三峡大学学报（自然科学版）2024，Vol.46Issue(1)：63-70,8.DOI:10.13393/j.cnki.issn.1672-948X.2024.01.010
基于深度学习的软基管廊结构性能预测
Performance Prediction of Utility Tunnel for Soft Soil Foundation During Operation Period Based on Depth Learning
摘要
Abstract
The underground utility tunnel is generally affected by the micro disturbance of vehicle load during the operation period.Tracking the micro abnormal deformation of the structure under this environment and judging the possible anomalies(similar to differential settlement,etc.)are of great significance to accurately predict the safety of the utility tunnel.For the utility tunnel project in Suqian High-speed Railway Business District,Jiangsu Province,field monitoring research on the utility tunnel structure was carried out.A big data set composed of the measured vehicle load,time,and other disturbance factors,as well as the structure responses(i.e.,structure settlement displacement and structure stress)under their influence has been constructed.Based on the monitoring results,using the integrated deep learning model based on whale algorithm and depth belief network,to predict the safety of structure during the operation period,the data mining and learning on the constructed big data set has been conducted.The results show that the deep learning model can accurately predict the safety performance of the utility tunnel structure under vehicle load and other micro disturbances,and the model has good applicability.关键词
地下综合管廊/现场监测/集成深度学习模型/车辆荷载/安全性预测Key words
underground utility tunnel/field monitoring/integrated deep learning model/vehicle load/safety prediction分类
建筑与水利引用本文复制引用
蔡丹丹,高玮,王森,杨鹏宇,葛双双,马鹏飞..基于深度学习的软基管廊结构性能预测[J].三峡大学学报（自然科学版）,2024,46(1):63-70,8.基金项目
国家自然科学基金项目(41831278) （41831278）
宿迁市交通运输科技与成果转化项目(Sqjt2020-18) （Sqjt2020-18）
