Artificial Intelligence Tools in Construction Management
Keywords:
artificial intelligence, construction project, resource allocation, technology acceptance and challenges, construction industryAbstract
The primary objective of this study is to examine modern applications of artificial intelligence (AI) in construction project management and to explore strategies for improving various processes within this industry. Given the increasing challenges and complexities in the construction sector, the introduction and evaluation of AI tools can contribute to enhancing project efficiency, reducing costs, improving forecasting accuracy, and accelerating construction processes. This research investigates these aspects and provides suggestions for optimizing project management processes through the utilization of these advanced technologies. The findings of this study can assist entities active in the construction industry in improving their project management procedures by leveraging AI-based tools, thereby completing projects with higher quality and reduced costs. The present study adopts a quantitative research design and is conducted as a descriptive and survey-based study. Data collection was carried out through library research and the compilation of information from documented and scientific sources. The statistical population of the study includes 384 project managers engaged in the residential construction sector. Data analysis was performed using SPSS version 22. The results of the study indicate that AI tools significantly contribute to improving resource allocation in construction projects. Moreover, the study highlights existing challenges related to the adoption of AI technologies within the construction industry, emphasizing the need for focused attention and resolution of these issues.
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References
Afzal, F., Yunfei, S., Nazir, M., & Bhatti, S. M. (2021). A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies. International Journal of Managing Projects in Business, 14(2), 300-328. https://doi.org/10.1108/IJMPB-02-2019-0047
Egwim, C. N., Alaka, Hafiz, Demir, Eren, Balogun, Habeeb, Olu-Ajayi, Razak, Sulaimon, Ismail, Wusu, Godoyon, Yusuf, Wasiu, Muideen, Adegoke A. (2023). Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle. Energies, 17(1), 182. https://doi.org/10.3390/en17010182
Ghosh, A. K., Ullah, A. S., & Kubo, A. (2019). Hidden Markov Model-Based Digital Twin Construction for Futuristic Manufacturing Systems. Artificial Intelligence for Engineering Design Analysis and Manufacturing, 33(03), 317-331. https://doi.org/10.1017/s089006041900012x
Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
Holzmann, V., Lechiara, Michele. (2022). Artificial Intelligence in Construction Projects: An Explorative Study of Professionals’ Expectations. European Journal of Business and Management Research, 7(3), 151-162. https://doi.org/10.24018/ejbmr.2022.7.3.1432
Kord, A. (2023). The impact of artificial intelligence application on project management performance. Proceedings of the 10th International Conference on Management, Economics, and Accounting, https://civilica.com/doc/1816647
Lam, K. C., & Oshodi, O. S. (2016). Using univariate models for construction output forecasting: Comparing artificial intelligence and econometric techniques. Journal of Management in Engineering, 32(6), 04016021. https://doi.org/https://doi.org/10.1061/(ASCE)ME.1943-5479.0000462
Liu, S. (2024). Integration of Artificial Intelligence in Building Construction Management: Optimizing Cost and Schedule. 525-532. https://doi.org/10.2991/978-94-6463-398-6_50
Rabbi, A. B. K., Jeelani, Idris. (2024). AI integration in construction safety: Current state, challenges, and future opportunities in text, vision, and audio based applications. Automation in Construction, 164, 105443. https://doi.org/10.1016/j.autcon.2024.105443
Rampini, L., Cecconi, Fulvio Re. (2022). Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities. Journal of Information Technology in Construction, 27(no), 884-913. https://doi.org/10.36680/j.itcon.2022.043
Razi, P. Z., Sulaiman, S. K, Ali, M. I, Ramli, N. I, Saad, M. S. H, Jamaludin, O, Doh, S. I. (2023). How Artificial Intelligence Changed the Construction Industry in Safety Issues. IOP Conference Series: Earth and Environmental Science, 1140(1), 012004. https://doi.org/10.1088/1755-1315/1140/1/012004
Takhmasib, M., Lee, Hyuk Jae, Yi, Hwang. (2023). Machine-learned kinetic Façade: Construction and artificial intelligence enabled predictive control for visual comfort. Automation in Construction, 156(no), 105093. https://doi.org/10.1016/j.autcon.2023.105093
Wachnik, B. (2022). Analysis of the Use of Artificial Intelligence in the Management of Industry 4.0 Projects. The Perspective of Polish Industry. Production Engineering Archives, 28(1), 56-63. https://doi.org/10.30657/pea.2022.28.07
Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Wang, J. (2022). Intelligent Decision Support System for Building Project Management Based on Artificial Intelligence. Journal of Physics Conference Series. https://doi.org/10.1088/1742-6596/2665/1/012022
Wang, X., & Liu, Z. (2022). Three-Dimensional Reconstruction of National Traditional Sports Cultural Heritage Based on Feature Clustering and Artificial Intelligence. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2022/8159045
Zia, M. T. (2024). The Role and Impact of Artificial Intelligence on Project Management. Abbdm, 4(02). https://doi.org/10.62019/abbdm.v4i02.160
