基于LSTM的面板堆石坝变形预测实例应用与对比分析

Application and comparative analysis ofdeformation prediction of concrete face rockfill dam based on LSTM

堆石料流变效应引起的面板堆石坝工后变形持续积累,可能引起面板的压碎、开裂、错动或脱空,严重影响大坝的长效运行安全。基于深度学习(长短期记忆LSTM)技术,结合泽雅水库建库以来表面变形人工监测数据对大坝的长期变形行为进行了预测,同时针对沉降变形开展了统计模型预测与LSTM预测的对比分析。结果表明,经过数据清洗后LSTM方法具有良好的预测精度和稳定性,大坝表面沉降和水平位移在蓄水后35年~40年基本达到稳定,变形稳定时最大沉降量和水平位移分别为272.60 mm和178.92 mm。

The continuous accumulation of post-construction deformations in concrete face rockfill dams,induced by the rheological effects of rockfill materials,may lead to panel crushing,cracking,dislocation,or detachment,severely jeopardizing the long-term operational safety of the dam.This paper utilizes deep learning(Long Short-Term Memory,LSTM)techniques,in conjunction with manual monitoring data of surface deformations collected since the construction of the Zeya Reservoir,to predict the long-term deformation behavior of the dam.A comparative analysis between the predictions of the statistical model and the LSTM model for settlement deformations is also presented.The results demonstrate that,following data cleansing,the LSTM method exhibits good prediction accuracy and stability.The surface settlement and horizontal displacement of the dam essentially stabilize 35~40 years after impoundment,with the maximum settlement and horizontal displacement recorded at deformation stabilization being 272.60 mm and 178.92 mm,respectively.