Abstract
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.
Highlights
In recent years, rapid growth in the construction industry has led to an increase in size and complexity of the projects (Luo et al, 2016; Qazi et al, 2016)
Proposed a framework for measuring project complexity based on the Shannon information theory Defined the project complexity framework as project size, project variety, project interdependence, and elements of context Reported a grounded model with an investigation of the perceptions of project managers Developed a framework for characterizing project complexity as technical, organizational and environmental complexity Used the Analytic Hierarchy Process (AHP) to formulate a project complexity measure model for assisting project managers’ decision making Developed the “complexity footprint” based on an international research team’s detailed study of eighteen complex projects Developed a five-dimensional complexity model including cost, schedule, design, context, and finance Combined managerial and technical graphs and the complexity design structure matrix to measure the relative complexity of design projects
Identified several key parameters for measuring building project complexity Built a house of project complexity to understand complexity in large infrastructure projects Utilized a Fuzzy Analytic Network Process (FANP) approach to measure the complexity of mega construction projects
Summary
Rapid growth in the construction industry has led to an increase in size and complexity of the projects (Luo et al, 2016; Qazi et al, 2016). Numerous studies have been conducted on measuring project complexity from different perspectives (Bosch-Rekveldt, 2011; Lebcir & Choudrie, 2011; Qazi et al, 2016). Most studies focus on the framework of project complexity and ignore the cause-effect relationships between project complexity and its influential factors. It is necessary to propose an approach that can measure the project complexity considering the cause-effect relationships
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