Predicting the schedule and cost performance in public school building projects in Taiwan
Abstract
The Ministry of Education (MOE) of Taiwan invests about NTD 30 billion a year in Public School Building Projects (PSBPs). However, 95% of the PSBPs have been extended and have incurred increased costs. A PSBP performance evaluation and prediction system was established by using the Fuzzy Delphi Method (FDM), association rules and an Artificial Neural Network (ANN). Sixty-two Taiwanese PSBPs were used as the samples, while eleven high correlation factors that influence the project performance of PSBPs were defined, and the reasons leading to the poor project performance were discussed in this study. Moreover, the results of the test cases operated by ANN showed that the accuracy rate for schedule and cost variability predictions can reach 84%. The high accuracy rate indicated the reliability of priority control for high-risk projects in the future. The proposed approach can be provided to clients, design and construction firms, and project managers to understand the project performance in real time and to establish a dynamic tracking review and response measures for improving the overall project satisfaction.
First published online 20 December 2021
Keyword : public school building projects (PSBPs), project performance, Fuzzy Delphi Method (FDM), association rules, Artificial Neural Network (ANN)
How to Cite
Juan, Y.-K., & Liou, L.-E. (2022). Predicting the schedule and cost performance in public school building projects in Taiwan. Journal of Civil Engineering and Management, 28(1), 51–67. https://doi.org/10.3846/jcem.2021.15853
This work is licensed under a Creative Commons Attribution 4.0 International License.
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https://doi.org/10.1061/(ASCE)0742-597X(2004)20:2(42)
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Fragkakis, N., Lambropoulos, S., & Pantouvakis, J.-P. (2010). A cost estimate method for bridge superstructures using regression analysis and bootstrap. Organization, Technology & Management in Construction: An International Journal, 2(2), 182–190.
Gudiene, N., Banaitis, A., Podvezko, V., & Banaitiene, N. (2014). Identification and evaluation of the critical success factors for construction pro-jects in Lithuania: AHP approach. Journal of Civil Engineering and Management, 20(3), 350–359. https://doi.org/10.3846/13923730.2014.914082
Gunduz, M., & Yahya, A. M. A. (2018). Analysis of project success factors in construction industry. Technological and Economic Development of Economy, 24(1), 67–80. https://doi.org/10.3846/20294913.2015.1074129
Hajdu, M., & Bokor, O. (2016). Sensitivity analysis in PERT networks: Does activity duration distribution matter? Automation in Construction, 65, 1–8. https://doi.org/10.1016/j.autcon.2016.01.003
Haponava, T., & Al-jibouri, S. (2012). Proposed system for measuring project performance using process-based key performance Indicators. Journal of Management in Engineering, 28(2), 140–149. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000078
Hardie, M., & Saha, S. (2009). Builders’ perceptions of lowest cost procurement and its impact on quality. The Australasian Journal of Construc-tion Economics and Building, 9(1), 1–8. https://doi.org/10.5130/AJCEB.v9i1.3009
Hola, B., & Schabowicz, K. (2010). Estimation of earthworks execution time cost by means of artificial neural networks. Automation in Construc-tion, 19(5), 570–579. https://doi.org/10.1016/j.autcon.2010.02.004
Huang, J., Koopialipoor, M., & Armaghani, D. J. (2020). A combination of fuzzy Delphi method and hybrid ANN based systems to forecast ground vibration resulting from blasting. Scientific Reports, 10, 19397. https://doi.org/10.1038/s41598-020-76569-2
Ibbs, W., Nguyen, L. D., & Simonian, L. (2011). Concurrent delays and apportionment of damages. Journal of Construction Engineering and Management, 137(2), 119–126. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000259
Jha, K. N., & Iyer, K. C. (2007). Commitment, coordination, competence and the iron triangle. International Journal of Project Management, 25(5), 527–540. https://doi.org/10.1016/j.ijproman.2006.11.009
Juan, Y. K., Hsu, Y. H., & Xie, X. (2017). Identifying customer behavioral factors and price premiums of green building purchasing. Industrial Marketing Management, 64, 36–43. https://doi.org/10.1016/j.indmarman.2017.03.004
Kamsu-Foguem, B., Rigal, F., & Mauget, F. (2013). Mining association rules for the quality improvement of the production process. Expert Sys-tems with Applications, 40(4), 1034–1045. https://doi.org/10.1016/j.eswa.2012.08.039
Kog, Y. C., & Loh, P. K. (2012). Critical success factors for different components of construction projects. Journal of Construction Engineering and Management, 138(4), 520–528. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000464
Leung, M. Y., Ng, S. T., & Cheung, S. O. (2004). Measuring construction project participant satisfaction. Construction Management and Eco-nomics, 22(3), 319–331. https://doi.org/10.1080/01446190320000000000
Li, D., Koopialipoor, M., & Armaghani, D. J. (2021). A combination of Fuzzy Delphi method and ANN-based models to investigate factors of flyrock induced by mine blasting. Natural Resources Research, 30, 1905–1924. https://doi.org/10.1007/s11053-020-09794-1
Ling, F. Y. Y., & Liu, M. (2004). Using neural network to predict performance of design-build projects in Singapore. Building and Environment, 39(10), 1263–1274. https://doi.org/10.1016/j.buildenv.2004.02.008
Lin, G., Shen, G., Sun, M., & Kelly, J. (2011). Identification of key performance indicators for measuring the performance of value management studies in construction. Journal of Construction Engineering and Management, 137(9), 698–706. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000348
Liu, B., Hsu, W., Chen, S., & Ma, Y. (2000). Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems and their Applications, 15(5), 47–55. https://doi.org/10.1109/5254.889106
Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. North-Holland.
Kuo, Y. F., & Chen, P. C. (2008). Constructing performance appraisal indicators for mobility of the service industries using Fuzzy Delphi Method. Expert Systems with Applications, 35(4), 1930–1939. https://doi.org/10.1016/j.eswa.2007.08.068
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