结构工程师2026，Vol.42Issue(2)：69-82,14.DOI:10.15935/j.cnki.jggcs.202602.0008
基于LSTM的自复位中心支撑钢框架地震响应预测
Prediction of Seismic Response for Self-Centering Braced Steel Frames Based on LSTM
摘要
Abstract
To predict the response of self-centering braced steel frame structures under seismic loading,this paper employs a deep learning algorithm to construct a Long Short-Term Memory(LSTM)neural network model.This model establishes a nonlinear mapping relationship between input seismic acceleration and output structure seismic drift responses,exploring the effects of data window-size and dataset splitting ratios on the predictive performance of the model.The research findings indicate that the proposed LSTM neural network model demonstrates robust predictive capabilities,particularly in predicting the roof drift of the structure,achieving a peak relative error of 2.36%and a correlation coefficient of 0.94.Increasing the data window-size appropriately can still accurately predict both the roof drift and residual inter-story drift of the structure while enhancing prediction efficiency,without significantly altering the peak phase distribution of the predicted responses.For scenarios with limited sample sizes,when the known dataset splitting ratio falls within the range of 2∶1 to 3∶1,the model exhibits satisfactory overall predictive performance.When the training and validation sets adequately cover various time-step types,the model's overall generalization ability is effectively improved.However,it displays slightly less sensitivity to micro-data.关键词
自复位中心支撑钢框架/长短期记忆神经网络/地震响应预测/抗震性能Key words
self-centering braced steel frame/long short-term memory neural network/seismic response prediction/seismic performance分类
建筑与水利引用本文复制引用
史佳祺,王伟,胡书领,张瑞斌..基于LSTM的自复位中心支撑钢框架地震响应预测[J].结构工程师,2026,42(2):69-82,14.基金项目
国家自然科学基金项目(52378182,52308195) （52378182,52308195）
