Purpose–The wavelet neural network(WNN)has the drawbacks of slow convergence speed and easy falling into local optima in data prediction.Although the artificial bee colony(ABC)algorithm has strong global optimization...Purpose–The wavelet neural network(WNN)has the drawbacks of slow convergence speed and easy falling into local optima in data prediction.Although the artificial bee colony(ABC)algorithm has strong global optimization ability and fast convergence speed,it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.Design/methodology/approach–This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model.Based on the example of the Jinan Yuhan underground tunnel project,the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed,and the analysis results are compared with the actual detection amount.Findings–The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data,with a maximum relative error of only 4.73%.On this basis,the results show that the statistical features of ABC-WNN are the lowest,with the errors at 0.566 and 0.573,compared with the single back propagation(BP)neural network model and WNN model.Therefore,it can be derived that the ABC-WNN model has higher prediction accuracy,better computational stability and faster convergence speed for deformation.Originality/value–This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels.This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multiarch tunnels and small clearance tunnels.It can provide a new and effective way for deformation prediction in similar projects.展开更多
During tunneling in loose grounds, the ground deformation caused by drillings around the tunnel, leads to land subsidence and the adjacent tunnel which would affect tunnel structure and surrounding structures. In such...During tunneling in loose grounds, the ground deformation caused by drillings around the tunnel, leads to land subsidence and the adjacent tunnel which would affect tunnel structure and surrounding structures. In such situations it is necessary to improve the properties of the ground prior to drilling operations. In order to acquire tunnel face stability during excavation operations in areas with loose soil fault or areas with lack of adhesion, there are various methods such as split cross drilling, frame holder or auxiliary pre-holding methods such as umbrella arch method;pre-holding methods must provide safety when drilling and must be affordable, economically. In this study, we assessed the previous studies on methods and behaviors of umbrella arch strategy in reinforcing the concrete tunnels, reached the purpose with experimental and numerical methods and offered the latest design achievements, implementation progresses and analysis in relation with this method.展开更多
基金funded by the Natural Science Foundation of Hebei Province(No:E2020210068)Project of Science and Technology Research and Development Program of China National Railway Group Co.,Ltd.(No:N2020G009).
文摘Purpose–The wavelet neural network(WNN)has the drawbacks of slow convergence speed and easy falling into local optima in data prediction.Although the artificial bee colony(ABC)algorithm has strong global optimization ability and fast convergence speed,it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.Design/methodology/approach–This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model.Based on the example of the Jinan Yuhan underground tunnel project,the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed,and the analysis results are compared with the actual detection amount.Findings–The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data,with a maximum relative error of only 4.73%.On this basis,the results show that the statistical features of ABC-WNN are the lowest,with the errors at 0.566 and 0.573,compared with the single back propagation(BP)neural network model and WNN model.Therefore,it can be derived that the ABC-WNN model has higher prediction accuracy,better computational stability and faster convergence speed for deformation.Originality/value–This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels.This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multiarch tunnels and small clearance tunnels.It can provide a new and effective way for deformation prediction in similar projects.
文摘During tunneling in loose grounds, the ground deformation caused by drillings around the tunnel, leads to land subsidence and the adjacent tunnel which would affect tunnel structure and surrounding structures. In such situations it is necessary to improve the properties of the ground prior to drilling operations. In order to acquire tunnel face stability during excavation operations in areas with loose soil fault or areas with lack of adhesion, there are various methods such as split cross drilling, frame holder or auxiliary pre-holding methods such as umbrella arch method;pre-holding methods must provide safety when drilling and must be affordable, economically. In this study, we assessed the previous studies on methods and behaviors of umbrella arch strategy in reinforcing the concrete tunnels, reached the purpose with experimental and numerical methods and offered the latest design achievements, implementation progresses and analysis in relation with this method.
