Back analysis of rock mass parameters and initial stress for the Longtan tunnel in China

The Longtan tunnel is one key tunnel along the Hurong (Shanghai–Chengdu) national roadway in the Hubei province of China. This tunnel is a typical long and deep tunnel, with a length of approximately 8700 m and a depth of approximately 500 m. Because of the complicated geological conditions and high initial stress, to compute the stability of the surrounding rock, it is very important to back analyze the material parameters and initial stress from the measured displacements. Based on a novel evolutionary neural network proposed by the author, a new back analysis method is proposed that can be used to simultaneously determine the material parameters and the initial stress. Using this new back analysis method, some typical monitoring sections of the Longtan tunnel are computed. From numerical computations using the back analysis results, it is found that the stability of the tunnel is consistent with engineering practice and that the initial stresses determined from the back analysis coincide very well with the measured values. Therefore, using the proposed back analysis method, the relevant parameters for the entire tunnel can be conveniently obtained.

[1]  K. Takeuchi,et al.  Back analysis of measured displacements of tunnels , 1983 .

[2]  P. K. Kaiser,et al.  Stress determination by back-analysis of excavation-induced stress changes — a case study , 1990 .

[3]  Charles Leave.  Neural Networks: Algorithms, Applications and Programming Techniques , 1992 .

[4]  M. Hisatake.  Back analysis in tunneling , 1995 .

[5]  L. Yang,et al.  Back analysis of initial rock stresses and time-dependent parameters , 1996 .

[6]  Nii Attoh-Okine.  Artificial intelligence and mathematical methods in pavement and geomechanical systems , 1998 .

[7]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[8]  Xia-Ting Feng,et al.  Estimating mechanical rock mass parameters relating to the Three Gorges Project permanent shiplock using an intelligent displacement back analysis method , 2000 .

[9]  Yanjun Shang,et al.  Intelligent back analysis of displacements using precedent type analysis for tunneling , 2002 .

[10]  Xu Han,et al.  Neural identification of rock parameters using fuzzy adaptive learning parameters , 2003 .

[11]  Pierpaolo Oreste,et al.  Back-Analysis Techniques for the Improvement of the Understanding of Rock in Underground Constructions , 2005 .

[12]  Luqing Zhang,et al.  A displacement-based back-analysis method for rock mass modulus and horizontal in situ stress in tunneling – Illustrated with a case study , 2006 .

[13]  Hiroshi Morioka,et al.  Back-analysis of rock mass strength parameters using AE monitoring data , 2007 .

[14]  G. Wei.  STUDY ON SURROUNDING ROCK STABILITY OF LONGTAN TUNNEL BASED ON IN-SITU MONITORING MEASUREMENTS , 2007 .

[15]  Yuzhen Yu,et al.  An intelligent displacement back-analysis method for earth-rockfill dams , 2007 .

[16]  M. Hisatake,et al.  Three-dimensional back-analysis method for the mechanical parameters of the new ground ahead of a tunnel face , 2008 .

[17]  Mostafa Sharifzadeh,et al.  Displacement-based numerical back analysis for estimation of rock mass parameters in Siah Bisheh powerhouse cavern using continuum and discontinuum approach , 2012 .

[18]  Mohammad Ali Moridi,et al.  Time-dependent behavior of tunnel lining in weak rock mass based on displacement back analysis method , 2013 .

[19]  John C. Brigham,et al.  Inverse calculation of in situ stress in rock mass using the Surrogate-Model Accelerated Random Search algorithm , 2014 .

[20]  R. Azevedo.  Applications of Computational Mechanics in Geotechnical Engineering , 2020 .