Influence analysis and relationship evolution between construction parameters and ground settlements induced by shield tunneling under soil-rock mixed-face conditions

[1]  Zhijun Wu,et al.  Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method , 2021, Journal of Rock Mechanics and Geotechnical Engineering.

[2]  Manoj Khandelwal,et al.  An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass , 2021, Journal of Rock Mechanics and Geotechnical Engineering.

[3]  Odey Alshboul,et al.  Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression , 2021 .

[4]  H. Lyu,et al.  Data on performance and variation index for shield tunnelling through soft deposit , 2021, Data in brief.

[5]  J. W. Ju,et al.  Face stability conditions in granular soils during the advancing and stopping of earth-pressure-balanced-shield machine , 2021 .

[6]  Yujing Jiang,et al.  Deformation and mechanical characteristics of tunneling in squeezing ground: A case study of the west section of the Tawarazaka Tunnel in Japan , 2021 .

[7]  Kun Zhang,et al.  Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements , 2020 .

[8]  Zuyu Chen,et al.  Diagnosing tunnel collapse sections based on TBM tunneling big data and deep learning: A case study on the Yinsong Project, China , 2020 .

[9]  H. Lyu,et al.  Calculation of pressure on the shallow-buried twin-tunnel in layered strata , 2020 .

[10]  Hao Wu,et al.  Short-term rockburst risk prediction using ensemble learning methods , 2020, Natural Hazards.

[11]  G. Walton,et al.  A DEM-based study of the disturbance in dry sandy ground caused by EPB shield tunneling , 2020 .

[12]  Tatsuro Yamane,et al.  Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine , 2020, Comput. Aided Civ. Infrastructure Eng..

[13]  Ying-jie Wei,et al.  Effects of Soil Conditioning on Characteristics of a Clay-Sand-Gravel Mixed Soil Based on Laboratory Test , 2020, Applied Sciences.

[14]  Wenli Liu,et al.  Global sensitivity analysis of influential parameters for excavation stability of metro tunnel , 2020 .

[15]  Tommy H.T. Chan,et al.  Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study , 2020 .

[16]  S. Shen,et al.  A three-dimensional fluid-solid coupled numerical modeling of the barrier leakage below the excavation surface due to dewatering , 2020, Hydrogeology Journal.

[17]  Yin‐Fu Jin,et al.  A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest , 2020 .

[18]  Asok Ray,et al.  Statistical Analysis of the Capabilities of Various Pattern Recognition Algorithms for Fracture Detection Based on Monitoring Drilling Parameters , 2019, Rock Mechanics and Rock Engineering.

[19]  Pin Zhang,et al.  A novel feature selection method based on global sensitivity analysis with application in machine learning-based prediction model , 2019, Appl. Soft Comput..

[20]  Huai-Na Wu,et al.  Real-time analysis and regulation of EPB shield steering using Random Forest , 2019, Automation in Construction.

[21]  Pin Zhang,et al.  Prediction of shield tunneling-induced ground settlement using machine learning techniques , 2019, Frontiers of Structural and Civil Engineering.

[22]  Yu-Lin Lee,et al.  Incremental procedure method for the analysis of ground reaction due to excavation of a circular tunnel by considering the effect of overburden depth , 2019, Tunnelling and Underground Space Technology.

[23]  Miroslaw J. Skibniewski,et al.  Visibility graph analysis on time series of shield tunneling parameters based on complex network theory , 2019, Tunnelling and Underground Space Technology.

[24]  Xin Kang,et al.  Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods , 2019, Soils and Foundations.

[25]  Annan Zhou,et al.  Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm , 2019, Applied Sciences.

[26]  S. Saab,et al.  Improving the capability of detecting joints and fractures in rock mass from roof bolt drilling data by using wavelet analysis , 2018, International Journal of Oil, Gas and Coal Technology.

[27]  Arul Arulrajah,et al.  Prediction Model of TBM Disc Cutter Wear During Tunnelling in Heterogeneous Ground , 2018, Rock Mechanics and Rock Engineering.

[28]  Arul Arulrajah,et al.  Evaluation of ground loss ratio with moving trajectories induced in double-O-tube (DOT) tunnelling , 2018, Canadian Geotechnical Journal.

[29]  J. Rostami,et al.  Parametric study of the impacts of various geological and machine parameters on thrust force requirements for operating a single shield TBM in squeezing ground , 2018 .

[30]  Asok Ray,et al.  Application of Composite Indices for Improving Joint Detection Capabilities of Instrumented Roof Bolt Drills in Underground Mining and Construction , 2018, Rock Mechanics and Rock Engineering.

[31]  Nicolas Berthoz,et al.  TBM soft ground interaction: Experimental study on a 1 g reduced-scale EPBS model , 2018 .

[32]  Yin-Fu Jin,et al.  Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement , 2018 .

[33]  Fabrice Emeriault,et al.  Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method , 2017 .

[34]  Kenichi Soga,et al.  Long-term tunnel behaviour and ground movements after tunnelling in clayey soils , 2017 .

[35]  Günther Meschke,et al.  Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes , 2017, Computers & Structures.

[36]  Jamal Rostami,et al.  Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground , 2016 .

[37]  Fan Wang,et al.  Modeling of shield-ground interaction using an adaptive relevance vector machine , 2016 .

[38]  Climent Molins,et al.  3D analytical prediction of building damage due to ground subsidence produced by tunneling , 2015 .

[39]  Manchao He,et al.  Thermal image and spectral characterization of roadway failure process in geologically 45° inclined rocks , 2015 .

[40]  Saeid R. Dindarloo,et al.  Maximum surface settlement based classification of shallow tunnels in soft ground , 2015 .

[41]  H. Copur,et al.  Predicting performance of EPB TBMs by using a stochastic model implemented into a deterministic model , 2014 .

[42]  Kenichi Soga,et al.  Tunnelling-induced consolidation settlements in London Clay , 2013 .

[43]  Fan Wang,et al.  Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine , 2013 .

[44]  Ezio Giuriani,et al.  Photogrammetric measurements of the experimental analysis of a masonry façade subject to tunnelling-induced settlement , 2012 .

[45]  Satar Mahdevari,et al.  Prediction of tunnel convergence using Artificial Neural Networks , 2012 .

[46]  Jamal Rostami,et al.  TBM Performance Analysis in Pyroclastic Rocks: A Case History of Karaj Water Conveyance Tunnel , 2010 .

[47]  Pijush Samui,et al.  Least‐square support vector machine applied to settlement of shallow foundations on cohesionless soils , 2008 .

[48]  Qiuming Gong,et al.  In situ TBM penetration tests and rock mass boreability analysis in hard rock tunnels , 2007 .

[49]  K. Soga,et al.  Estimating the Effects of Tunneling on Existing Pipelines , 2005 .

[50]  R. Ribacchi,et al.  Influence of Rock Mass Parameters on the Performance of a TBM in a Gneissic Formation (Varzo Tunnel) , 2005 .

[51]  R. Teale.  The concept of specific energy in rock drilling , 1965 .

[52]  Shui-Long Shen,et al.  Prediction Model of Shield Performance During Tunneling via Incorporating Improved Particle Swarm Optimization Into ANFIS , 2020, IEEE Access.

[53]  Chen Yang,et al.  A case study of TBM performance prediction using field tunnelling tests in limestone strata , 2019, Tunnelling and Underground Space Technology.

[54]  Jamal Rostami,et al.  Application of new void detection algorithm for analysis of feed pressure and rotation pressure of roof bolters , 2017 .

[55]  Jamal Rostami,et al.  Evaluating the Suitability of Existing Rock Mass Classification Systems for TBM Performance Prediction by using a Regression Tree , 2017 .

[56]  Ke Ma,et al.  Effects of excavation unloading on the energy-release patterns and stability of underground water-sealed oil storage caverns , 2017 .

[57]  Jian Zhao,et al.  Analysis and prediction of TBM performance in blocky rock conditions at the Lötschberg Base Tunnel , 2013 .

[58]  F. Kulhawy,et al.  Factors Affecting Tbm Penetration Rates In Sedimentary Rocks , 1983 .