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Evolution of AEC Project Networks: a...

Pidiha, Nishchay.

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Evolution of AEC Project Networks: an Agent-Based Modeling Approach.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Evolution of AEC Project Networks: an Agent-Based Modeling Approach./
作者:	Pidiha, Nishchay.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	160 p.
附註:	Source: Masters Abstracts International, Volume: 82-11.
Contained By:	Masters Abstracts International82-11.
標題:	Architectural engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28417709
ISBN:	9798728227311

Evolution of AEC Project Networks: an Agent-Based Modeling Approach.
Pidiha, Nishchay.

Evolution of AEC Project Networks: an Agent-Based Modeling Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 160 p.

Source: Masters Abstracts International, Volume: 82-11.

Thesis (M.S.)--Michigan State University, 2021.

This item must not be sold to any third party vendors.

In Architecture, Engineering, and Construction (AEC) industry, projects call for collaboration between different expertise areas. To improve collaboration and knowledge transfer between different stakeholders, an in-depth understanding of the communication network structure is crucial. While Social Network Analysis (SNA) shows promise in analyzing communication network structures, the AEC literature to date shows its use mainly in a cross-sectional manner. A recent longitudinal case study shows that these networks are not static and can evolve overtime. However, due to fast-paced delivery of any AEC project, researchers often arrive at missing data, sometimes depriving them from concluding statistically significant results in a longitudinal study. Hence, there is a need for further investigating the evolution of AEC project teams network structures through a simulation that emulate certain aspects of a social network structure evolution over time. In response to this need, the study aims to explicate, how real-world size AEC project networks evolve over project delivery based on similarity of node characteristics (e.g., homophily) and node behaviors in networks (e.g., node's popularity). To do so, the study adopted selection model, and simulated multiple agent-based models basing its initial condition on an empirical case study. The evolutions of the network structures were analyzed overtime. Finally, the study compared the results with recent similar works. Deliverables include an improved understanding of AEC project network structures and characteristics, and practical implications relating to team collaboration and coordination considering project characteristics such as team size, and complexity.

ISBN: 9798728227311Subjects--Topical Terms:

3174102
Architectural engineering.
Subjects--Index Terms:

Agent-Based Model

Evolution of AEC Project Networks: an Agent-Based Modeling Approach.
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506 $a This item must not be sold to any third party vendors.
520 $a In Architecture, Engineering, and Construction (AEC) industry, projects call for collaboration between different expertise areas. To improve collaboration and knowledge transfer between different stakeholders, an in-depth understanding of the communication network structure is crucial. While Social Network Analysis (SNA) shows promise in analyzing communication network structures, the AEC literature to date shows its use mainly in a cross-sectional manner. A recent longitudinal case study shows that these networks are not static and can evolve overtime. However, due to fast-paced delivery of any AEC project, researchers often arrive at missing data, sometimes depriving them from concluding statistically significant results in a longitudinal study. Hence, there is a need for further investigating the evolution of AEC project teams network structures through a simulation that emulate certain aspects of a social network structure evolution over time. In response to this need, the study aims to explicate, how real-world size AEC project networks evolve over project delivery based on similarity of node characteristics (e.g., homophily) and node behaviors in networks (e.g., node's popularity). To do so, the study adopted selection model, and simulated multiple agent-based models basing its initial condition on an empirical case study. The evolutions of the network structures were analyzed overtime. Finally, the study compared the results with recent similar works. Deliverables include an improved understanding of AEC project network structures and characteristics, and practical implications relating to team collaboration and coordination considering project characteristics such as team size, and complexity.
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