水力发电学报2025，Vol.44Issue(1)：18-29,12.DOI:10.11660/slfdxb.20250102
SBL驱动的可解释性大坝变形区间预测模型
Sparse Bayesian learning-driven interpretable interval prediction model for dam deformation
摘要
Abstract
Deformation is a crucial indicator of the structural behaviors of a water dam.To address the issues of uncertainty quantification and model interpretability in dam deformation prediction,this study presents a sparse Bayesian learning(SBL)-driven model for interval prediction of dam deformation,considering both data noise and parameter uncertainty.We adopt a parallel Rao-3 algorithm and a cross-validation strategy to optimize adaptively the parameters of the kernel function,and then construct an optimized sparse Bayesian learning model that accurately captures the nonlinear relationship between the input variables(i.e.reservoir water level,temperature,and time-dependent variables)and output variables(i.e.dam displacements).For the variables that influence dam deformation,we calculate their feature importance by integrating global sensitivity analysis with this new prediction model,and gain valuable insights into the impact of feature variables on deformation prediction.A case study is made on an EDF concrete arch dam originating from the 16th International Benchmark Workshop on Numerical Analysis of Dams.The results demonstrate our prediction model outperforms the multiple linear regression statistical models,radial basis function networks,and Gaussian process regression models in terms of point prediction and interval prediction accuracy while maintaining good interpretability.关键词
大坝变形预测/区间预测/安全监控/稀疏贝叶斯学习/全局敏感度分析/可解释性Key words
dam deformation prediction/interval prediction/safety monitoring/sparse Bayesian learning/global sensitivity analysis/interpretability分类
建筑与水利引用本文复制引用
陈斯煜,顾冲时,盛金保,谷艳昌,林潮宁..SBL驱动的可解释性大坝变形区间预测模型[J].水力发电学报,2025,44(1):18-29,12.基金项目
国家自然科学基金项目(52309157 （）
52309151) （）
水利部水库大坝安全重点实验室开放研究基金(YK323007) （YK323007）
