Research on Fire Risk Identification and Evolution in University Personnel Intensive Places Based on D-ISM Improved Complex Network

LI Liang; WANG Qirui; GUO Ruipeng; ZHOU Ziyuan

doi:10.3724/j.gyjzG25031908

Volume 55 Issue 12

Dec. 2025

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2025 > 55(12): 96-106

LI Liang, WANG Qirui, GUO Ruipeng, ZHOU Ziyuan. Research on Fire Risk Identification and Evolution in University Personnel Intensive Places Based on D-ISM Improved Complex Network[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(12): 96-106. doi: 10.3724/j.gyjzG25031908

Citation:

LI Liang, WANG Qirui, GUO Ruipeng, ZHOU Ziyuan. Research on Fire Risk Identification and Evolution in University Personnel Intensive Places Based on D-ISM Improved Complex Network[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(12): 96-106. doi: 10.3724/j.gyjzG25031908

Citation:

LI Liang, WANG Qirui, GUO Ruipeng, ZHOU Ziyuan. Research on Fire Risk Identification and Evolution in University Personnel Intensive Places Based on D-ISM Improved Complex Network[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(12): 96-106. doi: 10.3724/j.gyjzG25031908

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Research on Fire Risk Identification and Evolution in University Personnel Intensive Places Based on D-ISM Improved Complex Network

doi: 10.3724/j.gyjzG25031908

1. Student Affairs Office of Xi’an University of Technology，Xi’an 710048，China;
2. School of Civil and Architectural Engineering of Xi’an University of Technology，Xi’an 710048，China

Received Date: 2025-03-19
Available Online: 2026-01-06
Publish Date: 2025-12-20

Abstract

Abstract

In order to effectively reduce the risk of fire in densely populated areas of universities and meet the requirements of refined fire safety management， this study employs Grounded Theory to systematically identify fire risk factors and establishes a complex network of fire risk evolution based on the DEMATEL-ISM （D-ISM） model. It addresses the problem of separating the analysis process of key risk nodes and risk propagation paths in traditional methods and classifies and analyzes risk factors by incorporating quantitative indicators. The main conclusions include： 1） The proposed three-tiered framework （core layer， indirect layer， direct layer） establishes a novel complex network architecture that effectively supports a stratified risk assessment system. 2） High-risk coupling characteristics in crowded campus areas exhibit evolutionary complexity originating from indirect-layer factors. 3） A progressive evolution pattern emerges from the core to the indirect and direct layers， with distinct risk features at each level enabling targeted control strategies through stratified classification.
- densely populated areas in universities,
- complex network,
- fire risk identification,
- risk evolution path

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References(40)

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