水力发电2025，Vol.51Issue(5)：112-118,124,8.
基于ZOA-BiLSTM的重力坝变形预测模型
Gravity Dam Deformation Prediction Model Based on ZOA-BiLSTM
摘要
Abstract
To address the limitations in prediction accuracy inherent in traditional monitoring models for deformation prediction of concrete gravity dams,this study proposes a dam deformation prediction model based on the Zebra Optimization Algorithm(ZOA)and Bidirectional Long Short-Term Memory(BiLSTM)network.Initially,typical measurement point data within the deformation zone are selected.Subsequently,the hyperparameters of the model are iteratively optimized using the ZOA to enhance model performance.Finally,a high-precision prediction of concrete gravity dam deformation is achieved through the proposed ZOA-BiLSTM prediction workflow.Engineering case study demonstrates that the model's predictions align with the spatial distribution characteristics of dam deformation,offering a novel and effective methodology for monitoring the comprehensive safety status of dams.关键词
变形预测/重力坝/HST/BiLSTM/ZOAKey words
deformation prediction/gravity dam/HST/BiLSTM/ZOA分类
建筑与水利引用本文复制引用
黄建生,周站勇,林兴铖,黄之源,林舒婷,赵晨博..基于ZOA-BiLSTM的重力坝变形预测模型[J].水力发电,2025,51(5):112-118,124,8.基金项目
国家自然科学基金资助项目(52309151) （52309151）
国家重点研发计划(2022YFC3005403) （2022YFC3005403）
