Research on dam health diagnosis methodology based on a cloud model and enhanced Dempster-Shafer evidence theory
Yong Xiong, Kui Wang, Cheng Yang, Chuan Zhou, Mingjie Zhao · Insight - Non-Destructive Testing and Condition Monitoring · 2025
As the water conservancy sector progresses, ensuring the safety of reservoir dams has become a paramount concern. As currently employed in dam health diagnosis, Dempster-Shafer (D-S) evidence theory encounters challenges due to its synthesis rules, which may lead to issues such as an inability to apply certain rules or contradiction with human intuition. Consequently , this paper proposes an enhancement to D-S evidence theory by incorporating evidence credibility (Crd(mi)) obtained from evidence similarity coefficients and integrating indicator weights to form indicator fusion coefficients. Building upon this enhancement, a methodology for reservoir dam health diagnosis based on a cloud model and improved D-S evidence theory is introduced. A method combining subjective and objective weighting is employed to assign weights to dam diagnostic indicators using an analytic hierarchy process-entropy weight methodlargest difference method (AHP-EWM-LDM). Finally, dam health diagnosis is conducted on reservoir dams based on cloud modelling and enhanced D-S evidence theory. The feasibility of this method is verified.