Construction Project Complexity: Research Trends and Implications

Luo, Lan; He, Qigai; Jaselskis, Edward J.; Xie, Jianxun

doi:10.1061/(asce)co.1943-7862.0001306

Cited by 144 publications

(103 citation statements)

References 43 publications

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“…It concludes from Figure 3 that the probability of the project complexity being the Low state is 27%, being the Moderate state is 49%, and being the High state is 25%. In recent years, the construction industry has seen rapid growth in projects of increasing size and complexity (Luo et al, 2017). This condition is proven by the results obtained through this research.…”

Section: Network Structure and Cpt Determinationsupporting

confidence: 64%

“…The concept of project complexity has been discussed for years, but there is a lack of consensus on what constitutes project complexity since it is a term that is difficult to define and even harder to quantify (Luo et al, 2017;Vidal et al, 2011a). Baccarini (1996) defined project complexity as "consisting of many varied interrelated parts".…”

Section: Literature Review On Project Complexity Measurementmentioning

confidence: 99%

“…However, construction projects are usually beset with serious waste and cost overruns (Applegate & Tien, 2018;Thomas & Mengel, 2008;Zhu & Mostafavi, 2017). Underestimating the project complexity is the main reason, which is the state of being involved and intricate as a result of including varied interrelated parts within a subject (Gao et al, 2018;He et al, 2015;Luo et al, 2017). Therefore, with an aim to manage the complexity of construction projects efficiently, researchers and industry experts concentrate on measuring project complexity (Bakhshi et al, 2016;Coenen et al, 2018;Luo et al, 2017;Wallner, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Belief Network-Based Project Complexity Measurement Considering Causal Relationships

Luo

Zhang

2020

Journal of Civil Engineering and Management

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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.

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Section: Network Structure and Cpt Determinationsupporting

confidence: 64%

Section: Literature Review On Project Complexity Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian Belief Network-Based Project Complexity Measurement Considering Causal Relationships

Luo

Zhang

2020

Journal of Civil Engineering and Management

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“…In the construction research domain, construction project complexity has been primarily investigated from four perspectives: (1) influencing factors contributing to project complexity; (2) the impact of project complexity; (3) project complexity measurement methods; and (4) management of project complexity (Luo et al 2017). Throughout these studies, product complexity has been found to influence overall project complexity (Baccarini 1996;Senescu et al 2012;Williams 1999).…”

Section: Rationale Product Complexitymentioning

confidence: 99%

Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering

AbouRizk

Zaı̈ane

et al. 2018

J. Constr. Eng. Manage.

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This paper proposes an uncertain data clustering approach to quantitatively analyze the complexity of prefabricated construction components through the integration of quality performance-based measures with associated engineering design information. The proposed model is constructed in three steps, which (1) measure prefabricated construction product complexity (hereafter referred to as product complexity) by introducing a Bayesian-based nonconforming quality performance indicator; (2) score each type of product complexity by developing a Hellinger distance-based distribution similarity measurement; and (3) cluster products into homogeneous complexity groups by using the agglomerative hierarchical clustering technique. An illustrative example is provided to demonstrate the proposed approach, and a case study of an industrial company in Edmonton, Canada, is conducted to validate the feasibility and applicability of the proposed model. This research inventively defines and investigates product complexity from the perspective of product quality performance with design information associated. The research outcomes provide simplified, interpretable, and informative insights for practitioners to better analyze and manage product complexity. In addition to this practical contribution, a novel hierarchical clustering technique is devised. This technique is capable of clustering uncertain data (i.e., beta distributions) with lower computational complexity and has the potential to be generalized to cluster all types of uncertain data.

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“…The difficulty to manage risks comes from the complexity of construction projects (Luo et al., ; Martínez‐Rojas et al., ). Indeed, construction projects may be assimilated to complex systems (Northrop, ) because they are composed of heterogeneous entities among which local interactions may lead to collective organizations at higher levels, and these emergent structures in turn influence individual behavior.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Processing and Risk Monitoring in Construction Projects Using Hierarchical Probabilistic Relational Models

Baudrit

Taillandier

Tran

et al. 2018

Computer aided Civil Eng

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Construction projects do not often reach their expected results regarding time, cost, and quality, due to the internal and external environment variations. Despite a substantial literature about risk management, no generic approach is proposed to represent construction project considering together technical and human dimensions or sustainability with their uncertainties. Modeling complex dynamical systems from heterogeneous pieces of knowledge varying in precision and reliability is a challenging task. This article proposes an innovative generic and versatile approach, based on the formalism of hierarchical probabilistic relational models to analyze and to propagate uncertainty in construction project regarding different levels of knowledge. The aim is to obtain a flexible, portable, and versatile model able to simulate the behavior of complex system's entities involved in any construction project at different levels of detail while taking uncertainty into account. To illustrate and highlight this approach, an academic example and a real case are proposed.

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Construction Project Complexity: Research Trends and Implications

Cited by 144 publications

References 43 publications

Bayesian Belief Network-Based Project Complexity Measurement Considering Causal Relationships

Bayesian Belief Network-Based Project Complexity Measurement Considering Causal Relationships

Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering

Uncertainty Processing and Risk Monitoring in Construction Projects Using Hierarchical Probabilistic Relational Models

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