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eXtreme gradient boosting algorithm with machine learning: A review
ZA Ali, ZH Abduljabbar, HA Tahir, AB Sallow… - Academic Journal of …, 2023 - cir.nii.ac.jp
< jats: p> The primary task of machine learning is to extract valuable information from the
data that is generated every day, process it to learn from it, and take useful actions. Original …
data that is generated every day, process it to learn from it, and take useful actions. Original …
Application of artificial intelligence in geotechnical engineering: A state-of-the-art review
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 …
constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of …
Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration
Accurate prediction of ground vibration caused by blasting has always been a significant
issue in the mining industry. Ground vibration caused by blasting is a harmful phenomenon …
issue in the mining industry. Ground vibration caused by blasting is a harmful phenomenon …
Short-term rockburst prediction in underground project: insights from an explainable and interpretable ensemble learning model
Rockburst is a frequent challenge during tunnel and other underground construction and is
an extreme rock damage phenomenon. Therefore, it is very crucial to accurately estimate the …
an extreme rock damage phenomenon. Therefore, it is very crucial to accurately estimate the …
Develo** a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations
Blasting is still being considered to be one the most important applicable alternatives for
conventional excavations. Ground vibration generated due to blasting is an undesirable …
conventional excavations. Ground vibration generated due to blasting is an undesirable …
Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate
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 …
parameter for the successful accomplishment of a tunneling project, and the proper and …
[HTML][HTML] Predicting TBM penetration rate in hard rock condition: a comparative study among six XGB-based metaheuristic techniques
A reliable and accurate prediction of the tunnel boring machine (TBM) performance can
assist in minimizing the relevant risks of high capital costs and in scheduling tunneling …
assist in minimizing the relevant risks of high capital costs and in scheduling tunneling …
Prediction and analysis of train arrival delay based on XGBoost and Bayesian optimization
R Shi, X Xu, J Li, Y Li - Applied Soft Computing, 2021 - Elsevier
Accurate train arrival delay prediction is critical for real-time train dispatching and for the
improvement of the transportation service. This study proposes a data-driven method that …
improvement of the transportation service. This study proposes a data-driven method that …
Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm
The squeezing behavior of surrounding rock can be described as the time-dependent large
deformation during tunnel excavation, which appears in special geological conditions, such …
deformation during tunnel excavation, which appears in special geological conditions, such …
Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost)
Abstract Machine learning algorithms have progressively become a part of landslide
susceptibility map** practices owing to their robustness in dealing with complicated and …
susceptibility map** practices owing to their robustness in dealing with complicated and …