Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretic...Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relationships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data. This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.展开更多
This study investigated the degradation mechanism of the surrounding rock of a heavy-haul railway under a water-rich condition,based on the construction of the Taihangshan tunnel for the Wari Railway,a heavy-haul rail...This study investigated the degradation mechanism of the surrounding rock of a heavy-haul railway under a water-rich condition,based on the construction of the Taihangshan tunnel for the Wari Railway,a heavy-haul railway that used standard construction practices for axle loads of 30 t.Remote monitoring demonstrated that the coupling effect between the dynamic load of a heavy-haul train and the groundwater leads to the deterioration and hollowing of the surrounding rock.This study clarified the void evolution process and deterioration mechanism of the basement rock under the comprehensive influence of the groundwater–train dynamic load using a refined discrete element numerical simulation.The results revealed that the groundwater was the primary influencing factor in the deterioration of the lower part of the heavy-haul railway tunnel.Rock particles were gradually lost under the effects of long-term erosion due to groundwater and heavy-haul trains,which inevitably damaged the basement rock after the construction was completed.Based on this observation,the critical conditions for the deterioration and attenuation law of the physical parameters of the basement rock were obtained.The results of this study can provide ideas and serve as a reference for the forecasting and disaster treatment of basement rock damage in heavy-haul railway tunnels.展开更多
基金supported by the National Natural Science Foundation of China (No.50609028)
文摘Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relationships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data. This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.
基金the National Natural Science Foundation of China(5108098,51508475)The Chongqing Education Commission science and technology research project(KJQN201901509)Sichuan University Key Laboratory Fundation of Bridge Nondestructive Testing and Engineering Calculation(2018QYJ06).
文摘This study investigated the degradation mechanism of the surrounding rock of a heavy-haul railway under a water-rich condition,based on the construction of the Taihangshan tunnel for the Wari Railway,a heavy-haul railway that used standard construction practices for axle loads of 30 t.Remote monitoring demonstrated that the coupling effect between the dynamic load of a heavy-haul train and the groundwater leads to the deterioration and hollowing of the surrounding rock.This study clarified the void evolution process and deterioration mechanism of the basement rock under the comprehensive influence of the groundwater–train dynamic load using a refined discrete element numerical simulation.The results revealed that the groundwater was the primary influencing factor in the deterioration of the lower part of the heavy-haul railway tunnel.Rock particles were gradually lost under the effects of long-term erosion due to groundwater and heavy-haul trains,which inevitably damaged the basement rock after the construction was completed.Based on this observation,the critical conditions for the deterioration and attenuation law of the physical parameters of the basement rock were obtained.The results of this study can provide ideas and serve as a reference for the forecasting and disaster treatment of basement rock damage in heavy-haul railway tunnels.
