Abstract
Cost and time overruns are currently posing a worldwide challenge to completing construction projects. Previous research looked at the factors that contributed to schedule and expense overruns to find a solution. Machine learning (ML) strategies have been successfully applied in a wide range of research fields to extract novel and important information from data. These strategies, however, have only recently been implemented in the construction industry. The goal of this research is to build a model capable of predicting project cost and time overruns using an appropriate data analysis approach and cost overrun elements as predictors. The specific goal of this research is to: After reviewing the relevant research, a number of risk indicators that are easily measurable and analysable in building projects were discovered. Delays or cost overruns, as well as the identification of the causes of these problems, as well as solutions that address the difficulty of predicting the values A case study was conducted to validate the model using an actual data set consisting of completed projects. The final model is simple, easy to understand, and quite accurate (83.76% in the KNN model and 99.28% in the ANN model), and it employs three data mining processes: clustering, feature selection, and prediction. These stages result in improved model performance.
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References
Abdul-Rahman, H., Berawi, M. A., Berawi, A. R., Mohamed, O., Othman, M., & Yahya, I. A. (2006). Delay mitigation in the Malaysian construction industry. Journal of Construction Engineering and Management, 132(2), 125–133.
Ahiaga-Dagbui, D. D., & Smith, S. D. (2014). Dealing with construction cost overruns using data mining. Construction Management and Economics, 32(7–8), 682–694.
Akoa, B. B. (2011). Cost overruns and time delays in highway and bridge projects in developing countries: Experiences from Cameroon. Michigan State University.
Al-Kodmany, K. (2018). The sustainability of tall building developments: A conceptual framework. Buildings, 8(1), 7.
AlSehaimi, A., Koskela, L., & Tzortzopoulos, P. (2013). Need for alternative research approaches in construction management: Case of delay studies. Journal of Management in Engineering, 29(4), 407–413.
Al-Tawal, D., Arafah, M., & Sweis, G. (2020). A model utilizing the artificial neural network in cost estimation of construction projects in Jordan. Engineering, Construction and Architectural Management, 28(9), 2466–2488. https://doi.org/10.1108/ecam-06-2020-0402
Alzara, M., Kashiwagi, J., Kashiwagi, D., & Al-Tassan, A. (2016). Using PIPS to minimize causes of delay in Saudi Arabian construction projects: University case study. Procedia Engineering, 145, 932–939.
An, S. H., Park, U. Y., Kang, K. I., Cho, M. Y., & Cho, H. H. (2007). Application of support vector machines in assessing conceptual cost estimates. Journal of Computing in Civil Engineering, 21(4), 259–264.
Arcila, S. G. (2012). Avoiding cost overruns in construction projects in the United Kingdom. Nature, 362(6420), 486–486.
Ballesteros-Perez, P., Sanz-Ablanedo, E., Soetanto, R., Gonzalez-Cruz, M. C., Larsen, G. D., & Cerezo-Narvaez, A. (2020). On the duration and cost variability of construction activities: An empirical study. Journal of Construction Engineering and Management, 146(1), 04019093.
Bayat, P., Monjezi, M., Rezakhah, M., & Armaghani, D. J. (2020). Artificial neural network and firefly algorithm for estimation and minimization of ground vibration induced by blasting in a mine. Natural Resources Research, 29(6), 4121–4132.
Bokaba, T., Doorsamy, W., & Paul, B. (2022). Comparative study of machine learning classifiers for modelling road traffic accidents. Applied Sciences, 12(2), 828. https://doi.org/10.3390/app12020828
Btoush, M., & Harun, A. (2017). Minimizing delays in the Jordanian construction industry by adopting BIM technology. IOP Conference Series: Materials Science and Engineering, 271, 012041. https://doi.org/10.1088/1757-899x/271/1/012041
Buakum, D., & Wisittipanich, W. (2020). Stochastic internal task scheduling in cross docking using chance-constrained programming. International Journal of Management Science and Engineering Management, 15(4), 258–264.
Budayan, C. et al. (2018) ‘A computerized method for delay risk assessment based on fuzzy set theory using MS ProjectTM’, KSCE Journal of Civil Engineering. Korean Society of Civil Engineers, pp. 1–12.
Ebtehaj, I., & Bonakdari, H. (2016a). Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms. KSCE Journal of Civil Engineering, 20(2), 581–589.
Ebtehaj, I., & Bonakdari, H. (2016b). A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes. Water Science and Technology, 73(9), 2244–2250.
El-Kholy, A. M. (2015). Predicting cost overrun in construction projects. International Journal of Construction Engineering and Management, 4(4), 95–105.
Forbes, L. H., & Ahmed, S. M. (2010). Modern construction: Lean project delivery and integrated practices. CRC Press.
Ghazal, M., & Hammad, A. (2020). Application of knowledge discovery in database (KDD) techniques in cost overrun of construction projects. International Journal of Construction Management, 22(9), 1632–1646. https://doi.org/10.1080/15623599.2020.1738205
Ghosh, M., Kabir, G., & Hasin, M. A. A. (2017). Project time–cost trade-off: A Bayesian approach to update project time and cost estimates. International Journal of Management Science and Engineering Management, 12(3), 206–215.
Gunduz, M., Nielsen, Y., & Ozdemir, M. (2015). Fuzzy assessment model to estimate the probability of delay in Turkish construction projects. Journal of Management in Engineering, 31(4), 04014055.
Hammad, A., AbouRizk, S., & Mohamed, Y. (2014). Application of KDD techniques to extract useful knowledge from labor resources data in industrial construction projects. Journal of Management in Engineering. https://doi.org/10.1061/(asce)me.1943-5479.0000280
Hammad, A. A. A., Ali, S. M. A., Sweis, G. J., & Bashir, A. (2008). Prediction model for construction cost and duration in Jordan. Jordan Journal of Civil Engineering, 2(3), 250–266.
Hegazy, T., & Ayed, A. (1998). Neural network model for parametric cost estimation of highway projects. Journal of Construction Engineering and Management, 124(3), 210–218.
Kaliba, C., Muya, M., & Mumba, K. (2009). Cost escalation and scheduled delays in road construction projects in Zambia. International Journal of Project Management, 27, 522–531.
Kamel, G., Aly, M. F., Mohib, A., & Afefy, I. H. (2020). Optimization of a multilevel integrated preventive maintenance scheduling mathematical model using genetic algorithm. International Journal of Management Science and Engineering Management, 15(4), 247–257.
Kaveh, A., Gholipour, Y., & Rahami, H. (2008). Optimal design of transmission towers using genetic algorithm and Neural Networks. International Journal of Space Structures, 23(1), 1–19. https://doi.org/10.1260/026635108785342073
Kaveh, A., & Seddighian, M. R. (2020). Domain decomposition of finite element models utilizing eight meta-heuristic algorithms: A comparative study. Mechanics Based Design of Structures and Machines, 50(8), 2616–2634. https://doi.org/10.1080/15397734.2020.1781655
Kaveh, A., & Servati, H. (2001). Design of double layer grids using backpropagation neural networks. Computers & Structures, 79(17), 1561–1568. https://doi.org/10.1016/s0045-7949(01)00034-7
Khair, K., Mohamed, Z., Mohammad, R., Farouk, H., & Ahmed, M. E. (2018). A management framework to reduce delays in road construction projects in Sudan. Arabian Journal for Science and Engineering, 43(4), 1925–1940.
Kotb, M. H. A., El Beheiry, H. S., & Kafafi, E. A. S. M. (2017). Guidelines for Delay Control in Construction Projects.
Lekan, A. (2011). Neural network-based cost predictive model for building works [Thesis (PhD)]. Covenant University.
Lim, C. S., Mohamad, E. T., Motahari, M. R., Armaghani, D. J., & Saad, R. (2020). Machine learning classifiers for modeling soil characteristics by geophysics investigations: A comparative study. Applied Sciences, 10(17), 5734.
Lowe, D. J., Emsley, M. W., & Harding, A. (2006). Predicting construction cost using multiple regression techniques. Journal of Construction Engineering and Management, 132(7), 750–758.
Mahamid, I. (2011). Early cost estimating for road construction projects using multiple regression techniques. Construction Economics and Building, 11(4), 87–101.
Mahamid, I., & Bruland, A. (2012). Cost deviation in road construction projects: The case of Palestine. Australasian Journal of Construction Economics and Building, the, 12(1), 58–71.
Mansfield, N. R., Ugwu, O. O., & Doran, T. (1994). Causes of delay and cost overruns in Nigerian construction projects. International Journal of Project Management, 12(4), 254–260.
Meeampol, S., & Ogunlana, S. O. (2006). Factors affecting cost and time performance on highway construction projects: Evidence from Thailand. Journal of Financial Management of Property and Construction, 11(1), 3–20.
Nasir, A. R., Gabriel, H. F., & Choudhry, R. M. (2011). Cost and time overruns in highway projects of Pakistan. In Sixth International Conference on Construction in the 21st Century, Kuala Lumpur, Malaysia (pp. 69–76).
Oesterreich, T. D., & Teuteberg, F. (2016). Understanding the implications of digitization and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121–139.
Pan, W., & Pan, M. (2022). Rethinking lean synergistically in practice for construction industry improvements. Engineering, Construction and Architectural Management. https://doi.org/10.1108/ecam-04-2021-0346
Rao, P. B., & Joseph Camron, C. (2014). Causes of delays in construction projects-A case study. International Journal of Current Research, 6(6), 7219–7222.
Ray, D. (2019). Breakthrough: how AI and machine learning could transform construction. https://www.building.co.Uk/focus/break-through-how-ai-and-machine-learning-could-transform-construction/5097559.article. Accessed 25 Mar 2021.
Sanni-Anibire, M., Mohamad Zin, R., & Olatunji, S. (2021). Developing a preliminary cost estimation model for tall buildings based on machine learning. International Journal of Management Science and Engineering Management, 16(2), 134–142. https://doi.org/10.1080/17509653.2021.1905568
Sharafi, H., Ebtehaj, I., Bonakdari, H., & Zaji, A. H. (2016). Design of a support vector machine with different kernel functions to predict scour depth around bridge piers. Natural Hazards, 84(3), 2145–2162.
Sharma, S., & Gupta, A. (2021). Analysis of Factors Affecting Cost and Time Overruns in Construction Projects. Lecture Notes in Civil Engineering (pp. 55–63). Springer. https://doi.org/10.1007/978-981-33-6969-6_6
Trauner, T. (2009) Construction delays: Understanding them clearly, analyzing them correctly.
Ujong, J., Mbadike, E., & Alaneme, G. (2022). Prediction of cost and duration of building construction using artificial neural network. Asian Journal of Civil Engineering, 23(7), 1117–1139. https://doi.org/10.1007/s42107-022-00474-4
Yeung, D., & Skitmore, M. (2012). A method for systematically pooling data in very early stage construction price forecasting. Construction Management and Economics, 30(11), 929–939.
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Ahmad Arabiat, Hamza Al-Bdour and Majdi Bisharah wrote the main manuscript text. All authors reviewed the manuscript.
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Arabiat, A., Al-Bdour, H. & Bisharah, M. Predicting the construction projects time and cost overruns using K-nearest neighbor and artificial neural network: a case study from Jordan. Asian J Civ Eng 24, 2405–2414 (2023). https://doi.org/10.1007/s42107-023-00649-7
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DOI: https://doi.org/10.1007/s42107-023-00649-7