A Network-Based Methodology for Quantitative Knowledge Gap Identification in Construction Simulation and Modeling Research


The traditional literature review process is not quantitative in nature, leading to duplication of work and publication of papers that tackle secondary knowledge gaps while missing the primary ones. This paper proposes a novel network-based methodology for quantitative analysis of model-based literary works, which utilize simulation models such as agent-based modeling (ABM), system dynamics (SD), and discrete event simulation (DES). The proposed methodology uses concepts from social network analysis (SNA) to: (1) study how the relationship between different modeling parameters, (2) compare their relative importance, (3) and identify the areas that are ill-studied. The developed methodology was used to analyze the literature of the application of SD models in managing construction projects. The results show that there is a significant gap regarding out-of-sequence work. The developed methodology directs researchers towards the proper knowledge gaps. This will lead to more well-directed research that has significant additions to the body of knowledge.

Meeting Name

ASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019 (2019: Jun. 17-19, Atlanta, GA)


Civil, Architectural and Environmental Engineering

Keywords and Phrases

Autonomous agents; Computational methods; Information theory; Visualization, Agent-based model; Body of knowledge; Construction projects; Construction simulation; Literature reviews; Model parameters; Out of sequences; Quantitative knowledge, Discrete event simulation

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2019 American Society of Civil Engineers (ASCE), All rights reserved.

Publication Date

01 Jun 2019