Management and Economics, Taylor & Francis Journals, vol. 17(2), pages 169-176.
    5. Seung Ok & Sunil Sinha, 2006. "Construction equipment productivity estimation using artificial neural network model," Construction Management and Economics, Taylor & Francis Journals, vol. 24(10), pages 1029-1044.
    6. Yang Miang Goh & David Chua, 2013. "Neural network analysis of construction safety management systems: a case study in Singapore," Construction Management and Economics, Taylor & Francis Journals, vol. 31(5), pages 460-470, May.
    7. Jihoon Moon & Sungwoo Park & Seungmin Rho & Eenjun Hwang, 2019. "A comparative analysis of artificial neural network architectures for building energy consumption forecasting," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    8. D’Oca, Simona & Hong, Tianzhen & Langevin, Jared, 2018. "The human dimensions of energy use in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 731-742.
    9. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    10. Wenquan Jin & Israr Ullah & Shabir Ahmad & Dohyeun Kim, 2019. "Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
    11. Christine Oliver, 1997. "Sustainable competitive advantage: combining institutional and resource‐based views," Strategic Management Journal, Wiley Blackwell, vol. 18(9), pages 697-713, October.
    12. Harnedi Maizir & Reni Suryanita & Hendra Jingga, 2016. "Estimation of Pile Bearing Capacity of Single Driven Pile in Sandy Soil Using Finite Element and Artificial Neural Network Methods," International Journal of Applied and Physical Sciences, Dr K.Vivehananthan, vol. 2(2), pages 45-50.
    13. K. C. Lam & Tiesong Hu & S. Thomas Ng & Martin Skitmore & S. O. Cheung, 2001. "A fuzzy neural network approach for contractor prequalification," Construction Management and Economics, Taylor & Francis Journals, vol. 19(2), pages 175-188.
    14. A. H. Boussabaine & A. P. Kaka, 1998. "A neural networks approach for cost flow forecasting," Construction Management and Economics, Taylor & Francis Journals, vol. 16(4), pages 471-479.
    15. Nivine Attoue & Isam Shahrour & Rafic Younes, 2018. "Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting," Energies, MDPI, vol. 11(2), pages 1-12, February.
    16. Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
    17. Giuseppina Nicolosi & Roberto Volpe & Antonio Messineo, 2017. "An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse," Energies, MDPI, vol. 10(5), pages 1-17, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xin J. Ge & G. Runeson, 2004. "Modeling Property Prices Using Neural Network Model for Hong Kong," International Real Estate Review, Global Social Science Institute, vol. 7(1), pages 121-138.
    2. Ciardiello, Adriana & Rosso, Federica & Dell'Olmo, Jacopo & Ciancio, Virgilio & Ferrero, Marco & Salata, Ferdinando, 2020. "Multi-objective approach to the optimization of shape and envelope in building energy design," Applied Energy, Elsevier, vol. 280(C).
    3. Najda Hayajneh & Taghrid Suifan & Bader Yousef Obeidat & Mohammd Abuhashesh & Raed Kareem Kanaan, 2020. "The relationship between organizational changes and job satisfaction in the Jordanian telecommunication industry," Journal of Social Sciences (COES&RJ-JSS), , vol. 9(1), pages 1-20, January.
    4. Vitaliy Roud & Thomas Wolfgang Thurner, 2018. "The Influence of State‐Ownership on Eco‐Innovations in Russian Manufacturing Firms," Journal of Industrial Ecology, Yale University, vol. 22(5), pages 1213-1227, October.
    5. Venugopal Gopalakrishna-Remani & Robert Paul Jones & Kerri M. Camp, 2019. "Levels of EMR Adoption in U.S. Hospitals: An Empirical Examination of Absorptive Capacity, Institutional Pressures, Top Management Beliefs, and Participation," Information Systems Frontiers, Springer, vol. 21(6), pages 1325-1344, December.
    6. Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    7. Park, Hyun-Soo & Auh, Seigyoung & Maher, Amro A. & Singhapakdi, Anusorn, 2012. "Marketing's accountability and internal legitimacy: Implications for firm performance," Journal of Business Research, Elsevier, vol. 65(11), pages 1576-1582.
    8. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    9. Luis Alfonso Dau & Aya S. Chacar & Marjorie A. Lyles & Jiatao Li, 2022. "Informal institutions and international business: Toward an integrative research agenda," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(6), pages 985-1010, August.
    10. Giacomo Chiesa & Andrea Acquaviva & Mario Grosso & Lorenzo Bottaccioli & Maurizio Floridia & Edoardo Pristeri & Edoardo Maria Sanna, 2019. "Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation," Sustainability, MDPI, vol. 11(11), pages 1-30, May.
    11. Nurul Fazleen Mohd Zariman & Norshima Humaidi & Muhammad Hafiz Abd Rashid, 2023. "Mobile commerce applications service quality in enhancing customer loyalty intention: mediating role of customer satisfaction," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 649-663, December.
    12. Krarti, Moncef & Aldubyan, Mohammad, 2021. "Mitigation analysis of water consumption for power generation and air conditioning of residential buildings: Case study of Saudi Arabia," Applied Energy, Elsevier, vol. 290(C).
    13. Saurbayeva, Assemgul & Memon, Shazim Ali & Kim, Jong, 2023. "Integrated multi-stage sensitivity analysis and multi-objective optimization approach for PCM integrated residential buildings in different climate zones," Energy, Elsevier, vol. 278(PB).
    14. Qingbin Cui & Makarand Hastak & Daniel Halpin, 2010. "Systems analysis of project cash flow management strategies," Construction Management and Economics, Taylor & Francis Journals, vol. 28(4), pages 361-376.
    15. Rao, Hayagreeva, 2004. "Institutional activism in the early American automobile industry," Journal of Business Venturing, Elsevier, vol. 19(3), pages 359-384, May.
    16. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    17. Mukhopadhyay, Jhuma & Chakraborty, Indrani, 2017. "Foreign institutional investment, business groups and firm performance: Evidence from India," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 454-465.
    18. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
    19. Tzeng, Cheng-Hua, 2018. "How foreign knowledge spillovers by returnee managers occur at domestic firms: An institutional theory perspective," International Business Review, Elsevier, vol. 27(3), pages 625-641.
    20. Shih-Chang Hung & Yung-Ching Tseng, 2017. "Extending the LLL framework through an institution-based view: Acer as a dragon multinational," Asia Pacific Journal of Management, Springer, vol. 34(4), pages 799-821, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14738-:d:967103. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.