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  11. Multisource Information Fusion For Safety Risk Assessment In Complex Projects Considering Dependence And Uncertainty

Multisource information fusion for safety risk assessment in complex projects considering dependence and uncertainty

Kai Guo1, Limao Zhang2

  • 1School of Public Administration, Nanjing Normal University, Nanjing, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|October 10, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Tunneling projects face leakage risks due to uncertainties. A novel hybrid approach using copula theory, cloud models, and risk matrices effectively assesses these risks, identifying waterproof materials as a key concern.

Area of Science:

  • Civil Engineering
  • Risk Management
  • Data Science

Background:

  • Tunneling projects are vital for infrastructure, but leakage risks pose significant challenges.
  • Uncertainty and fuzziness in risk factors complicate accurate assessment.

Purpose of the Study:

  • To propose a hybrid approach for robust tunnel leakage risk assessment.
  • To explore factor dependence and fuse multisource information effectively.

Main Methods:

  • Integration of copula theory, cloud model, and risk matrix.
  • Development of a risk index system with nine critical factors.
  • Application of Sobol-enabled global sensitivity analysis (GSA).

Main Results:

  • Identified risk statuses for three tunnel sections as Grade I (safe), II (low-risk), and III (medium-risk).
Keywords:
cloud modelcopula theoryinformation fusionrisk assessment

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  • Waterproof material degradation identified as a critical factor impacting tunnel sections.
  • Confirmed strong interactions between influential factors and the necessity of studying factor dependence.

    • Conclusions:

    • The hybrid copula-cloud model provides a robust method for tunnel risk assessment, considering factor dependence and uncertainty.
    • The proposed approach enhances understanding of risk trends and factor contributions.
    • The neutral risk matrix demonstrates robustness and high recognition capacity in risk assessment.
    risk matrix