H
Hongrui Li
Researcher at Chongqing University
Publications - 6
Citations - 529
Hongrui Li is an academic researcher from Chongqing University. The author has contributed to research in topics: Landslide & Computer science. The author has an hindex of 3, co-authored 3 publications receiving 114 citations.
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Application of deep learning algorithms in geotechnical engineering: a short critical review
TL;DR: This study presented the state of practice of DL in geotechnical engineering, and depicted the statistical trend of the published papers, as well as describing four major algorithms, including feedforward neural, recurrent neural network, convolutional neural network and generative adversarial network.
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Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling
TL;DR: P predictive models for assessing surface settlement caused by EPB tunneling were established based on extreme gradient boosting (XGBoost), artificial neural network, support vector machine, and multivariate adaptive regression spline, demonstrating acceptable accuracy of the model in predicting ground settlement.
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Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China
TL;DR: Wang et al. as discussed by the authors developed an ensemble learning-based method to predict the slope stability by introducing the random forest (RF) and extreme gradient boosting (XGBoost), which is applied to the stability prediction of 786 landslide cases in Yunyang County, Chongqing, China.
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Prediction of lining response for twin tunnels constructed in anisotropic clay using machine learning techniques
Tirai pembatas sholat,Wengang Zhang,Yongqin Li,Chongzhi Wu,Hongrui Li,A.T.C. Goh,Hanlong Liu +6 more
TL;DR: In this paper, an anisotropic soil model developed by the Norwegian Geotechnical Institute (NGI) based on the Active-Direct shear-Passive concept (ADP) was adopted to conduct finite element (FE) analyses.
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Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks
TL;DR: This study applies an advanced deep machine learning method called gated recurrent unit (GRU) to the displacement prediction of the Jiuxianping landslide, which is a typical reservoir landslide located in the Yunyang County of Chongqing, China.