Bayesian belief network-based project complexity measurement considering causal relationships

    Lan Luo Affiliation
    ; Limao Zhang Affiliation
    ; Guangdong Wu Affiliation


This research proposes a Bayesian belief network-based approach to measure the project complexity in the construction industry. Firstly, project complexity nodes are identified for model development based on the literature review. Secondly, the project complexity measurement model is developed with 225 training samples and validated with 20 test samples. Thirdly, the developed measurement model is utilized to conduct model analytics for sequential decision making, which includes predictive, diagnostic, sensitivity, and influence chain analysis. Finally, EXPO 2010 is used to testify the effectiveness and applicability of the proposed approach. Results indicate that (1) more attention should be paid on technological complexity, information complexity, and task complexity in the process of complexity management; (2) the proposed measurement model can be applied into practice to predict the complexity level for a specific project. The uniqueness of this study lies in developing project complexity measurement model (PCMM) with the cause-effect relationships taken into account. This research contributes to (a) the state of knowledge by proposing a method that is capable of measuring the complexity level under what-if scenarios for complexity management, and (b) the state of practice by providing insights into a better understanding of causal relationships among influencing factors of complexity in construction projects.

Keyword : project complexity measurement model (PCMM), Bayesian belief network, sensitivity analysis, influence chain analysis

How to Cite
Luo, L., Zhang, L., & Wu, G. (2020). Bayesian belief network-based project complexity measurement considering causal relationships. Journal of Civil Engineering and Management, 26(2), 200-215.
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Feb 21, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.


An, Y., Rogers, J., Kingsley, G., Matisoff, D. C., Mistur, E., & Ashuri, B. (2018). Influence of task complexity in shaping environmental review and engineering design durations. Journal of Management in Engineering, 34(6), 04018043.

Applegate, C. J., & Tien, I. (2018). Framework for probabilistic vulnerability analysis of interdependent infrastructure systems. Journal of Computing in Civil Engineering, 33(1), 04018058.

Baccarini, D. (1996). The concept of project complexity – a review. International Journal of Project Management, 14(4), 201–204.

Bakhshi, J., Ireland, V., & Gorod, A. (2016). Clarifying the project complexity construct: Past, present and future. International Journal of Project Management, 34(7), 1199–1213.

Berger, J. O. (2013). Statistical decision theory and Bayesian analysis. Springer Science & Business Media.

Bosch-Rekveldt, M. G. C. (2011). Managing project complexity: A study into adapting early project phases to improve project performance in large engineering projects. Delft University of Technology, Delft, Netherlands.

Bosch-Rekveldt, M., Jongkind, Y., Mooi, H., Bakker, H., & Verbraeck, A. (2011). Grasping project complexity in large engineering projects: The TOE (Technical, Organizational and Environmental) framework. International Journal of Project Management, 29(6), 728–739.

Coenen, J., Van der Heijden, R. E., & van Riel, A. C. (2018). Understanding approaches to complexity and uncertainty in closed-loop supply chain management: Past findings and future directions. Journal of Cleaner Production, 201, 1–13.

Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4), 309–347.

Diallo, T. M., Henry, S., Ouzrout, Y., & Bouras, A. (2018). Databased fault diagnosis model using a Bayesian causal analysis framework. International Journal of Information Technology & Decision Making, 17(2), 583–620.

Dikmen, I., Budayan, C., Talat Birgonul, M., & Hayat, E. (2018). Effects of risk attitude and controllability assumption on risk ratings: Observational study on international construction project risk assessment. Journal of Management in Engineering, 34(6), 04018037.

Ellinas, C., Allan, N., & Johansson, A. (2018). Toward project complexity evaluation: A structural perspective. IEEE Systems Journal, 12(1), 228–239.

Gao, N., Chen, Y., Wang, W., & Wang, Y. (2018). Addressing project complexity: The role of contractual functions. Journal of Management in Engineering, 34(3), 04018011.

Girmscheid, G., & Brockmann, C. (2008). The inherent complexity of large scale engineering projects. Project Perspectives, 29, 22–26.

Gransberg, D. D., Shane, J. S., Strong, K., & del Puerto, C. L. (2012). Project complexity mapping in five dimensions for complex transportation projects. Journal of Management in Engineering, 29(4), 316–326.

He, Q., Luo, L., Hu, Y., & Chan, A. P. (2015). Measuring the complexity of mega construction projects in China – A fuzzy analytic network process analysis. International Journal of Project Management, 33(3), 549–563.

Hwang, B.-G., Shan, M., & Looi, K.-Y. (2018). Key constraints and mitigation strategies for prefabricated prefinished volumetric construction. Journal of Cleaner Production, 183, 183–193.

Jarkas, A. M. (2017). Contractors’ perspective of construction project complexity: Definitions, principles, and relevant contributors. Journal of Professional Issues in Engineering Education and Practice, 143(4), 04017007.

Lebcir, R. M., & Choudrie, J. (2011). A dynamic model of the effects of project complexity on time to complete construction projects. International Journal of Innovation, Management and Technology, 2(6), 477–483.

Lessard, D., Sakhrani, V., & Miller, R. (2014). House of project complexity – understanding complexity in large infrastructure projects. Engineering Project Organization Journal, 4(4), 170–192.

Lu, Y., Luo, L., Wang, H., Le, Y., & Shi, Q. (2015). Measurement model of project complexity for large-scale projects from task and organization perspective. International Journal of Project Management, 33(3), 610–622.

Luo, L., He, Q., Xie, J., Yang, D., & Wu, G. (2016). Investigating the relationship between project complexity and success in complex construction projects. Journal of Management in Engineering, 33(2), 04016036.

Luo, L., He, Q., Jaselskis, E. J., & Xie, J. (2017). Construction project complexity: research trends and implications. Journal of Construction Engineering and Management, 143(7), 04017019.

Ma, X., Xiong, F., Olawumi, T. O., Dong, N., & Chan, A. P. (2018). Conceptual framework and roadmap approach for integrating BIM into lifecycle project management. Journal of Management in Engineering, 34(6), 05018011.

Maylor, H., Vidgen, R., & Carver, S. (2008). Managerial complexity in project-based operations: A grounded model and its implications for practice. Project Management Journal, 39(S1), S15–S26.

Mihm, J., Loch, C., & Huchzermeier, A. (2003). Problem-solving oscillations in complex engineering projects. Management Science, 49(6), 733–750.

Nguyen, A. T., Nguyen, L. D., Le-Hoai, L., & Dang, C. N. (2015). Quantifying the complexity of transportation projects using the fuzzy analytic hierarchy process. International Journal of Project Management, 33(6), 1364–1376.

Owens, J., Ahn, J., Shane, J. S., Strong, K. C., & Gransberg, D. D. (2012). Defining complex project management of large US transportation projects: A comparative case study analysis. Public Works Management & Policy, 17(2), 170–188.

Pan, Y., Zhang, L., Li, Z., & Ding, L. (2019). Improved fuzzy Bayesian network-based risk analysis with interval-valued fuzzy sets and DS evidence theory. IEEE Transactions on Fuzzy Systems.

Puddicombe, M. S. (2011). Novelty and technical complexity: Critical constructs in capital projects. Journal of Construction Engineering and Management, 138(5), 613–620.

Qazi, A., Quigley, J., Dickson, A., & Kirytopoulos, K. (2016). Project complexity and risk management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects. International Journal of Project Management, 34(7), 1183–1198.

Qureshi, S. M., & Kang, C. (2015). Analysing the organizational factors of project complexity using structural equation modelling. International Journal of Project Management, 33(1), 165–176.

Remington, K., & Pollack, J. (2016). Tools for complex projects. Routledge.

Remington, K., Zolin, R., & Turner, R. (2009). A model of project complexity: distinguishing dimensions of complexity from severity. In Proceedings of the 9th International Research Network of Project Management Conference, Berlin, Germany.

Santana, G. (1990). Classification of construction projects by scales of complexity. International Journal of Project Management, 8(2), 102–104.

Shafiei-Monfared, S., & Jenab, K. (2012). A novel approach for complexity measure analysis in design projects. Journal of Engineering Design, 23(3), 185–194.

Sinha, S., Kumar, B., & Thomson, A. (2006). Measuring project complexity: a project manager’s tool. Architectural Engineering and Design Management, 2(3), 187–202.

Tatikonda, M. V., & Rosenthal, S. R. (2000). Technology novelty, project complexity, and product development project execution success: a deeper look at task uncertainty in product innovation. IEEE Transactions on Engineering Management, 47(1), 74–87.

Thomas, J., & Mengel, T. (2008). Preparing project managers to deal with complexity – Advanced project management education. International Journal of Project Management, 26(3), 304–315.

Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413–1432.

Vidal, L.-A., & Marle, F. (2008). Understanding project complexity: implications on project management. Kybernetes, 37(8), 1094–1110.

Vidal, L.-A., Marle, F., & Bocquet, J.-C. (2011a). Measuring project complexity using the Analytic Hierarchy Process. International Journal of Project Management, 29(6), 718–727.

Vidal, L., Marle, F., & Bocquet, J. (2011b). Using a Delphi process and the Analytic Hierarchy Process (AHP) to evaluate the complexity of projects. Expert Systems with Applications, 38(5), 5388–5405.

Wallner, H. P. (1999). Towards sustainable development of industry: networking, complexity and eco-clusters. Journal of Cleaner Production, 7(1), 49–58.

Wang, L., & Zhang, X. (2018). Bayesian analytics for estimating risk probability in PPP waste-to-energy projects. Journal of Management in Engineering, 34(6), 04018047.

Wang, Y., Zhang, X., & Wang, Z. (2018). A proactive decision support system for online event streams. International Journal of Information Technology & Decision Making, 17(6), 1891–1913.

Wee, Y. Y., Cheah, W. P., Tan, S. C., & Wee, K. (2015). A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map. Expert Systems with Applications, 42(1), 468–487.

Williams, T. M. (1999). The need for new paradigms for complex projects. International Journal of Project Management, 17(5), 269–273.

Wu, X., Liu, H., Zhang, L., Skibniewski, M. J., Deng, Q., & Teng, J. (2015). A dynamic Bayesian network based approach to safety decision support in tunnel construction. Reliability Engineering & System Safety, 134, 157–168.

Xia, B., & Chan, A. P. (2012). Measuring complexity for building projects: a Delphi study. Engineering, Construction and Architectural Management, 19(1), 7–24.

Yukalov, V. I., & Sornette, D. (2015). Role of information in decision making of social agents. International Journal of Information Technology & Decision Making, 14(5), 1129–1166.

Zhang, L., Wu, X., Skibniewski, M. J., Zhong, J., & Lu, Y. (2014). Bayesian-network-based safety risk analysis in construction projects. Reliability Engineering & System Safety, 131, 29–39.

Zhang, L., Wu, X., Qin, Y., Skibniewski, M. J., & Liu, W. (2016). Towards a fuzzy Bayesian network based approach for safety risk analysis of tunnel – Induced pipeline damage. Risk Analysis, 36(2), 278–301.

Zhu, J., & Mostafavi, A. (2017). Discovering complexity and emergent properties in project systems: A new approach to understanding project performance. International Journal of Project Management, 35(1), 1–12.