Deep learning technologies for shield tunneling: Challenges and opportunities
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] Tunnelling-induced ground surface settlement: A comprehensive review with particular attention to artificial intelligence technologies
Shallow tunnels in urban areas are close to adjacent buildings and municipal pipelines.
Ground surface settlement (GSS) due to tunnelling can cause damage to those …
Ground surface settlement (GSS) due to tunnelling can cause damage to those …
[HTML][HTML] Comparison of machine learning methods for ground settlement prediction with different tunneling datasets
L Tang, SH Na - Journal of Rock Mechanics and Geotechnical …, 2021 - Elsevier
This study integrates different machine learning (ML) methods and 5-fold cross-validation
(CV) method to estimate the ground maximal surface settlement (MSS) induced by …
(CV) method to estimate the ground maximal surface settlement (MSS) induced by …
Deep learning-based prediction of steady surface settlement due to shield tunnelling
Predicting ground movement produced by shield tunnelling in densely built urban areas is of
practical significance. This study introduces an artificial intelligence method to predict the …
practical significance. This study introduces an artificial intelligence method to predict the …
[HTML][HTML] Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method
Accurate prediction of shield tunneling-induced settlement is a complex problem that
requires consideration of many influential parameters. Recent studies reveal that machine …
requires consideration of many influential parameters. Recent studies reveal that machine …
Surface settlement prediction for urban tunneling using machine learning algorithms with Bayesian optimization
This paper describes the prediction of settlements induced by urban area tunneling using
five machine learning (ML) algorithms. The settlement database, which was collected from a …
five machine learning (ML) algorithms. The settlement database, which was collected from a …
Artificial intelligence forecasting models of uniaxial compressive strength
The uniaxial compressive strength (UCS) is a vital rock geomechanical parameter widely
used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the …
used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the …
Optimized machine learning modelling for predicting the construction cost and duration of tunnelling projects
Predicting duration and cost of tunnelling projects is an essential factor in determining the
usefulness of a decision-making system. Therefore, research on the duration and cost of …
usefulness of a decision-making system. Therefore, research on the duration and cost of …
Machine learning techniques to predict rock strength parameters
To accurately estimate the rock shear strength parameters of cohesion (C) and friction angle
(φ), triaxial tests must be carried out at different stress levels so that a failure envelope can …
(φ), triaxial tests must be carried out at different stress levels so that a failure envelope can …
[HTML][HTML] Significance and methodology: Preprocessing the big data for machine learning on TBM performance
HH **ao, WK Yang, J Hu, YP Zhang, LJ **g… - Underground …, 2022 - Elsevier
This paper addresses the significance of preprocessing big data collected during a tunnel
boring machine (TBM) excavation before it is used for machine learning on various TBM …
boring machine (TBM) excavation before it is used for machine learning on various TBM …