Recently,the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly.However,updating the ground settlement profile,which is cruc...Recently,the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly.However,updating the ground settlement profile,which is crucial for determining potential damage to nearby infrastructures,has received limited attention.To address this,this paper proposes a physics-guided simplified model combined with a Bayesian updating framework to accurately predict the ground settlement profile.The advantage of this model is that it eliminates the need for complex finite element modeling and makes the updating framework user-friendly.Furthermore,the model is physically interpretable,which can provide valuable references for construction adjustments.The effectiveness of the proposed method is demonstrated through two field case studies,showing that it can yield satisfactory predictions for the settlement profile.展开更多
In order to ensure that the tunnel deformation and surface settlement are controlled within the allowable range during the construction process,the design unit has compiled technical measures and monitoring schemes fo...In order to ensure that the tunnel deformation and surface settlement are controlled within the allowable range during the construction process,the design unit has compiled technical measures and monitoring schemes for ground settlement control of this project.Based on the example of a shallow tunneling project on Subway line 8,this paper analyzes and discusses the shallow tunneling method in detail and puts forward corresponding technical measures for ground settlement control.展开更多
Prediction of tunneling-induced ground settlements is an essential task,particularly for tunneling in urban settings.Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground st...Prediction of tunneling-induced ground settlements is an essential task,particularly for tunneling in urban settings.Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures.Machine learning(ML)methods are becoming popular in many fields,including tunneling and underground excavations,as a powerful learning and predicting technique.However,the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods.Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small?In this study,seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation.These methods include multiple linear regression(MLR),decision tree(DT),random forest(RF),gradient boosting(GB),support vector regression(SVR),back-propagation neural network(BPNN),and permutation importancebased BPNN(PI-BPNN)models.All methods except BPNN and PI-BPNN are shallow-structure ML methods.The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability.The model accuracy is measured by the coefficient of determination(R2)of training and testing datasets,and the stability of a learning algorithm indicates robust predictive performance.Also,the quantile error(QE)criterion is introduced to assess model predictive performance considering underpredictions and overpredictions.Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy(0.9)and stability(3.0210^(-27)).Deep-structure ML models do not perform well for small datasets with relatively low model accuracy(0.59)and stability(5.76).The PI-BPNN architecture is proposed and designed for small datasets,showing better performance than typical BPNN.Six important contributing factors of ground settlements are identified,including tunnel depth,the distance between tunnel face and surface monitoring points(DTM),weighted average soil compressibility modulus(ACM),grouting pressure,penetrating rate and thrust force.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
A new technique for the analysis of the three-dimensional collapse failure mechanism and the ground surface settlements for the large-diameter shield tunnels were presented.The technique is based on a velocity field m...A new technique for the analysis of the three-dimensional collapse failure mechanism and the ground surface settlements for the large-diameter shield tunnels were presented.The technique is based on a velocity field model using more different truncated solid conical blocks to clarify the multiblock failure mechanism.Furthermore,the shape of blocks between the failure surface and the tunnel face was considered as an entire circle,and the supporting pressure was assumed as non-uniform distribution on the tunnel face and increased with the tunnel embedded depth.The ground surface settlements and failure mechanism above large-diameter shield tunnels were also investigated under different supporting pressures by the finite difference method.展开更多
At present the bored construction method is one of the main constructionmethods of metro and tunnel construction in China.The empirical estimated formuias oftunnel ground surface settlement using the bored constructio...At present the bored construction method is one of the main constructionmethods of metro and tunnel construction in China.The empirical estimated formuias oftunnel ground surface settlement using the bored construction method were obtained,combining the mechanical stimulant calculated result of tunnel model of different embed-ded depth,different cross section and different construction method and the actuai meas-urement data of Beijing metro construction.According to the regressed analysis of calcu-lated data,the calculated equations of ground surface settlement value and settlementrange of tunnel section under the condition of different embedded depth,different crosssection and different construction method were gained.Amongthem there are some em-pirical formulas can apply to the construction design of metro tunnel directly.展开更多
Since different underground engineering contains different geological conditions,structure and excavation methods,their disturbing degree on the ground also differs,but ground surface settlement caused by excavation h...Since different underground engineering contains different geological conditions,structure and excavation methods,their disturbing degree on the ground also differs,but ground surface settlement caused by excavation has one common point that they will form a ground surface settlement curve.Based on data statistical analysis,the article puts forward the relationship between point of inflection in digging tunnels with shallowburied method and the span of tunnel excavation against typical geological conditions in Changchun so as to predict the impact of tunnel excavation and improve reasonable ground surface settlement control standard.Research result will be useful for study on ground surface control standard in digging tunnels with shallow-buried method and setting settlement standard.展开更多
基金the financial support from the Guangdong Provincial Department of Science and Technology(Grant No.2022A0505030019)the Science and Technology Development Fund,Macao SAR,China(File Nos.0056/2023/RIB2 and SKL-IOTSC-2021-2023).
文摘Recently,the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly.However,updating the ground settlement profile,which is crucial for determining potential damage to nearby infrastructures,has received limited attention.To address this,this paper proposes a physics-guided simplified model combined with a Bayesian updating framework to accurately predict the ground settlement profile.The advantage of this model is that it eliminates the need for complex finite element modeling and makes the updating framework user-friendly.Furthermore,the model is physically interpretable,which can provide valuable references for construction adjustments.The effectiveness of the proposed method is demonstrated through two field case studies,showing that it can yield satisfactory predictions for the settlement profile.
文摘In order to ensure that the tunnel deformation and surface settlement are controlled within the allowable range during the construction process,the design unit has compiled technical measures and monitoring schemes for ground settlement control of this project.Based on the example of a shallow tunneling project on Subway line 8,this paper analyzes and discusses the shallow tunneling method in detail and puts forward corresponding technical measures for ground settlement control.
基金funded by the University Transportation Center for Underground Transportation Infrastructure(UTC-UTI)at the Colorado School of Mines under Grant No.69A3551747118 from the US Department of Transportation(DOT).
文摘Prediction of tunneling-induced ground settlements is an essential task,particularly for tunneling in urban settings.Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures.Machine learning(ML)methods are becoming popular in many fields,including tunneling and underground excavations,as a powerful learning and predicting technique.However,the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods.Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small?In this study,seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation.These methods include multiple linear regression(MLR),decision tree(DT),random forest(RF),gradient boosting(GB),support vector regression(SVR),back-propagation neural network(BPNN),and permutation importancebased BPNN(PI-BPNN)models.All methods except BPNN and PI-BPNN are shallow-structure ML methods.The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability.The model accuracy is measured by the coefficient of determination(R2)of training and testing datasets,and the stability of a learning algorithm indicates robust predictive performance.Also,the quantile error(QE)criterion is introduced to assess model predictive performance considering underpredictions and overpredictions.Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy(0.9)and stability(3.0210^(-27)).Deep-structure ML models do not perform well for small datasets with relatively low model accuracy(0.59)and stability(5.76).The PI-BPNN architecture is proposed and designed for small datasets,showing better performance than typical BPNN.Six important contributing factors of ground settlements are identified,including tunnel depth,the distance between tunnel face and surface monitoring points(DTM),weighted average soil compressibility modulus(ACM),grouting pressure,penetrating rate and thrust force.
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Project(41202220) supported by the National Natural Science Foundation of ChinaProject(2011YYL034) supported by the Fundamental Research Funds for the Central Universities,China
文摘A new technique for the analysis of the three-dimensional collapse failure mechanism and the ground surface settlements for the large-diameter shield tunnels were presented.The technique is based on a velocity field model using more different truncated solid conical blocks to clarify the multiblock failure mechanism.Furthermore,the shape of blocks between the failure surface and the tunnel face was considered as an entire circle,and the supporting pressure was assumed as non-uniform distribution on the tunnel face and increased with the tunnel embedded depth.The ground surface settlements and failure mechanism above large-diameter shield tunnels were also investigated under different supporting pressures by the finite difference method.
文摘At present the bored construction method is one of the main constructionmethods of metro and tunnel construction in China.The empirical estimated formuias oftunnel ground surface settlement using the bored construction method were obtained,combining the mechanical stimulant calculated result of tunnel model of different embed-ded depth,different cross section and different construction method and the actuai meas-urement data of Beijing metro construction.According to the regressed analysis of calcu-lated data,the calculated equations of ground surface settlement value and settlementrange of tunnel section under the condition of different embedded depth,different crosssection and different construction method were gained.Amongthem there are some em-pirical formulas can apply to the construction design of metro tunnel directly.
文摘Since different underground engineering contains different geological conditions,structure and excavation methods,their disturbing degree on the ground also differs,but ground surface settlement caused by excavation has one common point that they will form a ground surface settlement curve.Based on data statistical analysis,the article puts forward the relationship between point of inflection in digging tunnels with shallowburied method and the span of tunnel excavation against typical geological conditions in Changchun so as to predict the impact of tunnel excavation and improve reasonable ground surface settlement control standard.Research result will be useful for study on ground surface control standard in digging tunnels with shallow-buried method and setting settlement standard.
