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Privacy-aware community infrastructureChenlong Yue, Tugen Feng
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Uncertainty-based multi-objective optimization in twin tunnel design considering fluid-solid coupling
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Multi-objective robust optimization for enhanced safety in large-diameter tunnel construction with interactive and explainable AI
10.1016/j.ress.2023.109172 · doi-reference
Multi-objective optimization of shield construction parameters based on random forests and NSGA-II
10.1016/j.aei.2022.101751 · doi-reference
Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization
10.1016/j.jrmge.2023.02.014 · doi-reference
Integrated parameter optimization approach: just-in-time (JIT) operational control strategy for TBM tunnelling
10.1016/j.tust.2023.105040 · doi-reference
Multi-objective optimization control for tunnel boring machine performance improvement under uncertainty
10.1016/j.autcon.2022.104310 · doi-reference
Intelligent decision method for main control parameters of tunnel boring machine based on multi-objective optimization of excavation efficiency and cost
10.1016/j.tust.2021.104054 · doi-reference
A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM
10.1016/j.jrmge.2023.06.010 · doi-reference
TBM performance prediction using LSTM-based hybrid neural network model: case study of Baimang River tunnel project in Shenzhen
10.1016/j.undsp.2022.11.002 · doi-reference
Prediction of tunnel boring machine operating parameters using various machine learning algorithms
10.1016/j.tust.2020.103699 · doi-reference
Three hybrid intelligent models in estimating TBM advance rate
10.1007/s00366-018-0596-4 · doi-reference
Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition
10.1016/j.tust.2016.12.009 · doi-reference
Total power prediction of shield machine cutterhead based on hybrid neural networks and its confidence interval prediction
10.1016/j.engappai.2026.114375 · doi-reference
Factors influencing disc cutter wear
10.1002/geot.200800006 · doi-reference
Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network
10.1016/j.ymssp.2020.107386 · doi-reference
An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network
10.1016/j.ymssp.2021.108312 · doi-reference
Evaluation and optimization of the effective parameters on the shield TBM performance: torque and thrust—using discrete element method (DEM)
10.1007/s10706-020-01183-y · doi-reference
Utilizing rock mass properties for predicting TBM performance in hard rock condition
10.1016/j.tust.2007.04.011 · doi-reference
Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground
10.1016/j.tust.2016.01.009 · doi-reference
Development of a rock mass characteristics model for TBM penetration rate prediction
10.1016/j.ijrmms.2008.03.003 · doi-reference
Deep learning technologies for shield tunneling: challenges and opportunities
10.1016/j.autcon.2023.104982 · doi-reference
Dynamic prediction of mechanized shield tunneling performance
10.1016/j.autcon.2021.103958 · doi-reference
Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms
10.1016/j.gsf.2021.101177 · doi-reference
Shield tunneling and environment protection in Shanghai soft ground
10.1016/j.tust.2008.12.005 · doi-reference