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Machine Learning Algorithm Application in the Construction Industry – A Review

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Advances in Information Technology in Civil and Building Engineering (ICCCBE 2022)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 357))

Abstract

Industries like manufacturing use Machine Learning (ML) algorithms to conceive and produce excellent consumer goods. This achievement has persuaded other economic sectors, including the construction sector, to attempt and incorporate intelligent algorithms. The most recent developments in ML algorithms have made it possible to automate those non-trivial jobs that were thought unsolvable years back. Early involvement of Construction researchers in the ML process is necessary to ensure that they have sufficient awareness of the advantages and disadvantages. It is worthy of note that construction organisations have concerns due to the peculiarity of the sector. As such, adopting machine learning (ML) for profitability predictions or cost-saving results can be challenging. Construction industry stakeholders are eager to discover how ML may help improve operations, and the benefits of ML algorithms, among others, before adopting these algorithms for decision-making. To assist construction industry stakeholders in the adoption of ML algorithms, the study adopted a systematic literature review. The study helps in the proper identification of the uses of ML algorithms to improve the construction industry processes and product.

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References

  1. Adekunle SA, Aigbavboa C, Ejohwomu OA (2022) SCAN TO BIM: a systematic literature review network analysis. IOP Conf Ser Mater Sci Eng 1218(1):012057. https://doi.org/10.1088/1757-899x/1218/1/012057

    Article  Google Scholar 

  2. Adekunle SA, Aigbavboa CO, Ejohwomu O, Adekunle EA, Thwala WD (2021) Digital transformation in the construction industry : a bibliometric review. J Eng Des Technol. https://doi.org/10.1108/JEDT-08-2021-0442

  3. Aghimien DO, Aigbavboa CO, Oke AE, Thwala WD (2019) Mapping out research focus for robotics and automation research in construction-related studies: a bibliometric approach. J Eng Des Technol. https://doi.org/10.1108/JEDT-09-2019-0237

  4. Aghimien EI, Aghimien LM, Petinrin OO, Aghimien DO (2020) High-performance computing for computational modelling in built environment-related studies – a scientometric review. J Eng Des Technol 19(5):1138–1157. https://doi.org/10.1108/JEDT-07-2020-0294

    Article  Google Scholar 

  5. Antwi-Afari MF, Li H, Yu Y, Kong L (2018) Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers. Autom Constr 96:433–441. https://doi.org/10.1016/J.AUTCON.2018.10.004

    Article  Google Scholar 

  6. Artificial intelligence applied to conceptual design. A review of its use in architecture - ScienceDirect (n.d.). https://www.sciencedirect.com/science/article/pii/S0926580521000017. Accessed 18 July 2022

  7. Ayhan BU, Tokdemir OB (2019) Predicting the outcome of construction incidents. Saf Sci 113:91–104. https://doi.org/10.1016/J.SSCI.2018.11.001

    Article  Google Scholar 

  8. Banaei M, Ahmadi A, Yazdanfar A (2017) Application of AI methods in the clustering of architecture interior forms. Front Archit Res 6(3):360–373. https://doi.org/10.1016/j.foar.2017.05.002

    Article  Google Scholar 

  9. Bau D, et al (2019) Visualising and understanding generative adversarial networks (extended abstract). http://arxiv.org/abs/1901.09887

  10. Bilal M, Oyedele LO (2020) Guidelines for applied machine learning in construction industry—a case of profit margins estimation. Adv Eng Inform 43:101013. https://doi.org/10.1016/J.AEI.2019.101013

    Article  Google Scholar 

  11. Bilal M, et al (2016) Big Data in the construction industry: a review of present status, opportunities, and future trends. Adv Eng Inform 30(3):500–521. https://doi.org/10.1016/j.aei.2016.07.001

    Article  Google Scholar 

  12. Chellappa V, Srivastava V, Salve UR (2021) A systematic review of construction workers’ health and safety research in India. J Eng Des Technol. https://doi.org/10.1108/JEDT-08-2020-0345

  13. Choi J, Gu B, Chin S, Lee JS (2020) Machine learning predictive model based on national data for fatal accidents of construction workers. Autom Constr 110:102974. https://doi.org/10.1016/J.AUTCON.2019.102974

    Article  Google Scholar 

  14. Cutler J, Dickenson M (2020) Introduction to machine learning with python, pp 129–142. https://doi.org/10.1007/978-3-030-36826-5_10

  15. De Los Reyes A, Kazdin AE (2008) When the evidence says, “yes, no, and maybe so”: attending to and interpreting inconsistent findings among evidence-based interventions. Curr Dir Psychol Sci 17(1):47–51

    Article  Google Scholar 

  16. Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. http://arxiv.org/abs/1702.08608

  17. Fan C, Sun Y, Zhao Y, Song M, Wang J (2019) Deep learning-based feature engineering methods for improved building energy prediction. Appl Energy 240:35–45

    Google Scholar 

  18. Geissdoerfer M, Savaget P, Bocken NMP, Hultink EJ (2017) The circular economy – a new sustainability paradigm? J Clean Prod 143:757–768. https://doi.org/10.1016/J.JCLEPRO.2016.12.048

    Article  Google Scholar 

  19. Géron A (2017) Hands-on machine learning with scikit-learn, keras, and tensorflow (2019, O’reilly). Hands-on machine learning with R, p 510

    Google Scholar 

  20. Géron A (2019) Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems (2nd edn). O’Reilly

    Google Scholar 

  21. Gomber P, Kauffman RJ, Parker C, Weber BW (2018) On the fintech revolution: interpreting the forces of innovation, disruption, and transformation in financial services. J Manag Inf Syst 35(1):220–265. https://doi.org/10.1080/07421222.2018.1440766

    Article  Google Scholar 

  22. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier

    Google Scholar 

  23. Inza I, Larranaga P, Blanco R, Cerrolaza AJ (2004) Filter versus wrapper gene selection approaches in DNA microarray domains. Artif Intell Med 31(2):91–103

    Google Scholar 

  24. Sarker IH, Kayes ASM, Watters P (2019) Effectiveness analysis of machine learning classification models for predicting personalised context-aware smartphone usage. J Big Data. 6(1):1–28

    Article  Google Scholar 

  25. Kang K, Ryu H (2019) Predicting types of occupational accidents at construction sites in Korea using random forest model. Saf Sci 120:226–236. https://doi.org/10.1016/J.SSCI.2019.06.034

    Article  Google Scholar 

  26. Kanyilmaz A, Tichell PRN, Loiacono D (2022) A genetic algorithm tool for conceptual structural design with cost and embodied carbon optimisation. Eng Appl Artif Intell 112:104711. https://doi.org/10.1016/j.engappai.2022.104711

    Article  Google Scholar 

  27. Koc K, Ekmekcioğlu Ö, Gurgun AP (2021) Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers. Autom Constr 131:103896. https://doi.org/10.1016/J.AUTCON.2021.103896

    Article  Google Scholar 

  28. Kusonkhum W, Srinavin K, Leungbootnak N, Aksorn P, Chaitongrat T (2022) Government construction project budget prediction using machine learning. https://doi.org/10.12720/jait.13.1.29-35

  29. Ligler H (2021) Reconfiguring atrium hotels: generating hybrid designs with visual computations in Shape Machine. Autom Constr 132:103923. https://doi.org/10.1016/j.autcon.2021.103923

    Article  Google Scholar 

  30. Liu T, Tan Z, Xu C, Chen H, Li Z (2020) Study on deep reinforcement learning techniques for building energy consumption forecasting. Energy Build 208:109675

    Google Scholar 

  31. Madubuike OC, Anumba CJ, Khallaf R (2022) A review of digital twin applications in construction. ITcon 27:145–172. https://doi.org/10.36680/j.itcon.2022.008

  32. Mehyar M, Rostamizadeh A, Talwalkar A (2018) Foundations of machine learning, 2nd edn. Massachusetts Institute of Technology All

    Google Scholar 

  33. Mohri M (n.d.) Foundations of machine learning. https://cs.nyu.edu/~mohri/mlbook/. Accessed 21 Aug 2022

  34. Mendelson EB (2019) Artificial intelligence in breast imaging: potentials and limitations. Am J Roentgenol 212(2):293–299. https://doi.org/10.2214/AJR.18.20532

    Article  Google Scholar 

  35. Mistikoglu G, Gerek IH, Erdis E, Mumtaz Usmen PE, Cakan H, Kazan EE (2015) Decision tree analysis of construction fall accidents involving roofers. Expert Syst Appl 42(4):2256–2263. https://doi.org/10.1016/J.ESWA.2014.10.009

    Article  Google Scholar 

  36. Mullainathan S, Spiess J (2017) Machine learning: An applied econometric approach. J Econ Perspect 31(2):87–106. https://doi.org/10.1257/JEP.31.2.87

    Article  Google Scholar 

  37. Najafi B, Depalo M, Rinaldi F, Arghandeh R (2021) Building characterization through smart meter data analytics: determination of the most influential temporal and importance-in-prediction based features. Energy Build 234:110671

    Google Scholar 

  38. Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2(3):1–21. https://doi.org/10.1007/S42979-021-00592-X/FIGURES/11

    Article  MathSciNet  Google Scholar 

  39. Shapiro A (2017) Reform predictive policing. Nature 541(7638):458–460. https://doi.org/10.1038/541458A

    Article  Google Scholar 

  40. Tan Y, Tang P, Zhou Y, Luo W, Kang Y, Li G (2017) Photograph aesthetical evaluation and classification with deep convolutional neural networks. Neurocomputing 228:165–175. https://doi.org/10.1016/j.neucom.2016.08.098

    Article  Google Scholar 

  41. Wang Z, Xia L, Yuan H, Srinivasan RS, Song X (2022) Principles, research status, and prospects of feature engineering for data-driven building energy prediction: a comprehensive review. J Build Eng:105028

    Google Scholar 

  42. Yang K, Ahn CR, Vuran MC, Aria SS (2016) Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit. Autom Constr 68:194–202. https://doi.org/10.1016/J.AUTCON.2016.04.007

    Article  Google Scholar 

  43. 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. https://doi.org/10.1016/J.AUTCON.2018.12.016

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Johannesburg, Johannesburg, South Africa

    Samuel Adeniyi Adekunle & Clinton Aigbavboa

  2. University of Florida, Gainesville, USA

    A. Onatayo Damilola & Obinna C. Madubuike

  3. University of Manchester, Manchester, UK

    Obuks Ejohwomu

Authors
  1. Samuel Adeniyi Adekunle
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  2. A. Onatayo Damilola
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  3. Obinna C. Madubuike
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  4. Clinton Aigbavboa
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  5. Obuks Ejohwomu
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Correspondence to Samuel Adeniyi Adekunle .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, University of Cape Town, Rondebosch, South Africa

    Sebastian Skatulla

  2. Department of Civil Engineering, University of Cape Town, Rondebosch, South Africa

    Hans Beushausen

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Adekunle, S.A., Onatayo Damilola, A., Madubuike, O.C., Aigbavboa, C., Ejohwomu, O. (2024). Machine Learning Algorithm Application in the Construction Industry – A Review. In: Skatulla, S., Beushausen, H. (eds) Advances in Information Technology in Civil and Building Engineering. ICCCBE 2022. Lecture Notes in Civil Engineering, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-031-35399-4_21

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  • DOI: https://doi.org/10.1007/978-3-031-35399-4_21

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  • Print ISBN: 978-3-031-35398-7

  • Online ISBN: 978-3-031-35399-4

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