Application of deep learning algorithms in geotechnical engineering: a short critical review

W Zhang, H Li, Y Li, H Liu, Y Chen, X Ding - Artificial Intelligence Review, 2021 - Springer
With the advent of big data era, deep learning (DL) has become an essential research
subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful …

Application of artificial intelligence in geotechnical engineering: A state-of-the-art review

A Baghbani, T Choudhury, S Costa, J Reiner - Earth-Science Reviews, 2022 - Elsevier
Geotechnical engineering deals with soils and rocks and their use in engineering
constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of …

[HTML][HTML] Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

W Zhang, C Wu, H Zhong, Y Li, L Wang - Geoscience Frontiers, 2021 - Elsevier
Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great
concern in geotechnical engineering practice. This study applies novel data-driven extreme …

Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost …

A Shehadeh, O Alshboul, RE Al Mamlook… - Automation in …, 2021 - Elsevier
It is challenging to develop accurate models for heavy construction equipment residual
value prediction using conventional approaches. This article proposes three Machine …

Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

J Zhou, Y Qiu, S Zhu, DJ Armaghani, C Li… - … Applications of Artificial …, 2021 - Elsevier
The advance rate (AR) of a tunnel boring machine (TBM) in hard rock condition is a key
parameter for the successful accomplishment of a tunneling project, and the proper and …

Ensemble extreme gradient boosting based models to predict the bearing capacity of micropile group

M Esmaeili-Falak, RS Benemaran - Applied Ocean Research, 2024 - Elsevier
In most cases in which non-allowable settlement or losing of bearing capacity has been
encountered in geotechnical engineering, employing micropile usually leads to satisfactory …

[HTML][HTML] Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization

J Zhou, Y Qiu, S Zhu, DJ Armaghani, M Khandelwal… - Underground …, 2021 - Elsevier
The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key
parameter in the successful implementation of tunneling engineering. In this study, we …

hyOPTXg: OPTUNA hyper-parameter optimization framework for predicting cardiovascular disease using XGBoost

P Srinivas, R Katarya - Biomedical Signal Processing and Control, 2022 - Elsevier
Cardiovascular disease is a dangerous disorder that causes the most significant number of
deaths across the world. In the past years, researchers proposed several automated …

State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction

SC Jong, DEL Ong, E Oh - Tunnelling and Underground Space Technology, 2021 - Elsevier
There has been an increasing demand for underground construction due to urbanization
and limited land in metropolitan cities in the recent years. However, the behavior of …

A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the **'an metro, China

XD Bai, WC Cheng, G Li - Acta geotechnica, 2021 - Springer
Complex geological conditions and/or inappropriate shield tunnel boring machine (TBM)
operation can significantly degrade both the excavation and safety of tunnel construction. In …