IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2023i1p182-d1309512.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/1/182/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/1/182/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    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. Yunlong Han & Conghui Li & Linfeng Zheng & Gang Lei & Li Li, 2023. "Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network," Energies, MDPI, vol. 16(17), pages 1-16, August.
    2. Gi-Wook Cha & Se-Hyu Choi & Won-Hwa Hong & Choon-Wook Park, 2022. "Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas," IJERPH, MDPI, vol. 20(1), pages 1-17, December.
    3. Gi-Wook Cha & Won-Hwa Hong & Young-Chan Kim, 2023. "Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    4. Yuxin, Wang & Gui, Fu & Qian, Lyu & Jingru, Wu & Yali, Wu & Meng, Han & Yuxuan, Lu & Xuecai, Xie, 2024. "Accident case-driven study on the causal modeling and prevention strategies of coal-mine gas-explosion accidents: A systematic analysis of coal-mine accidents in China," Resources Policy, Elsevier, vol. 88(C).
    5. Gi-Wook Cha & Hyeun-Jun Moon & Young-Chan Kim, 2021. "Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables," IJERPH, MDPI, vol. 18(16), pages 1-16, August.
    6. Gi-Wook Cha & Won-Hwa Hong & Se-Hyu Choi & Young-Chan Kim, 2023. "Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    7. Gi-Wook Cha & Se-Hyu Choi & Won-Hwa Hong & Choon-Wook Park, 2023. "Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis," IJERPH, MDPI, vol. 20(4), pages 1-15, February.

    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:jeners:v:17:y:2023:i:1:p:182-:d:1309512. 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.