Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods
This paper presents a brief review on major accidents and conducts bibliometric analysis of
risk assessment methods for excavation system in recent year. The summarization of …
risk assessment methods for excavation system in recent year. The summarization of …
Deep learning technologies for shield tunneling: Challenges and opportunities
C Zhou, Y Gao, EJ Chen, L Ding, W Qin - Automation in Construction, 2023 - Elsevier
Shield tunneling has been prevalent in tunnel construction since its introduction into the
field. To take advantage of the massive data generated during tunneling and to assist in …
field. To take advantage of the massive data generated during tunneling and to assist in …
[HTML][HTML] Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy
The application of ensemble learning models has been continuously improved in recent
landslide susceptibility research, but most studies have no unified ensemble framework …
landslide susceptibility research, but most studies have no unified ensemble framework …
[HTML][HTML] Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data
Slope failures lead to catastrophic consequences in numerous countries and thus the
stability assessment for slopes is of high interest in geotechnical and geological engineering …
stability assessment for slopes is of high interest in geotechnical and geological engineering …
Deep learning analysis for energy consumption of shield tunneling machine drive system
Inaccurate estimation of energy from the shield driving system may result in serious energy
loss and low tunneling efficiency. A deep learning network is developed in this study to …
loss and low tunneling efficiency. A deep learning network is developed in this study to …
Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning
The bidirectional long short-term memory (Bi-LSTM) network is an innovative computation
paradigm that learns bidirectional long-term dependencies between time steps and …
paradigm that learns bidirectional long-term dependencies between time steps and …
Fault prediction based on leakage current in contaminated insulators using enhanced time series forecasting models
To improve the monitoring of the electrical power grid, it is necessary to evaluate the
influence of contamination in relation to leakage current and its progression to a disruptive …
influence of contamination in relation to leakage current and its progression to a disruptive …
Deep reinforcement learning approach to optimize the driving performance of shield tunnelling machines
This paper proposes a deep reinforcement learning (DRL)-based model as a valuable tool
to improve the performance of the driving system (ie thrust force and cutterhead torque) of a …
to improve the performance of the driving system (ie thrust force and cutterhead torque) of a …
Prediction of shield machine posture using the GRU algorithm with adaptive boosting: A case study of Chengdu Subway project
H Xiao, Z Chen, R Cao, Y Cao, L Zhao… - Transportation Geotechnics, 2022 - Elsevier
Shield machine deviation from the design tunnel axis (DTA) causes dislocation and damage
of the segments and may lead to poor tunnel quality, which is a primary concern in tunnel …
of the segments and may lead to poor tunnel quality, which is a primary concern in tunnel …
[HTML][HTML] Tunnel boring machine vibration-based deep learning for the ground identification of working faces
Tunnel boring machine (TBM) vibration induced by cutting complex ground contains
essential information that can help engineers evaluate the interaction between a cutterhead …
essential information that can help engineers evaluate the interaction between a cutterhead …