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| 1 | Seismic design and analysis of underground structures显示文摘 | Youssef M.A. Hashash Jeffrey J. Hook Birger Schmidt John I-Chiang Yao | 2001 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2001，，4： | 6 |
| 2 | Numerical modeling of the effects of joint orientation on rock fragmentation by TBM cutters显示文摘 | Qiu-Ming Gong Jian Zhao Yu-Yong Jiao | 2004 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2004，，2： | 5 |
| 3 | Intelligent classification model of surrounding rock of tunnel using drilling and blasting method显示文摘Classification of surrounding rock is the cornerstone of tunnel design and construction.The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience.To minimize the effect of the empirical judgment on the accuracy of surrounding rock classification,it is necessary to reduce human participation.An intelligent classification technique based on information technology and artificial intelligence could overcome these issues.In this regard,using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou-Wanzhou high-speed railway in China,an intelligent-classification surrounding-rock database is constructed in this study.Based on a machine learning algorithm,an intelligent classification model is then developed,which has an overall accuracy of 91.9%.Finally,using the core of the model,the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated,and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding rock.This approach provides a foundation for the dynamic design and construction(both conventional and intelligent)of tunnels. | Mingnian Wang Siguang Zhao Jianjun Tong Zhilong Wang Meng Yao Jiawang Li Wenhao Yi | 2021 | Underground Space2021，6，5： | 5 |
| 4 | Fragility assessment of tunnels in soft soils using artificial neural networks显示文摘Recent earthquakes have shown that tunnels are prone to damage,posing a major threat to safety and having major cascading and socioeconomic impacts.Therefore,reliable models are needed for the seismic fragility assessment of underground structures and the quantitative evaluation of expected losses.Based on previous researches,this paper presented a probabilistic framework based on an artificial neural network(ANN),aiming at the development of fragility curves for circular tunnels in soft soils.Initially,a two-dimensional incremental dynamic analysis of the nonlinear soil-tunnel system was performed to estimate the response of the tunnel under ground shaking.The effects of soil-structure-interaction and the ground motion characteristics on the seismic response and the fragility of tunnels were adequately considered within the proposed framework.An ANN was employed to develop a probabilistic seismic demand model,and its results were compared with the traditional linear regression models.Fragility curves were generated for various damage states,accounting for the associated uncertainties.The results indicate that the proposed ANN-based probabilistic framework can results in reliable fragility models,having similar capabilities as the traditional approaches,and a lower computational cost is required.The proposed fragility models can be adopted for the risk analysis of typical circular tunnel in soft soils subjected to seismic loading,and they are expected to facilitate decision-making and risk management toward more resilient transport infrastructure. | Zhongkai Huang Sotirios A.Argyroudis Kyriazis Pitilakis Dongmei Zhang Grigorios Tsinidis | 2022 | Underground Space2022，7，2： | 4 |
| 5 | Development of a 3D modeling algorithm for tunnel deformation monitoring based on terrestrial laser scanning显示文摘Deformation monitoring is vital for tunnel engineering.Traditional monitoring techniques measure only a few data points,which is insufficient to understand the deformation of the entire tunnel.Terrestrial Laser Scanning(TLS)is a newly developed technique that can collect thousands of data points in a few minutes,with promising applications to tunnel deformation monitoring.The raw point cloud collected from TLS cannot display tunnel deformation;therefore,a new 3D modeling algorithm was developed for this purpose.The 3D modeling algorithm includes modules for preprocessing the point cloud,extracting the tunnel axis,performing coordinate transformations,performing noise reduction and generating the 3D model.Measurement results from TLS were compared to the results of total station and numerical simulation,confirming the reliability of TLS for tunnel deformation monitoring.Finally,a case study of the Shanghai West Changjiang Road tunnel is introduced,where TLS was applied to measure shield tunnel deformation over multiple sections.Settlement,segment dislocation and cross section convergence were measured and visualized using the proposed 3D modeling algorithm. | Xiongyao Xie Xiaozhi Lu | 2017 | Underground Space2017，2，1： | 4 |
| 6 | Numerical simulation of rock fragmentation process induced by two TBM cutters and cutter spacing optimization显示文摘 | Q.M. Gong J. Zhao A.M. Hefny | 2006 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2006，，3： | 4 |
| 7 | Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization显示文摘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 improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting(XGBoost)with Bayesian optimization(BO)to model the TBM AR.To develop the proposed models,1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia.The database consists of rock mass and intact rock features,including rock mass rating,rock quality designation,weathered zone,uniaxial compressive strength,and Brazilian tensile strength.Machine specifications,including revolution per minute and thrust force,were considered to predict the TBM AR.The accuracies of the predictive models were examined using the root mean squares error(RMSE)and the coefficient of determination(R^(2))between the observed and predicted yield by employing a five-fold cross-validation procedure.Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model.The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R^(2) values of 0.0967 and 0.9806(for the testing phase),respectively.The results demonstrated the merits of the proposed BO-XGBoost model.In addition,variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties. | Jian Zhou Yingui Qiu Shuangli Zhu Danial Jahed Armaghani Manoj Khandelwal Edy Tonnizam Mohamad | 2021 | Underground Space2021，6，5： | 4 |
| 8 | TBM penetration rate prediction based on the long short-term memory neural network显示文摘Tunnel boring machines(TBMs)are widely used in tunnel engineering because of their safety and efficiency.The TBM penetration rate(PR)is crucial,as its real-time prediction can reflect the adaptation of a TBM under current geological conditions and assist the adjustment of operating parameters.In this study,deep learning technology is applied to TBM performance prediction,and a PR prediction model based on a long short-term memory(LSTM)neuron network is proposed.To verify the performance of the proposed model,the machine parameters,rock mass parameters,and geological survey data from the water conveyance tunnel of the Hangzhou Second Water Source project were collected to form a dataset.Furthermore,2313 excavation cycles were randomly composed of training datasets to train the LSTM-based model,and 257 excavation cycles were used as a testing dataset to test the performance.The root mean square error and the mean absolute error of the proposed model are 4.733 and 3.204,respectively.Compared with Recurrent neuron network(RNN)based model and traditional time-series prediction model autoregressive integrated moving average with explanation variables(ARIMAX),the overall performance on proposed model is better.Moreover,in the rapidly increasing period of the PR,the error of the LSTM-based model prediction curve is significantly smaller than those of the other two models.The prediction results indicate that the LSTM-based model proposed herein is relatively accurate,thereby providing guidance for the excavation process of TBMs and offering practical application value. | Boyang Gao RuiRui Wang Chunjin Lin Xu Guo Bin Liu Wengang Zhang | 2021 | Underground Space2021，6，6： | 4 |
| 9 | Seismic design and analysis of underground structures显示文摘 | Youssef M.A. Hashash Jeffrey J. Hook Birger Schmidt John I-Chiang Yao | 2001 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2001，，4： | 4 |
| 10 | Development of a web-based information system for shield tunnel construction projects显示文摘 | Xiaojun Li Hehua Zhu | 2013 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2013，，： | 3 |
| 11 | Analytical solution for tunnelling-induced ground movement in clays显示文摘 | Kyung-Ho Park | 2004 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2004，，3： | 3 |
| 12 | Assessment of damage in mountain tunnels due to the Taiwan Chi-Chi Earthquake显示文摘 | W.L. Wang T.T. Wang J.J. Su C.H. Lin C.R. Seng T.H. Huang | 2001 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2001，，3： | 3 |
| 13 | Optimized back-analysis for tunneling-induced ground movement using equivalent ground loss model显示文摘 | Shue-Yeong Chi Jin-Ching Chern Chin-Cheng Lin | 2001 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2001，，3： | 3 |
| 14 | Analysis of a tunnel failure caused by leakage of the shield tail seal system显示文摘This paper presents an analysis of a tunnel failure accident during shield tunnel construction on Foshan Metro Line 2 in China.The failure is caused by the leakage of the multilayer seal system,which consists of several brush seals at the tail of the shield.Four different failure modes for the multilayer seal system are discussed.A simple structural analysis of the brush seals is then conducted,and failure mode 4(failure due to brush seal deformation)is identified as a major reason for the Foshan tunnel accident.A finite element method(FEM)analysis is employed to validate the conclusions drawn from the simple structural analysis of the brush seals. | Chao Yu Annan Zhou Jun Chen Arul Arulrajah Suksun Horpibulsuk | 2020 | Underground Space2020，5，2： | 3 |
| 15 | Errors in ground distortions due to settlement trough adjustment显示文摘 | T.B. Celestino R.A.M.P. Gomes A.A. Bortolucci | 2000 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2000，，1： | 3 |
| 16 | Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling显示文摘Estimating surface settlement induced by excavation construction is an indispensable task in tunneling,particularly for earth pressure balance(EPB)shield machines.In this study,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.Datasets from three tunnel construction projects in Singapore were used,with main input parameters of cover depth,advance rate,earth pressure,mean standard penetration test(SPT)value above crown level,mean tunnel SPT value,mean moisture content,mean soil elastic modulus,and grout pressure.The performances of these soft computing models were evaluated by comparing predicted deformation with measured values.Results demonstrate the acceptable accuracy of the model in predicting ground settlement,while XGBoost demonstrates a slightly higher accuracy.In addition,the ensemble method of XGBoost is more computationally efficient and can be used as a reliable alternative in solving multivariate nonlinear geo-engineering problems. | W.G.Zhang H.R.Li C.Z.Wu Y.Q.Li Z.Q.Liu H.L.Liu | 2021 | Underground Space2021，6，4： | 3 |
| 17 | Numerical modelling of the effects of joint spacing on rock fragmentation by TBM cutters显示文摘 | Q.M. Gong Y.Y. Jiao J. Zhao | 2005 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2005，，1： | 3 |
| 18 | Collapse mechanism of shallow tunnel based on nonlinear Hoek–Brown failure criterion显示文摘 | X.L. Yang F. Huang | 2011 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2011，，6： | 3 |
| 19 | Predictions of ground deformations in shallow tunnels in clay显示文摘 | Wei-I. Chou Antonio Bobet | 2002 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2002，，1： | 3 |
| 20 | On the 3D numerical modelling of the time-dependent development of the damage zone around underground galleries during and after excavation显示文摘 | F. Pellet M. Roosefid F. Deleruyelle | 2009 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2009，，6： | 3 |
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