Evaluating complexity of construction precast component: empirical study in Taiwan
Abstract
Companies in the construction precast industry usually face lack of skilled manpower, overtime working, and complexity of manpower allocation. The objective of this research is to identify the complexity of precast components using Swarm-Inspired Projection (SIP) algorithm. After conducting a comprehensive literature review regarding precast production, clustering, classification, cost management, manpower allocation, and optimization, expertise from field/head-quarter supervision leads the way to SIP algorithm that drives collected data converted to certain clusters. Data collection was carried out to gather over 90% precast construction data in Taiwan for the recent decade. A total of 1,015,840 datasets were collected and then 772,212 datasets were taken into computation SIP algorithm after data filtering. Evaluation and comparison of models reveal SIP’s remarkable efficiency, halving processing time while delivering superior results. The study identifies four complexity tiers linked to the manufacturing of building precast elements. Significant variations exist among these tiers, with workload increments of 18.22%, 11.71%, and 30.08% between Level 1 and 2, Level 2 and 3, and Level 3 and 4, respectively.
Keyword : construction precast, clustering, complexity level, Swarm-Inspired Projection, manpower allocation, component production
How to Cite
Chen, J.-H., Su, M.-C., Lin, S., Tai, H.-W., & Hsu, S.-C. (2025). Evaluating complexity of construction precast component: empirical study in Taiwan. Journal of Civil Engineering and Management, 31(3), 266–280. https://doi.org/10.3846/jcem.2025.23323
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Kong, L. L., Li, H., Luo, H. B., Ding, L. Y., Luo, X. C., & Skitmore, M. (2017). Optimal single-machine batch scheduling for the manufacture, transportation and JIT assembly of precast construction with changeover costs within due dates. Automation in Construction, 81, 34–43. https://doi.org/10.1016/j.autcon.2017.03.016
Leu, S. S., & Hwang, S. T. (2002). GA-based resource-constrained flow-shop scheduling model for mixed precast production. Automation in Construction, 11, 439–452. https://doi.org/10.1016/S0926-5805(01)00083-8
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Low, S. P., & Choong, J. C. (2001). Just-in-time management of pre-cast concrete component. Journal of Construction Engineering and Management, 127(6), 494–501. https://doi.org/10.1061/(ASCE)0733-9364(2001)127:6(494)
Ma, Z. L., Yang, Z. T., Liu, S. L., & Wu, S. (2018a). Optimized rescheduling of multiple production lines for flowshop production of reinforced precast concrete components. Automation in Construction, 95, 86–97. https://doi.org/10.1016/j.autcon.2018.08.002
Ma, Z., Zhao, Z., & Yan, L. (2018b). Heterogeneous fuzzy XML data integration based on structural and semantic similarities. Fuzzy Sets and Systems, 351, 64–89. https://doi.org/10.1016/j.fss.2018.04.018
Omran, M., Salman, A., & Engelbrecht, A. (2005). Dynamic clustering using particle swarm optimization with application in unsupervised image classification. In Proceedings of 5th World Enformatika Conference (ICCI), Prague, Czech Republic.
Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. Computer Graphics, 21(4), 25–34. https://doi.org/10.1145/37402.37406
Salama, D. A., & El-Gohary, N. M. (2013). Automated compliance checking of construction operation plans using a deontology for the construction domain. Journal of Computing in Civil Engineering, 27(6), 681–698. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000298
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Wang, Z., Hu, H., & Gong, J. (2018). Framework for modeling operational uncertainty to optimize offsite production scheduling of precast components. Automation in Construction, 86, 69–80. https://doi.org/10.1016/j.autcon.2017.10.026
Won, K. H., Goonjae, L., Sungho, L., & Sunkuk, K. (2014). Algorithms for in-situ production layout of composite precast concrete members. Automation in Construction, 41, 50–59. https://doi.org/10.1016/j.autcon.2014.02.005
Yang, Z., Ma, Z., & Wu, S. (2016). Optimized flowshop scheduling of multiple production lines for precast production. Automation in Construction, 72, 321–329. https://doi.org/10.1016/j.autcon.2016.08.021
Yeung, C. L., Cheung, C. F., Wang, W. M., Tsui, E., & Lee, W. B. (2016). Managing knowledge in the construction industry through computational generation of semi-fiction narratives. Journal of Knowledge Management, 20(2), 386–414. https://doi.org/10.1108/JKM-07-2015-0253
Yeung, C. L., Wang, W. M., Cheung, C. F., Tsui, E., Setchi, R., & Lee, R. W. B. (2018). Computational narrative mapping for the acquisition and representation of lessons learned knowledge. Engineering Applications of Artificial Intelligence, 71, 190–209. https://doi.org/10.1016/j.engappai.2018.02.011
Yuan, Z., Sun, C., & Wang, Y. (2018). Design for manufacture and assembly-oriented parametric design of prefabricated buildings. Automation in Construction, 88, 13–22. https://doi.org/10.1016/j.autcon.2017.12.021
Zhang, L., & Atkins, A. S. (2015). A decision support application in tracking construction waste using rule-based reasoning and RFID technology. International Journal of Computational Intelligence Systems, 8(1), 128–137. https://doi.org/10.1080/18756891.2014.963988
Anvari, B., Angeloudis, P., & Ochieng, W. Y. (2016). A multi-objective GA-based optimisation for holistic manufacturing, transportation and assembly of precast construction. Automation in Construction, 71, 226–241. https://doi.org/10.1016/j.autcon.2016.08.007
Arabameri, A., Pourghasemi, H. R., & Yamani, M. (2017). Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environmental Earth Sciences, 76, Article 832. https://doi.org/10.1007/s12665-017-7177-5
Arashpour, M., Bai, Y., Aranda-mena, G., Bab-Hadiashar, A., Hosseini, R., & Kalutara, P. (2017). Optimizing decisions in advanced manufacturing of prefabricated products: Theorizing supply chain configurations in off-site construction. Automation in Construction, 84, 146–153. https://doi.org/10.1016/j.autcon.2017.08.032
Bandyopadhyay, S., & Maulik, U. (2002). Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition, 35(6), 1197–1208. https://doi.org/10.1016/S0031-3203(01)00108-X
Bu, F. (2018). An efficient fuzzy c-means approach based on canonical polyadic decomposition for clustering big data in IoT. Future Generation Computer Systems, 88, 675–682. https://doi.org/10.1016/j.future.2018.04.045
Chou, J.-S., & Ngoc-Tri, N. (2018). Engineering strength of fiber-reinforced soil estimated by swarm intelligence optimized regression system. Neural Computing and Applications, 30(7), 2129–2144. https://doi.org/10.1007/s00521-016-2739-0
Cao, M.-T., Cheng, M.-Y., & Wu, Y.-W. (2015). Hybrid computational model for forecasting Taiwan construction cost index. Journal of Construction Engineering and Management, 141(4), Article 04014089. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000948
Chan, W. T., & Hu, H. (2001). An application of genetic algorithms to precast production scheduling. Computers and Structures, 79, 1605–1616. https://doi.org/10.1016/S0045-7949(01)00036-0
Chan, W. T., & Hu, H. (2002). Constraint programming approach to precast production scheduling. Journal of Construction Engineering and Management, 128(6), 513–521. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:6(513)
Chou, J.-S., & Anh-Duc, P. (2014). Hybrid computational model for predicting bridge scour depth near piers and abutments. Automation in Construction, 48, 88–96. https://doi.org/10.1016/j.autcon.2014.08.006
Chen, J.-H., Su, M.-C., & Annuerine, B. (2016a). Exploring and weighting features for financially distressed construction companies using Swarm Inspired Projection algorithm. Advanced Engineering Informatics, 30, 376–389. https://doi.org/10.1016/j.aei.2016.05.003
Chen, J.-H., Yang, L.-R., & Tai, H.-W. (2016b). Process reengineering and improvement for building precast production. Automation in Construction, 68, 249–258. https://doi.org/10.1016/j.autcon.2016.05.015
Chen, J.-H., Su, M.-C, Lin, S.-K., Lin W.-J., & Gheisari, M. (2023). Smart bridge maintenance using cluster merging algorithm based on self-organizing map optimization. Automation in Construction, 152, Article 104913. https://doi.org/10.1016/j.autcon.2023.104913
Davi, C. C. M., Silveira, D. S., & Neto, F. B. L. (2014). A framework using computational intelligence techniques for decision support systems in medicine. IEEE Latin America Transactions, 12(2), 205–211. https://doi.org/10.1109/TLA.2014.6749539
de Albuquerque, A. T., El Debs, M. K., & Melo, A. M. C. (2012). A cost optimization-based design of precast concrete floors using genetic algorithms. Automation in Construction, 22, 348–356. https://doi.org/10.1016/j.autcon.2011.09.013
Fasanghari, M., Iranmanesh, S. H., & Amalnick, M. S. (2015). Predicting the success of projects using evolutionary hybrid fuzzy neural network method in early stages. Journal of Multivalued Logic and Soft Computing, 25(2–3), 291–321.
Glauber, R., & Claro, D. B. (2018). A systematic mapping study on open information extraction. Expert Systems with Applications, 241, 127–132. https://doi.org/10.1016/j.eswa.2018.06.046
Gautam, G., & Chaudhuri, B. B. (2004). A novel genetic algorithm for automatic clustering. Pattern Recognition Letters, 25, 173–187. https://doi.org/10.1016/j.patrec.2003.09.012
Hallowell, M. R., & Gambatese, J. A. (2010). Qualitative research: Application of Delphi method to CEM research. Journal of Construction Engineering and Management, 136, 99–107. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000137
Han, J., & Kamber, M. (2000). Data mining: Concepts and techniques. Morgan Kaufmann.
Han, C., Li, Q. N., Wang, X., Jiang, W. S., & Li, W. (2016) Research on rotation capacity of the new precast concrete assemble beam-column joints. Steel and Composites Structures, 22(3), 613–625. https://doi.org/10.12989/scs.2016.22.3.613
Holimchayachotikul, P., & Leksakul, K. (2017). Predictive performance measurement system for retail industry using neuro-fuzzy system based on swarm intelligence. Soft Computing, 21(7), 1895–1912. https://doi.org/10.1007/s00500-016-2082-5
Ji, W., AbouRizk, S. M., Zaiane, O. R., & Li, Y. T. (2018). Complexity analysis approach for prefabricated construction products using uncertain data clustering. Journal of Construction Engineering and Management, 144(8), Article 04018063. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001520
Kieran, S., & Timberlake, J. (2004). Refabricating architecture. McGraw-Hill.
Ko, H. C., & Wang, S. F. (2010). GA-based decision support systems for precast production planning. Automation in Construction, 19, 907–916. https://doi.org/10.1016/j.autcon.2010.06.004
Kong, L. L., Li, H., Luo, H. B., Ding, L. Y., Luo, X. C., & Skitmore, M. (2017). Optimal single-machine batch scheduling for the manufacture, transportation and JIT assembly of precast construction with changeover costs within due dates. Automation in Construction, 81, 34–43. https://doi.org/10.1016/j.autcon.2017.03.016
Leu, S. S., & Hwang, S. T. (2002). GA-based resource-constrained flow-shop scheduling model for mixed precast production. Automation in Construction, 11, 439–452. https://doi.org/10.1016/S0926-5805(01)00083-8
Liu, J., Chen, Y., & Wang, X. (2022). Factors driving waste sorting in construction projects in China. Journal of Cleaner Production, 336, Article 130397. https://doi.org/10.1016/j.jclepro.2022.130397
Low, S. P., & Choong, J. C. (2001). Just-in-time management of pre-cast concrete component. Journal of Construction Engineering and Management, 127(6), 494–501. https://doi.org/10.1061/(ASCE)0733-9364(2001)127:6(494)
Ma, Z. L., Yang, Z. T., Liu, S. L., & Wu, S. (2018a). Optimized rescheduling of multiple production lines for flowshop production of reinforced precast concrete components. Automation in Construction, 95, 86–97. https://doi.org/10.1016/j.autcon.2018.08.002
Ma, Z., Zhao, Z., & Yan, L. (2018b). Heterogeneous fuzzy XML data integration based on structural and semantic similarities. Fuzzy Sets and Systems, 351, 64–89. https://doi.org/10.1016/j.fss.2018.04.018
Omran, M., Salman, A., & Engelbrecht, A. (2005). Dynamic clustering using particle swarm optimization with application in unsupervised image classification. In Proceedings of 5th World Enformatika Conference (ICCI), Prague, Czech Republic.
Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. Computer Graphics, 21(4), 25–34. https://doi.org/10.1145/37402.37406
Salama, D. A., & El-Gohary, N. M. (2013). Automated compliance checking of construction operation plans using a deontology for the construction domain. Journal of Computing in Civil Engineering, 27(6), 681–698. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000298
Su, M. C., Su, S. Y., & Zhao, Y. X. (2009). A swarm-inspired projection algorithm. Journal of the Pattern Recognition Society, 42, 2764–2786. https://doi.org/10.1016/j.patcog.2009.03.020
Wang, Z., Hu, H., & Gong, J. (2018). Framework for modeling operational uncertainty to optimize offsite production scheduling of precast components. Automation in Construction, 86, 69–80. https://doi.org/10.1016/j.autcon.2017.10.026
Won, K. H., Goonjae, L., Sungho, L., & Sunkuk, K. (2014). Algorithms for in-situ production layout of composite precast concrete members. Automation in Construction, 41, 50–59. https://doi.org/10.1016/j.autcon.2014.02.005
Yang, Z., Ma, Z., & Wu, S. (2016). Optimized flowshop scheduling of multiple production lines for precast production. Automation in Construction, 72, 321–329. https://doi.org/10.1016/j.autcon.2016.08.021
Yeung, C. L., Cheung, C. F., Wang, W. M., Tsui, E., & Lee, W. B. (2016). Managing knowledge in the construction industry through computational generation of semi-fiction narratives. Journal of Knowledge Management, 20(2), 386–414. https://doi.org/10.1108/JKM-07-2015-0253
Yeung, C. L., Wang, W. M., Cheung, C. F., Tsui, E., Setchi, R., & Lee, R. W. B. (2018). Computational narrative mapping for the acquisition and representation of lessons learned knowledge. Engineering Applications of Artificial Intelligence, 71, 190–209. https://doi.org/10.1016/j.engappai.2018.02.011
Yuan, Z., Sun, C., & Wang, Y. (2018). Design for manufacture and assembly-oriented parametric design of prefabricated buildings. Automation in Construction, 88, 13–22. https://doi.org/10.1016/j.autcon.2017.12.021
Zhang, L., & Atkins, A. S. (2015). A decision support application in tracking construction waste using rule-based reasoning and RFID technology. International Journal of Computational Intelligence Systems, 8(1), 128–137. https://doi.org/10.1080/18756891.2014.963988