Recently,with the development of underground construction,multi-tunnel engineering has become a matter of concern since the interaction between tunnels at close ranges could cause additional deformation in strata as w...Recently,with the development of underground construction,multi-tunnel engineering has become a matter of concern since the interaction between tunnels at close ranges could cause additional deformation in strata as well as surrounding structures and even serious damage to surface buildings.These tunnel displacement and soil deformation problems can be effectively predicted using numerical methods considering the influence of various factors,such as the anisotropic nature of soft clay.To this end,the anisotropic elastoplastic constitutive S-CLAY1 is implemented in finite element code to investigate deformation problems in the strata and nearby structures caused by the excavation of multi-tunnels.This paper focuses in particular on configurations of two crossing tunnels.Multiple 3D numerical simulations using ABAQUS enable successive analyses conducted for tunnels at different spacings(1.5D,2.5D,3.5D and 4.5D,where D is the tunnel diameter)of configurations aligned vertically.The results,including the ground settlement,lining force and moment,and tunnel convergence,are analyzed.For each aspect,the most unfavorable case is determined by comparing the results of different simulations.This investigation can provide a reference for multi-tunnels design and construction.展开更多
Excessive ground surface settlement induced by pit excavation(i.e.braced excavation) can potentially result in damage to the nearby buildings and facilities.In this paper,extensive finite element analyses have been ca...Excessive ground surface settlement induced by pit excavation(i.e.braced excavation) can potentially result in damage to the nearby buildings and facilities.In this paper,extensive finite element analyses have been carried out to evaluate the effects of various structural,soil and geometric properties on the maximum ground surface settlement induced by braced excavation in anisotropic clays.The anisotropic soil properties considered include the plane strain shear strength ratio(i.e.the ratio of the passive undrained shear strength to the active one) and the unloading shear modulus ratio.Other parameters considered include the support system stiffness,the excavation width to excavation depth ratio,and the wall penetration depth to excavation depth ratio.Subsequently,the maximum ground surface settlement of a total of 1479 hypothetical cases were analyzed by various machine learning algorithms including the ensemble learning methods(extreme gradient boosting(XGBoost) and random forest regression(RFR)algorithms).The prediction models developed by the XGBoost and RFR are compared with that of two conventional regression methods,and the predictive accuracy of these models are assessed.This study aims to highlight the technical feasibility and applicability of advanced ensemble learning methods in geotechnical engineering practice.展开更多
This paper aims to establish an intelligent procedure that combines the observational method with the existing deep learning technique for updating deformation of braced excavation in clay.The gated recurrent unit(GRU...This paper aims to establish an intelligent procedure that combines the observational method with the existing deep learning technique for updating deformation of braced excavation in clay.The gated recurrent unit(GRU) neural network is adopted to formulate the forecast model and learn the potential rules in the field observations using the Nesterov-accelerated Adam(Nadam) algorithm.In the proposed procedure,the GRU-based forecast model is first trained based on the field data of previous and current stages.Then,the field data of the current stage are used as input to predict the deformation response of the next stage via the previously trained GRU-based forecast model.This updating process will loop up till the end of the excavation.This procedure has the advantage of directly predicting the deformation response of unexcavated stages based on the monitoring data.The proposed intelligent procedure is verified on two well-documented cases in terms of accuracy and reliability.The results indicate that both wall deflection and ground settlement are accurately predicted as the excavation proceeds.Furthermore,the advantages of the proposed intelligent procedure compared with the Bayesian/o ptimization updating are illustrated.展开更多
基金supported by the National Natural Science Foundation of China(No.51579179).
文摘Recently,with the development of underground construction,multi-tunnel engineering has become a matter of concern since the interaction between tunnels at close ranges could cause additional deformation in strata as well as surrounding structures and even serious damage to surface buildings.These tunnel displacement and soil deformation problems can be effectively predicted using numerical methods considering the influence of various factors,such as the anisotropic nature of soft clay.To this end,the anisotropic elastoplastic constitutive S-CLAY1 is implemented in finite element code to investigate deformation problems in the strata and nearby structures caused by the excavation of multi-tunnels.This paper focuses in particular on configurations of two crossing tunnels.Multiple 3D numerical simulations using ABAQUS enable successive analyses conducted for tunnels at different spacings(1.5D,2.5D,3.5D and 4.5D,where D is the tunnel diameter)of configurations aligned vertically.The results,including the ground settlement,lining force and moment,and tunnel convergence,are analyzed.For each aspect,the most unfavorable case is determined by comparing the results of different simulations.This investigation can provide a reference for multi-tunnels design and construction.
基金supported by the National Natural Science Foundation of China(Grant Nos.52078086 and 51778092)Program of Distinguished Young Scholars,Natural Science Foundation of Chongqing,China(Grant No.cstc2020jcyj-jq0087)。
文摘Excessive ground surface settlement induced by pit excavation(i.e.braced excavation) can potentially result in damage to the nearby buildings and facilities.In this paper,extensive finite element analyses have been carried out to evaluate the effects of various structural,soil and geometric properties on the maximum ground surface settlement induced by braced excavation in anisotropic clays.The anisotropic soil properties considered include the plane strain shear strength ratio(i.e.the ratio of the passive undrained shear strength to the active one) and the unloading shear modulus ratio.Other parameters considered include the support system stiffness,the excavation width to excavation depth ratio,and the wall penetration depth to excavation depth ratio.Subsequently,the maximum ground surface settlement of a total of 1479 hypothetical cases were analyzed by various machine learning algorithms including the ensemble learning methods(extreme gradient boosting(XGBoost) and random forest regression(RFR)algorithms).The prediction models developed by the XGBoost and RFR are compared with that of two conventional regression methods,and the predictive accuracy of these models are assessed.This study aims to highlight the technical feasibility and applicability of advanced ensemble learning methods in geotechnical engineering practice.
基金The financial supports provided by the Research Grants Council(RGC)of Hong Kong Special Administrative Region Government(HKSARG)of China(Grant Nos.15209119 and PolyU R5037-18F)Zhongtian Construction Group Co.Ltd.(Grant No.ZTCG-GDJTYJSJSFW-2020002)。
文摘This paper aims to establish an intelligent procedure that combines the observational method with the existing deep learning technique for updating deformation of braced excavation in clay.The gated recurrent unit(GRU) neural network is adopted to formulate the forecast model and learn the potential rules in the field observations using the Nesterov-accelerated Adam(Nadam) algorithm.In the proposed procedure,the GRU-based forecast model is first trained based on the field data of previous and current stages.Then,the field data of the current stage are used as input to predict the deformation response of the next stage via the previously trained GRU-based forecast model.This updating process will loop up till the end of the excavation.This procedure has the advantage of directly predicting the deformation response of unexcavated stages based on the monitoring data.The proposed intelligent procedure is verified on two well-documented cases in terms of accuracy and reliability.The results indicate that both wall deflection and ground settlement are accurately predicted as the excavation proceeds.Furthermore,the advantages of the proposed intelligent procedure compared with the Bayesian/o ptimization updating are illustrated.
