Application of Artificial Intelligence in Construction Project Management in Nigeria: Assessing Its Effect on Cost, Time, Risk, and Project Performance
DOI:
https://doi.org/10.61424/rjbe.v1i1.1045Keywords:
Artificial intelligence, construction project management, cost performance, time performance, risk management, project performance, NigeriaAbstract
The construction industry in Nigeria is experiencing several problems that are associated with cost overruns, delays, risk, and poor project performance. The progressive use of artificial intelligence (AI) offers an opportunity to advance construction project management through predictive analysis, cost estimation, delay forecasting, risk management, and decision support. This research explores the application of artificial intelligence in construction project management in Nigeria with regard to its impact on cost, time, risk, and project performance. A quantitative research method was utilized to gather the required data using a structured questionnaire administered to construction practitioners. The data collected were analyzed using descriptive statistics, correlation analysis, and regression analysis to establish the correlation existing between AI Application and the variables of project performance mentioned above. The results obtained reveal that AI Application has a positive impact on cost performance, time performance, risk management, and overall project performance. The study further shows that artificial intelligence can augment effective support for cost prediction, early delay detection, and accurate risk assessment, leading to better project decision-making. The implementation of AI applications is, however, subject to several constraints such as data accessibility, technological infrastructure, professional skills, and organizational problems. The analysis reveals that there is a very valuable opportunity for improving construction project management in Nigeria and recommends greater investment in AI technologies, digital infrastructure, professional training, and reliable construction databases to support effective adoption.
References
Adeleke, A. Q., Bahaudin, A. Y., & Kamaruddeen, A. M. (2015). Level of risk management practice in Nigeria construction industry: From a knowledge-based approach. Journal of Management Marketing and Logistics, 2(1). https://doi.org/10.17261/Pressacademia.2015111601
Agumalu, S. A. (2022). A predictive cost-management and benefits realization model for international projects using earned value management (EVM) to reduce cost overruns and improve project outcomes. International Journal of Research Publications in Engineering, Technology and Management, 5(2), 6517–6527. https://doi.org/10.15662/IJRPETM.2022.0502005
Ahmed, S., & El-Sayegh, S. (2020). Critical review of the evolution of project delivery methods in the construction industry. Buildings, 11(1), 11. https://doi.org/10.3390/buildings11010011
Amusan, L. M. (2019). Neural network and probability-based cost expectation limit model for residential building cost. Proceedings of International Structural Engineering and Construction. https://doi.org/10.14455/ISEC.RES.2019.119
Amusan, L. M., Opeyemi, J., Ebuoluwa, A., & Omuh, I. (2019). Cost expectation limit model for residential building cost. In Proceedings of the International Structural Engineering and Construction Conference.
Bala, K., Bustani, S. A., & Chindo, S. S. (2014). A computer-based cost prediction model for institutional building projects in Nigeria: An artificial neural network approach. Journal of Engineering, Design and Technology, 12(4), 519–530. https://doi.org/10.1108/JEDT-06-2012-0026
Daniel, C. O. (2019). Project failure and its influence on the performance of construction firms in Nigeria.
Egwim, C. N., Alaka, H., Toriola-Coker, L. O., Balogun, H., Ajayi, S., & Oseghale, R. (2021). Extraction of underlying factors causing construction projects delay in Nigeria. Journal of Engineering, Design and Technology, 21(5), 1323–1342. https://doi.org/10.1108/JEDT-04-2021-0211
Egwim, C. N., Alaka, H., Toriola-Coker, L. O., Balogun, H., & Sunmola, F. (2021). Applied artificial intelligence for predicting construction projects delay. Machine Learning with Applications, 6, 100166. https://doi.org/10.1016/j.mlwa.2021.100166
Fang, Y., & Ng, S. T. (2019). Genetic algorithm for determining the construction logistics of precast components. Engineering, Construction and Architectural Management, 26(10), 2289–2306.
Lekan, A., Clinton, A., Stella, E., Moses, E., & Biodun, O. (2022). Construction 4.0 application: Industry 4.0, Internet of Things and lean construction tools’ application in quality management system of residential building projects. Buildings, 12(10), 1557.
Liu, K. S., & Lin, M. H. (2021). Performance assessment on the application of artificial intelligence to sustainable supply chain management in the construction material industry. Sustainability, 13(22), 12767. https://doi.org/10.3390/su132212767
Musa, M. A., & Akanbi, T. (2022). Integrating deep learning with web-based price analysis to support cost estimation. In Proceedings of the 9th International Conference on Construction Engineering and Project Management (ICCEPM 2022).
Oke, A. A. (2018). Development of an artificial neural network model for predicting the impact of risk on cost of building projects [Doctoral dissertation, Federal University of Technology, Minna]. FUT Minna Institutional Repository.
Oke, A. E., Kineber, A. F., Albukhari, I., Othman, I., & Kingsley, C. (2021). Assessment of cloud computing success factors for sustainable construction industry: The case of Nigeria. Buildings, 11(2), 36.
Okereke, R. A., Pepple, D. I., & Ihekweme, N. M. (2022). Assessment of the major contractors’ selection criteria and their impacts in civil engineering construction projects. ITEGAM-JETIA, 8(36), 4–13.
Omotayo, T., Bankole, A., & Olubunmi Olanipekun, A. (2020). An artificial neural network approach to predicting most applicable post-contract cost controlling techniques in construction projects. Applied Sciences, 10(15), 5171. https://doi.org/10.3390/app10155171
Onifade, M. K., & Oroye, O. A. (2020). Application of artificial neural networks technique to project management and control: A pilot assessment of some firms in Lagos, Nigeria. Journal of Science and Technology Research, 2(1), 40–46.
Quốc, Đ. T. (2021). Research trends on machine learning in construction management: A scientometric analysis. Journal of Applied Science and Technology Trends.
Ujene, A. O., & Umoh, A. A. (2017). Utilizing site characteristics in neural network modelling of percentage cost-time overrun of building projects. Journal of Civil Engineering, Science and Technology, 8(1), 1–15. https://doi.org/10.33736/jcest.374.2017
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