An Explainable Probabilistic Model for Health Monitoring of Concrete Dam via Optimized Sparse Bayesian Learning and Sensitivity Analysis

Abstract

Machine learning has become increasingly popular for modeling dam behavior due to its ability to capture complex relationships between input parameters and dam behavior responses. However, the use of sophisticated machine learning methods for monitoring dam behaviors and making decisions is often hindered by model uncertainty and a lack of interpretability. This paper introduces a novel model for dam health monitoring, focused on monitoring radial displacement and seepage, using optimized sparse Bayesian learning and sensitivity analysis. The model hyperparameters are optimized using an intelligent optimization method integrating the multi-population Rao algorithm and blocked cross-validation, while sensitivity analysis is employed to calculate the relative importance of input variables for a better understanding of the dam’s state. The effectiveness of the proposed model is verified by using long-term monitoring data of a prototype concrete arch dam. The results confirm that the proposed model provides satisfactory performance on both the point predictions and the interval predictions for dam structural behaviors while obtaining effective explainability.

An Automated Framework for the Health Monitoring of Dams Using Deep Learning Algorithms and Numerical Methods

“…Intelligent algorithmic models are characterized by high computational efficiency and the strong fitting ability of nonlinear mapping [17,18]. Lin et al proposed a novel dam health monitoring model using optimized sparse Bayesian learning and sensitivity analysis, mainly for monitoring radial displacement and seepage [19]. Cheng et al compared the results of support vector machines, artificial neural networks, and hybrid artificial intelligence models in dam displacement prediction [20].…”

Identification of Trends in Dam Monitoring Data Series Based on Machine Learning and Individual Conditional Expectation Curves

“…Based on this work, Lin et al [14] proposed a method for explanation of an Optimized Sparse Bayesian Learning. The explanation was focused on the relative importance of the input variables.…”