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Collapse risk assessment based on linear programming variable weight-cloud model.

Xiaoyi Zhou1, Ke Hu1, Tingqiang Zhou1

  • 1Department of Civil Engineering, Chongqing Three Gorges University, Wanzhou, Chongqing, China.

Plos One
|December 16, 2024
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Summary
This summary is machine-generated.

This study introduces a novel method for assessing geological collapse risk in mountainous regions. The new approach enhances accuracy by integrating linear programming and cloud models, identifying 65% of analyzed collapses as extremely dangerous.

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Area of Science:

  • Geological Engineering
  • Disaster Risk Assessment
  • Geotechnical Engineering

Background:

  • Collapse risk assessment is crucial for managing geological disasters in mountainous areas.
  • Traditional methods for collapse hazard evaluation face limitations due to indicator assignment variability and inability to handle data ambiguity and randomness simultaneously.
  • Existing research on collapse hazard evaluation is limited, hindering advancements in risk management.

Purpose of the Study:

  • To develop an advanced collapse risk assessment model by combining linear programming theory and cloud models.
  • To improve the accuracy and reliability of collapse risk evaluation for geological disaster prevention and control.
  • To address the limitations of traditional indicator assignment and comprehensive evaluation methods.

Main Methods:

  • Determining the weight interval of evaluation indices using an improved analytic hierarchy process, entropy weight method, and coefficient of variation method.
  • Employing a linear programming algorithm to select specific weights for each collapse sample to maximize risk within the interval.
  • Constructing a comprehensive evaluation model based on the cloud model to ascertain the risk level of collapses.

Main Results:

  • The developed method was applied to 20 collapse samples from the G4217 Wenchuan-Lixian section.
  • Evaluation results indicated that 13 samples (65%) were extremely dangerous, 2 (10%) highly dangerous, 4 (20%) moderately dangerous, and 1 (5%) lowly dangerous.
  • The method's reliability and rationality were validated through comparison with other evaluation techniques and field survey data, showing better alignment with actual conditions.

Conclusions:

  • The integrated linear programming and cloud model approach offers a more reliable and rational method for collapse risk assessment.
  • This advanced model effectively handles the ambiguity and randomness inherent in indicator data, outperforming traditional methods.
  • The findings provide a valuable tool for enhancing geological disaster prevention and control strategies in mountainous regions.