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Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle

Author

Listed:
  • Christian Nnaemeka Egwim

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Hafiz Alaka

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Eren Demir

    (Decision Sciences Business Analysis and Statistics Group, Hertfordshire Business School, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Habeeb Balogun

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Razak Olu-Ajayi

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Ismail Sulaimon

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Godoyon Wusu

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Wasiu Yusuf

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Adegoke A. Muideen

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

Abstract

In recent years, there has been a surge in the global digitization of corporate processes and concepts such as digital technology development which is growing at such a quick pace that the construction industry is struggling to catch up with latest developments. A formidable digital technology, artificial intelligence (AI), is recognized as an essential element within the paradigm of digital transformation, having been widely adopted across different industries. Also, AI is anticipated to open a slew of new possibilities for how construction projects are designed and built. To obtain a better knowledge of the trend and trajectory of research concerning AI technology application in the construction industry, this research presents an exhaustive systematic review of seventy articles toward AI applicability to the entire lifecycle of the construction value chain identified via the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The review’s findings show foremostly that AI technologies are mostly used in facility management, creating a huge opportunity for the industry to profit by allowing facility managers to take proactive action. Secondly, it shows the potential for design expansion as a key benefit according to most of the selected literature. Finally, it found data augmentation as one of the quickest prospects for technical improvement. This knowledge will assist construction companies across the world in recognizing the efficiency and productivity advantages that AI technologies can provide while helping them make smarter technology investment decisions.

Suggested Citation

  • Christian Nnaemeka Egwim & Hafiz Alaka & Eren Demir & Habeeb Balogun & Razak Olu-Ajayi & Ismail Sulaimon & Godoyon Wusu & Wasiu Yusuf & Adegoke A. Muideen, 2023. "Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle," Energies, MDPI, vol. 17(1), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:182-:d:1309512
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    References listed on IDEAS

    as
    1. Anuoluwapo Ajayi & Lukumon Oyedele & Hakeem Owolabi & Olugbenga Akinade & Muhammad Bilal & Juan Manuel Davila Delgado & Lukman Akanbi, 2020. "Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects," Risk Analysis, John Wiley & Sons, vol. 40(10), pages 2019-2039, October.
    2. Amin Aghalari & Nazanin Morshedlou & Mohammad Marufuzzaman & Daniel Carruth, 2021. "Inverse reinforcement learning to assess safety of a workplace under an active shooter incident," IISE Transactions, Taylor & Francis Journals, vol. 53(12), pages 1337-1350, December.
    3. Gi-Wook Cha & Hyeun Jun Moon & Young-Min Kim & Won-Hwa Hong & Jung-Ha Hwang & Won-Jun Park & Young-Chan Kim, 2020. "Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets," IJERPH, MDPI, vol. 17(19), pages 1-15, September.
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