结构工程师2026，Vol.42Issue(4)：62-71,10.DOI:10.15935/j.cnki.jggcs.202604.0008
基于DBO-LSTM模型的桥梁环境温度及其效应预测方法研究
A Hybrid DBO-LSTM Model for Predicting Bridge Ambient Temperature and Its Thermal Response
摘要
Abstract
To address the limitations of traditional bridge health monitoring systems and finite element methods,such as heavy computational demands and poor real-time performance,a hybrid prediction model based on Dung Beetle Optimization and Long Short-Term Memory networks(DBO-LSTM)is proposed for forecasting bridge ambient temperature and temperature-induced effects.The model utilizes the DBO algorithm to optimize three critical hyperparameters of the LSTM network:the number of hidden units,initial learning rate,and L2 regularization coefficient.This optimization enhances the model's ability to learn complex nonlinear dependencies in long-term monitoring data.Using one year of real-time monitoring data from the Chengmian Interchange mainline bridge,the model was applied to predict multiple indicators,including ambient temperature,expansion joint displacement,bottom beam strain,and pier top displacement.The results demonstrate high predictive performance,with coefficients of determination(R²)ranging from 0.966 to 0.995 and mean absolute percentage errors(MAPE)between 0.02 and 0.38.The predicted values closely match the actual measurements in both magnitude and trend.关键词
桥梁工程/温度效应/DBO-LSTM/长期监测/预测方法Key words
bridge engineering/temperature effect/DBO-LSTM/long-term monitoring/prediction technique分类
交通工程引用本文复制引用
肖冠英,文亚男,冯若骁,郑易涛..基于DBO-LSTM模型的桥梁环境温度及其效应预测方法研究[J].结构工程师,2026,42(4):62-71,10.基金项目
四川交投科研联合项目(编号:HT-GLQ2024-0055) （编号:HT-GLQ2024-0055）
