Share:


Predicting the schedule and cost performance in public school building projects in Taiwan

    Yi-Kai Juan   Affiliation
    ; Ling-Er Liou Affiliation

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
Published in Issue
Jan 11, 2022
Abstract Views
1286
PDF Downloads
793
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Adeli, H., & Wu, M. (1998). Regularization neural network for construction cost estimation. Journal of Construction Engineering and Manage-ment, 124(4), 18–24. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:1(18)

Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In ACM SIGMOD Interna-tional Conference on Management of Data (pp. 207–216), Washington D.C., USA. https://doi.org/10.1145/170036.170072

Ahadzie, D. K., Proverbs, D. G., & Olomolaiye, P. O. (2008). Critical success criteria for mass house building projects in developing countries. International Journal of Project Management, 26(6), 675–687. https://doi.org/10.1016/j.ijproman.2007.09.006

Alaloul, W. S., Lieew, M. S., Zawawi, N. A. W., Mohammed, B. S., & Adamu, M. (2018). An Artificial neural networks (ANN) model for evalu-ating construction project performance based on coordination factors. Cogent Engineering, 5(1), 1507657. https://doi.org/10.1080/23311916.2018.1507657

Al-Momani, A. H. (1996). Construction cost prediction for public school buildings in Jordan. Construction Management and Economics, 14(4), 311–317. https://doi.org/10.1080/014461996373386

Alzahrani, J. I., & Emsley, M. W. (2013). The impact of contractors’ attributes on construction project success: a post construction evaluation. International Journal of Project Management, 31, 313–322. https://doi.org/10.1016/j.ijproman.2012.06.006

Aretoulis, G. N., Angelides, D. C., Kalfakakou, G. P., Fotiadis, G. S., & Anastasiadis, K. I. (2006). A prototype system for the prediction of final cost in construction projects. International Journal of Operational Research, 6(3), 423–432. https://doi.org/10.1007/BF02941260

Aretoulis, G. N. (2019). Neural network models for actual cost prediction in Greek public highway projects. International Journal of Project Or-ganisation and Management, 11(1), 41–64. https://doi.org/10.1504/IJPOM.2019.098712

Armaghani, D. J., Mohamad, E. T., Narayanasamy, M.S., Narita, N., & Yagiz, S. (2017). Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunnelling and Underground Space Technology, 63, 29–43. https://doi.org/10.1016/j.tust.2016.12.009

Baccarini, D. (1999). The logical framework method for defining project success. Project Management Journal, 30(4), 25–32. https://doi.org/10.1177/875697289903000405

Bagaya, O., & Song, J. (2016). Empirical study of factors influencing schedule delays of public construction projects in Burkina Faso. Journal of Management in Engineering, 32(5), 05016014. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000443

Barraaza, G. A., Back, W. E., & Mata, F. (2000). Probabilistic monitoring of project performance using SS-curves. Journal of Construction Engi-neering and Management, 126(2), 142–148. https://doi.org/10.1061/(ASCE)0733-9364(2000)126:2(142)

Bassioni, H., Price, A., & Hassan, T. (2004). Performance measurement in construction. Journal of Management in Engineering, 20(2), 42–50.
https://doi.org/10.1061/(ASCE)0742-597X(2004)20:2(42)

Bryde, D. J., & Wright, G. H. (2007). Project management priorities and the link with performance management systems. Project Management Journal, 38(4), 5–11. https://doi.org/10.1002/pmj.20014

Chan, A. P. C., Scott, D., & Chan, A. P. L. (2004). Factors affecting the success of a construction project. Journal of Construction Engineering and Management, 130(1), 153–155. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:1(153)

Chen, W. T., & Huang, Y. H. (2006). Approximately predicting the cost and duration of school reconstruction projects in Taiwan. Construction Management and Economics, 24(12), 1231–1239. https://doi.org/10.1080/01446190600953805

Cristóbal, J. R. S. (2017). The S-curve envelope as a tool for monitoring and control of projects. Procedia Computer Science, 121, 756–761. https://doi.org/10.1016/j.procs.2017.11.097

Doloi, H., Sawhney, A., Iyer, K. C., & Rentala, S. (2012). Analysing factors affecting delays in Indian construction projects. International Journal of Project Management, 30(4), 479–489. https://doi.org/10.1016/j.ijproman.2011.10.004

Enshassi, A., Mohamed, S., & Abushaban, S. (2008). Factors affecting the performance of construction projects in the Gaza strip. Journal of Civil Engineering and Management, 15(3), 269–280. https://doi.org/10.3846/1392-3730.2009.15.269-280

Faridi, A., & El-Sayegh, S. (2006). Significant factors causing delay in the UAE construction industry. Construction Management and Economics, 24(11), 1167–1176. https://doi.org/10.1080/01446190600827033

Flanagan, R., Lu, W., Shen, L., & Jewell, C. (2006). Competitiveness in construction: a critical review of research. Construction Management and Economics, 25(9), 989–1000. https://doi.org/10.1080/01446190701258039

Fleming, Q. W., & Koppelman, J. M. (2010). Earned value project management (4th ed.). Project Management Institute.

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

Mansingh, G., Osei-Bryson, K. M., & Reichgelt, H. (2011). Using ontologies to facilitate post-processing of association rules by domain experts. Information Science, 181(3), 419–434. https://doi.org/10.1016/j.ins.2010.09.027

Mitkus, S., & Trinkūnienė, E. (2008). Reasoned decisions in construction contracts evaluation. Technological and Economic Development of Economy, 14(3), 402–416. https://doi.org/10.3846/1392-8619.2008.14.402-416

Mohamad, E. T., Armaghani, D. J., Momeni, E., & Abad, S. V. A. N. K. (2014). Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bulletin of Engineering Geology and the Environment, 74, 745–757. https://doi.org/10.1007/s10064-014-0638-0

Murray, T. J., Pipino, L. L., & van Gigch, J. P. (1985). A pilot study of fuzzy set modification of Delphi. Human Systems Management, 5(1), 76–80. https://doi.org/10.3233/HSM-1985-5111

National Development Council. (2020). Overview of the project implementation of Ministry of Education. https://join.gov.tw/acts/gpivp/6223bf3f-206e-425a-9872-b1f2a9ad4281/index

Nassar, N., & AbouRizk, S. (2014). Practical application for integrated performance measurement of construction projects. Journal of Management in Engineering, 30(6), 04014027. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000287

Olafsson, S., Li, X., & Wu, S. (2008). Operations research and data mining. European Journal of Operational Research, 187(3), 1429–1448. https://doi.org/10.1016/j.ejor.2006.09.023

Patanakul, P., Kwak, Y.H., Zwikael, O., & Liu, M. (2016). What impacts the performance of large-scale government projects? International Jour-nal of Project Management, 34(3), 452–466. https://doi.org/10.1016/j.ijproman.2015.12.001

Perng, Y. H., Juan, Y. K., & Chien, S. F. (2006). Exploring the bidding situation for economically most advantageous tender projects using a bid-ding game. Journal of Construction Engineering and Management, 132(10), 1037–1042. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:10(1037)

Petroutsatou, K., Georgopoulos, E., Lambropoulos, S., & Pantouvakis, J. (2011). Early cost estimating of road tunnel construction using neural networks. Journal of Construction Engineering and Management, 138(6), 679–687. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000479

Public Construction Commission. (2020). Construction management information system. https://cmdweb.pcc.gov.tw//

Rozenes, S., Vitner, G., & Spraggett, S. (2004). MPCS: Multidimensional project control system. International Journal of Project Manage-ment, 22(2), 109–118. https://doi.org/10.1016/S0263-7863(03)00002-4

Sambasivan, M., & Soon, Y. (2007). Causes and effects of delays in Malaysian construction industry. International Journal of Project Manage-ment, 25, 517–526. https://doi.org/10.1016/j.ijproman.2006.11.007

Santoso, D. S., & Gallage, P. G. M. P. (2019). Critical factors affecting the performance of large construction projects in developing countries: A case study of Sri Lanka. Journal of Engineering, Design and Technology, 18(3), 531–556. https://doi.org/10.1108/JEDT-05-2019-0130

Sanvido, V., Grobler, F., Parfitt, K., Guvenis, M., & Coyle, M. (1992). Critical success factors for construction projects. Journal of Construction Engineering and Management, 118(1), 94–111. https://doi.org/10.1061/(ASCE)0733-9364(1992)118:1(94)

Shrestha, P. P., & Zeleke, H. (2018). Effect of change orders on cost and schedule overruns of school building renovation projects. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 10(4), 04518018. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000271

Sullivan, J., El Asmar, M., Chalhoub, J., & Obeid, H. (2017). Two decades of performance comparisons for design-build, construction manager at risk, and design-bid-build: quantitative analysis of the state of knowledge on project cost, schedule, and quality. Journal of Construction Engi-neering and Management, 143(6), 04017009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001282

Tabish, S. Z., & Jha, K. N. (2012). Success traits for a construction project. Journal of Construction Engineering and Management, 138(10), 1131–1138. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000538

Telikani, A., Gandomi, A. H., & Shahbahrami, A. (2020). A survey of evolutionary computation for association rule mining. Information Science, 524, 318–352. https://doi.org/10.1016/j.ins.2020.02.073

Ting, C. K., Liaw, R. T., Wang, T. C., & Hong, T. P. (2018). Mining fuzzy association rules using a memetic algorithm based on structure repre-sentation. Memetic Computing, 10, 15–28. https://doi.org/10.1007/s12293-016-0220-3

Wang, Y. R., Yu, C. Y., & Chan, H. H. (2012). Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. International Journal of Project Management, 30(4), 470–478. https://doi.org/10.1016/j.ijproman.2011.09.002

Wei, W. L., & Chang, W. C. (2008). Analytic network process-based model for selecting an optimal product design solution with zero-one goal programming. Journal of Engineering Design, 19(1), 15–44. https://doi.org/10.1080/09544820601186054

Yang, J., Shen, G. Q., Drew, D. S., & Ho, M. (2010). Critical success factors for stakeholder management: construction practitioners’ perspectives. Journal of Construction Engineering and Management, 136(7), 778–786. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000180

Zavadskas, E. K., Vilutiene, T., Turskis, Z., & Saparauskas, J. (2014). Multi-criteria analysis of projects’ performance in construction. Archives of Civil and Mechanical Engineering, 14(1), 114–121. https://doi.org/10.1016/j.acme.2013.07.006