Dam Health Diagnosis Model Based on Cumulative Distribution Function

Traditional methods for diagnosing dam health often rely on single point measurements, which require assumptions about the distributions of these measurements. These approaches fail to integrate multiple measured values for joint diagnosis and overlook the true distribution of the measured values, leading to potential misdiagnosis. This paper proposes a dam health diagnosis method based on kernel density estimation (KDE) and copula functions to address these limitations. The method incorporates a measurement analysis flow that extends from a single point to multiple points and establishes criteria for dam health diagnosis. In addition, this paper proposes to select the optimal copula function based on the Akaike information criterion (AIC). An engineering example is presented to demonstrate the proposed method's effectiveness in diagnosing a dam's health without assuming a specific measurement distribution function. This research contributes to the field of engineering safety management by enabling comprehensive dam health diagnosis from local dam states to the entire dam structure.

Automated summary

Traditional methods for diagnosing dam health based on single point measurements may lead to potential misdiagnosis and overlook the true distribution of measurements. This paper proposes a new method using kernel density estimation and copula functions to address these limitations and enable comprehensive dam health diagnosis. The method incorporates multiple measured values and establishes criteria for diagnosis, and also suggests the use of the optimal copula function based on the Akaike information criterion. An engineering example is presented to demonstrate the effectiveness of the proposed method in diagnosing dam health without assuming a specific measurement distribution function.