Reliability Assessment of Long-Service Gravity Dams Based on Historical Water Level Monitoring Data

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書誌詳細
出版年:	Water vol. 17, no. 23 (2025), p. 3374-3391
第一著者:	Lu Yuzhou
その他の著者:	Qi Huijun, Li, Ziwei, Du Xiaohu, Lin Chaoning, Sheng Taozhen, Li Tongchun
出版事項:	MDPI AG
主題:	Load Dams Monte Carlo simulation Accuracy Datasets Random variables Parameter identification Decision making Water levels Hydrology International organizations Engineering Methods Probability distribution Hydraulics Risk assessment Parameter estimation
オンライン･アクセス:	Citation/Abstract Full Text + Graphics Full Text - PDF
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その他の書誌記述
抄録:	This paper addresses the challenge of systemic extreme risk in long-service gravity dams under human-controlled operation. It is the first study to construct a Generalized Extreme Value (GEV) distribution model using long-term operational monitoring data. The model, validated by multiple statistical tests and engineering boundary conditions, is then applied within a Response Surface Method-Monte Carlo (RSM-MC) reliability framework. Results indicate that the historical GEV model accurately captures the high-water-level tail characteristics and significantly overcomes the risk underestimation inherent in the uniform distribution model. Compared to the Log-Pearson Type III (Log-P3) design condition model, the GEV model yields a significantly lower probability of failure, e.g., the probability of cracking at the dam heel, the most sensitive failure mode, is reduced by nearly six times. This quantitative difference fully demonstrates GEV’s ability to precisely quantify the effective risk reduction achieved by human control, establishing a more scientific and realistic foundation for risk assessment of long-service gravity dams.
ISSN:	2073-4441
DOI:	10.3390/w17233374
ソース:	Publicly Available Content Database