• Abstract

    The integration of Artificial Intelligence (AI) into the management and building sector has witnessed significant advancements in recent years. Traditional methods can fall short in meeting these demands, prompting the exploration of AI solutions. This review aims to identify existing gaps, assess the efficacy of current AI applications and provide insights into potential areas for further development. A comprehensive literature review was conducted, encompassing peer-reviewed articles, industry reports and case studies. The analysis focused on AI applications such as machine learning, robotics and data analytics in the context of project management, resource optimization along with sustainable building practices. The review identifies key AI applications in project planning, risk management and construction processes, demonstrating their potential to streamline operations and improve decision-making. The analysis reveals successful implementations of AI-driven technologies, highlighting their impact on cost reduction, time efficiency and sustainability practices. Additionally, emerging trends such as generative design and smart buildings indicate promising directions for future development. The integration of Artificial Intelligence in the management and building sector demonstrates substantial benefits in efficiency, cost reduction and sustainability, while ongoing research as well as adaptation to emerging technologies is crucial for sustained progress.

  • References

    1. Abioye S.O, Oyedele L.O, Akanbi L, Ajayi A, Delgado J.M.D, Bilal M, Akinade O.O and Ahmed A et al (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering ,44, p.103299. DOI: 10.1016/j.jobe.2021.103299
    2. Ali D and Frimpong S (2020). Artificial intelligence, machine learning and process automation: Existing knowledge frontier and way forward for mining sector. Artificial Intelligence Review, 53, pp.6025-6042. DOI:10.1007/s10462-020-09841-6
    3. Auger S.D, Jacobs B.M, Dobson R, Marshall C.R and Noyce A.J(2020). Big data, machine learning and artificial intelligence: a neurologist’s guide. Practical Neurology. DOI:10.1136/practneurol-2020-002688
    4. Alavipour S.R and Arditi D, (2019). Time-cost tradeoff analysis with minimized project financing cost. Automation in Construction, 98, pp.110-121. DOI: 10.1016/j.autcon.2018.09.009
    5. Benbya H, Davenport T.H and Pachidi S (2020). Artificial intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive,19(4). DOI:10.2139/ssrn.3741983
    6. Briganti G and Le Moine O, (2020). Artificial intelligence in medicine: today and tomorrow. Frontiers in medicine, 7, p.27. DOI:10.3389/fmed.2020.00027
    7. Chan H.M(2023). Impact of artificial intelligence (AI) system implementation in the construction industry: Case study of Klang valley (Doctoral dissertation, UTAR).
    8. Mohapatra A, Mohammed A.R and Panda S. Role of Artificial Intelligence in the Construction Industry–A Systematic Review. DOI: 10.17148/IJARCCE.2023.12205.
    9. Chen C,Zhu Z, Hammad A (2020). Automated excavators activity recognition and productivity analysis from construction site surveillance videos, Autom. Constr. 110 103045. DOI: 10.1016/j.autcon.2019.103045.
    10. Chiu M.C, Hwang GJ, Hsia LH and Shyu FM (2022). Artificial intelligence-supported art education: A deep learning-based system for promoting university students’ artwork appreciation and painting outcomes. Interactive Learning Environments, pp.1-19. DOI:10.1080/10494820.2022.2100426
    11. Chowdhary K and Chowdhary K.R(2020). Natural language processing. Fundamentals of artificial intelligence, pp.603-649. DOI:10.1007/978-81-322-3972-7_19
    12. Cheng M.Y, Kusoemo D & Gosno R.A (2020). Text mining-based construction site accident classification using hybrid supervised machine learning. Automation in Construction, 118, p.103265. DOI: 10.1016/j.autcon.2020.103265
    13. Deligia M, Congiu E, Marano G.C, Briseghella B & Fenu L (2021). Structural optimization of composite steel trussed-concrete beams. Procedia Structural Integrity, 33, 613-622. DOI: 10.1016/j.prostr.2021.10.068
    14. Dung C.V (2019), Autonomous concrete crack detection using deep fully convolutional neural network, Autom. Constr., 99, 52–58, DOI:10.1016/j. autcon.2018.11.028. DOI: 10.1016/j.autcon.2018.11.028
    15. Gao Y, Kong B & Mosalam K.M(2019). Deep leaf‐bootstrapping generative adversarial network for structural image data augmentation. Computer‐Aided Civil and Infrastructure Engineering, 34(9), 755-773. DOI:10.1111/mice.12458
    16. Hatami M, Flood I, Franz B & Zhang X (2019). State-of-the-art review on the applicability of AI methods to automated construction manufacturing. In ASCE International Conference on Computing in Civil Engineering, 368-375. Reston, VA: American Society of Civil Engineers.
    17. Kaul V, Enslin S & Gross S.A, (2020). History of artificial intelligence in medicine. Gastrointestinal endoscopy, 92(4), 807-812. DOI: 10.1016/j.gie.2020.06.040
    18. Kouhestani S & Nik-Bakht M (2020). IFC-based process mining for design authoring. Automation in Construction, 112, 103069. DOI:10.1016/j.autcon.2019.103069
    19. Leite M.L, De Loiola Costa L.S, Cunha V.A, Kreniski V, De Oliveira Braga Filho M, Da Cunha N.B & Costa FF et al (2021). Artificial intelligence and the future of life sciences. Drug Discovery Today26(11), 2515-2526. DOI: 10.1016/j.drudis.2021.07.002
    20. Mohamed M.A & Mohamad D (2021, May). The implementation of artificial intelligence (AI) in the Malaysia construction industry. In AIP Conference Proceedings, 2339(1). AIP Publishing. DOI:10.1063/5.0044597
    21. Pan Y & Zhang L (2020) BIM log mining: exploring design productivity characteristics, Autom. Constr. 109, 102997, DOI: 10.1016/j. autcon.2019.102997.
    22. Pan Y, Zhang L & Skibniewski M.J (2020). Clustering of designers based on building information modeling event logs, Comput. Aided Civ. Infrastruct. Eng., 35 (7), 701–718. DOI: 10.1111/mice.12551.
    23. Pan Y & Zhang L (2020) BIM log mining: learning and predicting design commands, Autom. Constr. 112, 103107, DOI:10.1016/j. autcon.2020.103107.
    24. Panchalingam R & Chan K.C (2021). A state-of-the-art review on artificial intelligence for Smart Buildings. Intelligent Buildings International,13(4), 203-226. DOI:10.1080/17508975.2019.1613219
    25. Paraskevoudis K, Karayannis P & Koumoulos E.P (2020). Real-time 3D printing remote defect detection (stringing) with computer vision and artificial intelligence. Processes, 8(11), 1464. DOI:10.3390/pr8111464
    26. Patil G (2019). Applications of artificial intelligence in construction management. International Journal of Research in Engineering, 32(03), 32-1541.
    27. Regona M, Yigitcanlar T, Xia B & Li R.Y.M(2022). Opportunities and adoption challenges of AI in the construction industry: a PRISMA review. Journal of Open Innovation: Technology, Market, and Complexity,8(1), 45. DOI: 10.1016/j.jobe.2021.103299
    28. Sarker I.H (2022). Ai-based modeling: Techniques, applications and research issues toward automation, intelligent and smart systems. SN Computer Science, 3(2), 158. DOI:10.1007/s42979-022-01043-x
    29. Schia M.H (2019). The introduction of AI in the construction industry and its impact on human behavior. DOI:10.24928/2019/0191.
    30. Schober K.S (2020). Artificial intelligence in the construction industry. Roland Berger. Roland Berger GmbH.
    31. Son H, Choi H, Seong H & Kim C (2019), Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks, Autom. Constr., 99, 27–38, DOI: 10.1016/j. autcon.2018.11.033. DOI: 10.1016/j.autcon.2018.11.033
    32. Yigitcanlar T & Desouza K.C (2020), Butler L and Roozkhosh F. Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), 1473. DOI:10.3390/en13061473
    33. Zhang C, Chang C.C & Jamshidi M (2020). Concrete bridge surface damage detection using a single‐stage detector. Computer‐Aided Civil and Infrastructure Engineering, 35(4), 389-409. DOI:10.1111/mice.12500
    34. Zhang F, Fleyeh H, Wang X & Lu M (2019), Construction site accident analysis using text mining and natural language processing techniques, Autom. Constr. 99, 238–248. DOI: 10.1016/j.autcon.2018.12.016
    35. Zhang F (2022). A hybrid structured deep neural network with Word2Vec for construction accident causes classification. International Journal of Construction Management, 22(6), 1120-1140. DOI: 10.1080/15623599.2019.1683692

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2023 Malque Publishing

How to cite

Mishra, A., Pareek, R. K., Kumar, S., & Varalakshmi, S. (2024). A review of the current and future developments of artificial intelligence in the management and building sectors. Multidisciplinary Reviews, 6, 2023ss068. https://doi.org/10.31893/multirev.2023ss068
  • Article viewed - 22
  • PDF downloaded - 7