Abstract
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.
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Data used in the research work have been acknowledged, and data are available on request.
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This research is supported by the National Natural Science Foundation of China (52109156).
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All authors contributed to the study conception and design. Zhenxiang Jiang: proposing the idea, designing the method, collecting data, data analysis and the first draft of the manuscript. Bo Wu: graphical illustrations and full text proofreading. Hui Chen: review, advisor.
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Jiang, Z., Wu, B. & Chen, H. Dam Health Diagnosis Model Based on Cumulative Distribution Function. Water Resour Manage 37, 4293–4308 (2023). https://doi.org/10.1007/s11269-023-03553-6
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DOI: https://doi.org/10.1007/s11269-023-03553-6