Analysis of the Schedule Risk of Prefabricated Buildings Based on ISM and Research of Transfer Path

Xiaobo Shi, Chenchen Liu, Wanying Liu, Fang Shen, Jiayan Chen, Kunkun Ma


Project schedule management is an important part of prefabricated construction project management. General contracting is an effective way to promote the development of prefabricated construction. However, at present, from the perspective of general contracting, the risk factors affecting the project progress of prefabricated buildings are not clear, and the relationship between risks is not known. The purpose of this study is to study the composition, hierarchical structure and transmission path of schedule risk factors of prefabricated construction in general contracting mode, so as to help the general contractor formulate effective schedule risk avoidance measures. This study uses grounded theory to obtain 22 risk factors that affect the progress of assembly building projects, and the data are from expert interviews. Using Delphi method and interpretative structural modeling (ISM), these factors are divided into seven levels, and the ISM model of schedule risk factors is constructed. The research shows that there are 60 progress risk transmission paths, and four progress risk transfer chains are obtained. This paper also further analyzes and puts forward suggestions to avoid risks for each level.


Doi: 10.28991/CEJ-2022-08-01-010

Full Text: PDF


Prefabricated Buildings; Schedule Management; Risk Factors; General Contracting Mode.


Wang, Z.-L., Shen, H.-C., & Zuo, J. (2019). Risks in Prefabricated Buildings in China: Importance-Performance Analysis Approach. Sustainability, 11(12), 3450. doi:10.3390/su11123450.

Yuan, Z., Sun, C., & Wang, Y. (2018). Design for Manufacture and Assembly-oriented parametric design of prefabricated buildings. Automation in Construction, 88, 13–22. doi:10.1016/j.autcon.2017.12.021.

Khalili, A., & Chua, D. K. (2014). Integrated Prefabrication Configuration and Component Grouping for Resource Optimization of Precast Production. Journal of Construction Engineering and Management, 140(2), 04013052. doi:10.1061/(asce)co.1943-7862.0000798.

Priestley, M. J. N., Sritharan, S. S., Conley, J. R., & Pampanin, S. (1999). Preliminary results and conclusions from the PRESSS five-story precast concrete test building. PCI Journal, 44(6), 42–67. doi:10.15554/pcij.11011999.42.67.

Tory, M., Staub-French, S., Huang, D., Chang, Y. L., Swindells, C., & Pottinger, R. (2013). Comparative visualization of construction schedules. Automation in Construction, 29, 68–82. doi:10.1016/j.autcon.2012.08.004.

Kim, K., Kim, G., Kim, K., Lee, Y., & Kim, J. (2009). Real-time progress management system for steel structure construction. Journal of Asian Architecture and Building Engineering, 8(1), 111–118. doi:10.3130/jaabe.8.111.

Luu, V. T., Kim, S. Y., Tuan, N. Van, & Ogunlana, S. O. (2009). Quantifying schedule risk in construction projects using Bayesian belief networks. International Journal of Project Management, 27(1), 39–50. doi:10.1016/j.ijproman.2008.03.003.

Castro-Lacouture, D., Süer, G. A., Gonzalez-Joaqui, J., & Yates, J. K. (2009). Construction Project Scheduling with Time, Cost, and Material Restrictions Using Fuzzy Mathematical Models and Critical Path Method. Journal of Construction Engineering and Management, 135(10), 1096–1104. doi:10.1061/(asce)0733-9364(2009)135:10(1096).

Bi, H., Lu, F., Duan, S., Huang, M., Zhu, J., & Liu, M. (2020). Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm. Engineering Applications of Artificial Intelligence, 91, 103584. doi:10.1016/j.engappai.2020.103584.

Chen, L., Lu, Q., Li, S., He, W., & Yang, J. (2021). Bayesian Monte Carlo Simulation–Driven Approach for Construction Schedule Risk Inference. Journal of Management in Engineering, 37(2), 04020115. doi:10.1061/(asce)me.1943-5479.0000884.

Xu, X., Wang, J., Li, C. Z., Huang, W., & Xia, N. (2018). Schedule risk analysis of infrastructure projects: A hybrid dynamic approach. Automation in Construction, 95, 20–34. doi:10.1016/j.autcon.2018.07.026.

Li, C. Z., Hong, J., Fan, C., Xu, X., & Shen, G. Q. (2018). Schedule delay analysis of prefabricated housing production: A hybrid dynamic approach. Journal of Cleaner Production, 195, 1533–1545. doi:10.1016/j.jclepro.2017.09.066.

Arashpour, M., Wakefield, R., Lee, E. W. M., Chan, R., & Hosseini, M. R. (2016). Analysis of interacting uncertainties in on-site and off-site activities: Implications for hybrid construction. International Journal of Project Management, 34(7), 1393–1402. doi:10.1016/j.ijproman.2016.02.004.

Ji, Y., Qi, L., Liu, Y., Liu, X., Li, H. X., & Li, Y. (2018). Assessing and prioritising delay factors of prefabricated concrete building projects in China. Applied Sciences (Switzerland), 8(11), 2324. doi:10.3390/app8112324.

Zhao, Y., Chen, W., Arashpour, M., Yang, Z., Shao, C., & Li, C. (2021). Predicting delays in prefabricated projects: SD-BP neural network to define effects of risk disruption. Engineering, Construction and Architectural Management. doi:10.1108/ECAM-12-2020-1050.

Tokdemir, O. B., Erol, H., & Dikmen, I. (2019). Delay Risk Assessment of Repetitive Construction Projects Using Line-of-Balance Scheduling and Monte Carlo Simulation. Journal of Construction Engineering and Management, 145(2), 04018132. doi:10.1061/(asce)co.1943-7862.0001595.

Abuzeinab, A., Arif, M., & Qadri, M. A. (2017). Barriers to MNEs green business models in the UK construction sector: An ISM analysis. Journal of Cleaner Production, 160, 27–37. doi:10.1016/j.jclepro.2017.01.003.

Sarhan, J. G., Xia, B., Fawzia, S., Karim, A., Olanipekun, A. O., & Coffey, V. (2020). Framework for the implementation of lean construction strategies using the interpretive structural modelling (ISM) technique: A case of the Saudi construction industry. Engineering, Construction and Architectural Management, 27(1), 1–23. doi:10.1108/ECAM-03-2018-0136.

Gan, X., Chang, R., Zuo, J., Wen, T., & Zillante, G. (2018). Barriers to the transition towards off-site construction in China: An Interpretive structural modeling approach. Journal of Cleaner Production, 197(PT1), 8–18. doi:10.1016/j.jclepro.2018.06.184.

Liu, H., Skibniewski, M. J., & Wang, M. (2016). Identification and hierarchical structure of critical success factors for innovation in construction projects: Chinese perspective. Journal of Civil Engineering and Management, 22(3), 401–416. doi:10.3846/13923730.2014.975739.

Wang, L., Ma, L., Wu, K. J., Chiu, A. S. F., & Nathaphan, S. (2018). Applying fuzzy interpretive structural modeling to evaluate responsible consumption and production under uncertainty. Industrial Management and Data Systems, 118(2), 432–462. doi:10.1108/IMDS-03-2017-0109.

Full Text: PDF

DOI: 10.28991/CEJ-2022-08-01-010


  • There are currently no refbacks.

Copyright (c) 2022 Wanying Liu

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