Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data-Reference-Cited by-同舟云学术

Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data

Published:2023-04 Issue:4 Volume:15 Page:886-895
ISSN:1674-7755
Container-title:Journal of Rock Mechanics and Geotechnical Engineering
language:en
Short-container-title:Journal of Rock Mechanics and Geotechnical Engineering

Author:

Tan Xuyan^ORCID,Chen Weizhong,Zou Tao,Yang Jianping,Du Bowen

Funder

Chinese Academy of Sciences

Liverpool School of Tropical Medicine

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology

Reference38 articles.

1. Probabilistic identification of simulated damage on the Dowling Hall footbridge through Bayesian finite element model updating;Behmanesh;Struct. Control Health Monit.,2015

2. Artificial neural network surrogate modelling for real-time predictions and control of building damage during mechanised tunnelling;Cao;Adv. Eng. Software,2020

3. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling;Chung,2014

4. Development of load-temporal model to predict the further mechanical behaviors of tunnel structure under various boundary conditions;Du;Tunn. Undergr. Space Technol.,2021

5. Full-scale bridge damage identification using time series analysis of a dense array of geophones excited by drop weight;Farahani;Struct. Control Health Monit.,2016

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Leakage Identification of Underground Structures Using Classification Deep Neural Networks and Transfer Learning;Sensors;2024-08-28

2. Identification of anomaly of tunnel segment strain using an adaptive machine learning model;Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards;2024-08-27

3. Optimising creep constitutive modelling of layered soft rocks using particle swarm method;Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards;2024-08-27

4. Real-time monitoring of disc cutter wear in tunnel boring machines: A sound and vibration sensor-based approach with machine learning technique;Journal of Rock Mechanics and Geotechnical Engineering;2024-08

5. Performance Prediction for Steel Bridges Using SHM Data and Bayesian Dynamic Regression Linear Model: A Novel Approach;Journal of Bridge Engineering;2024-07