Future of machine learning in geotechnics
KK Phoon, W Zhang - … : Assessment and Management of Risk for …, 2023 - Taylor & Francis
Machine learning (ML) is widely used in many industries, resulting in recent interests to
explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms …
explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms …
Time capsule for geotechnical risk and reliability
This paper is motivated by the Time Capsule Project (TCP) of the International Society for
Soil Mechanics and Geotechnical Engineering (ISSMGE). The historical developments of …
Soil Mechanics and Geotechnical Engineering (ISSMGE). The historical developments of …
[HTML][HTML] Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification …
This study presents a framework for predicting geological characteristics based on
integrating a stacking classification algorithm (SCA) with a grid search (GS) and K-fold cross …
integrating a stacking classification algorithm (SCA) with a grid search (GS) and K-fold cross …
Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for …
In this study, it was investigated that how machine learning (ML) methods show performance
in different problems having different characteristics. Six ML approaches including Artificial …
in different problems having different characteristics. Six ML approaches including Artificial …
[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 …
Prediction of wall deflection induced by braced excavation in spatially variable soils via convolutional neural network
Recently, the random field finite element method (RF-FEM) has attracted significantly
increasing attention in the field of geotechnical engineering, especially for the purpose of …
increasing attention in the field of geotechnical engineering, especially for the purpose of …
Machine learning-based intelligent modeling of hydraulic conductivity of sandy soils considering a wide range of grain sizes
This study presents novel intelligent modeling of the hydraulic conductivity (k) of sandy soil
by employing machine learning (ML) algorithms ie, artificial neural network (ANN), multi …
by employing machine learning (ML) algorithms ie, artificial neural network (ANN), multi …
[HTML][HTML] Unconfined compressive strength of MICP and EICP treated sands subjected to cycles of wetting-drying, freezing-thawing and elevated temperature …
Microbial-induced carbonate precipitation (MICP) and enzyme-induced carbonate
precipitation (EICP) are two bio-cementation techniques, which are relatively new methods …
precipitation (EICP) are two bio-cementation techniques, which are relatively new methods …
[HTML][HTML] Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement
The influence of a deep excavation on existing shield tunnels nearby is a vital issue in
tunnelling engineering. Whereas, there lacks robust methods to predict excavation-induced …
tunnelling engineering. Whereas, there lacks robust methods to predict excavation-induced …
[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 …