| 1 | Robust model for tunnel squeezing using Bayesian optimized classifiers with partially missing database显示文摘Accurately predicting and estimating the squeezing and ground response to tunneling remains challenging.Moreover,tunnel-squeezing hazards are much more likely to occur in deeply buried long tunnels with complex engineering-geological environments.There-fore,a high-performance predictive model for tunnel squeezing is necessary.A superior ensemble classifier is put forward in this study,which is composed of four individual classifiers(gradient boosting classifier,extra-trees classifier,AdaBoost classifier,and Logistic regression classifier)and two optimization algorithms(Bayesian optimization(BO)and sparrow search algorithm(SSA)).The training database covers five parameters:tunnel depth(H),rock tunneling quality index(Q),tunnel diameter(D),support stiffness(K),and strength stress ratio(SSR),about which the basic information is accessible at the early design phases.However,the dataset compiled from the literature is insufficient.Thus,the ten proposed methods are used to replace the missing values.During the model training pro-cess,BO shows its strong ability to optimize seventeen hyperparameters.When applied to tune the classifiers’weights,SSA achieves a fast and efficient performance.The novel Shapley Additive Explanations–LightGBM method indicates that the K is the most important input feature,followed by SSR,Q,H,and D,respectively.The ensemble classifier is then validated using the test set and additional his-torical case projects.The validation shows that the model can achieve an accuracy of 98%(i.e.,the error rate is 2%)on the test set,higher than those achieved by previous prediction models.Moreover,the predicted probability could provide warning information for timely support measures.Finally,the application results are illustrated through tests on the tunnel sections that have not yet been excavated in the line of the Sichuan–Tibet railway project.The applied predictive tendencies and laws are in line with the practical experience.In sum-mary,the proposed model’s prediction results are reasonable,and its prediction will be more accurate as more data is collected and trained for prewarning the tunnel squeezing hazard. | Yin Bo Xing Huang Yucong Pan Yanfang Feng Penghai Deng Feng Gao Ping Liu Quansheng Liu | 2023 | Underground Space2023，，3： | 0 |
| 2 | Integrated simulation and monitoring to analyze failure mechanism of the anti-dip layered slope with soft and hard rock interbedding显示文摘The significant difference between the mechanical properties of soft rock and hard rock results in the complexity of the failure mode of the anti-dip layered slope with soft and hard rock interbedding.In order to reveal the landslide mechanism,taking the north slope of Fushun West Open-pit Mine as an example,this paper analyzed the failure mechanism of different landslides with monitoring and field surveys,and simulated the evolution of landslides.The study indicated that when the green mudstone(hard rock)of the anti-dip slope contains siltized intercalations(soft rock),the existence of weak layers not only aggravates the toppling deformation of anti-dip layered slope with high dip,but also causes the shear failure of anti-dip layered slope with stable low dip.The shear failure including subsidence induced sliding and wedge failure mainly exists in the unloading zone of the slope.Its failure depth and failure time were far less than that of toppling failure.In terms of the development characteristics of deformation,toppling deformation has the long-term and progressive characteristics,but shear failure deformation has the abrupt and transient characteristics.This study has deepened the understanding of such slope landslide mechanism,and can provide reference for similar engineering. | Jinduo Li Yuan Gao Tianhong Yang Penghai Zhang Yong Zhao Wenxue Deng Honglei Liu Feiyue Liu | 2023 | International Journal of Mining Science and Technology2023，33，9： | 0 |
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