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Reliability Assessment of Long-Service Gravity Dams Based on Historical Water Level Monitoring Data
by
Du, Xiaohu
, Li, Ziwei
, Lu, Yuzhou
, Sheng, Taozhen
, Qi, Huijun
, Lin, Chaoning
, Li, Tongchun
in
Accuracy
/ China
/ Comparative analysis
/ Dams
/ Datasets
/ Decision making
/ Engineering
/ Hydraulics
/ Hydrology
/ International organizations
/ Load
/ Methods
/ Monte Carlo simulation
/ Parameter estimation
/ Parameter identification
/ Probability distribution
/ Random variables
/ Risk assessment
/ Water levels
2025
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Reliability Assessment of Long-Service Gravity Dams Based on Historical Water Level Monitoring Data
by
Du, Xiaohu
, Li, Ziwei
, Lu, Yuzhou
, Sheng, Taozhen
, Qi, Huijun
, Lin, Chaoning
, Li, Tongchun
in
Accuracy
/ China
/ Comparative analysis
/ Dams
/ Datasets
/ Decision making
/ Engineering
/ Hydraulics
/ Hydrology
/ International organizations
/ Load
/ Methods
/ Monte Carlo simulation
/ Parameter estimation
/ Parameter identification
/ Probability distribution
/ Random variables
/ Risk assessment
/ Water levels
2025
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Do you wish to request the book?
Reliability Assessment of Long-Service Gravity Dams Based on Historical Water Level Monitoring Data
by
Du, Xiaohu
, Li, Ziwei
, Lu, Yuzhou
, Sheng, Taozhen
, Qi, Huijun
, Lin, Chaoning
, Li, Tongchun
in
Accuracy
/ China
/ Comparative analysis
/ Dams
/ Datasets
/ Decision making
/ Engineering
/ Hydraulics
/ Hydrology
/ International organizations
/ Load
/ Methods
/ Monte Carlo simulation
/ Parameter estimation
/ Parameter identification
/ Probability distribution
/ Random variables
/ Risk assessment
/ Water levels
2025
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Reliability Assessment of Long-Service Gravity Dams Based on Historical Water Level Monitoring Data
Journal Article
Reliability Assessment of Long-Service Gravity Dams Based on Historical Water Level Monitoring Data
2025
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Overview
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.
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