User profiles for Yingui Qiu
 | Yingui QiuCentral South University Verified email at csu.edu.cn Cited by 2380 |
Short-term rockburst damage assessment in burst-prone mines: an explainable XGBOOST hybrid model with SCSO algorithm
Rockburst can cause significant damage to infrastructure and equipment, and pose a
substantial risk to the safety of mine workers. Effective prediction of short-term rockburst damage …
substantial risk to the safety of mine workers. Effective prediction of short-term rockburst damage …
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 …
State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting
The technological difficulties related with blasting operations have become increasingly
significant. It is crucial to give due consideration to the evaluation of rock fragmentation and the …
significant. It is crucial to give due consideration to the evaluation of rock fragmentation and the …
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 to …
issue in the mining industry. Ground vibration caused by blasting is a harmful phenomenon to …
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 reliable …
parameter for the successful accomplishment of a tunneling project, and the proper and reliable …
[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 projects. …
in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. …
Developing 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 random forest through the use of MVO, GWO and MFO in evaluating the stability of underground entry-type excavations
The stability evaluation of underground entry-type excavations is a prerequisite of the entry-type
mining method, which directly affects whether workers can be provided with a safe and …
mining method, which directly affects whether workers can be provided with a safe and …
[HTML][HTML] Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization
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 …
key parameter in the successful implementation of tunneling engineering. In this study, we …
Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential
Among the research hotspots in geological/geotechnical engineering, research on the
prediction of soil liquefaction potential is still limited. In this research, several machine-learning …
prediction of soil liquefaction potential is still limited. In this research, several machine-learning …