The vibration response and noise caused by subway trains can affect the safety and comfort of superstructures.To study the dynamic response characteristics of subway stations and superstructures under train loads with...The vibration response and noise caused by subway trains can affect the safety and comfort of superstructures.To study the dynamic response characteristics of subway stations and superstructures under train loads with a hard combination,a numerical model is developed in this study.The indoor model test verified the accuracy of the numerical model.The influence laws of different hard combinations,train operating speeds and modes were studied and evaluated accordingly.The results show that the frequency corresponding to the peak vibration acceleration level of each floor of the superstructure property is concentrated at 10–20 Hz.The vibration response decreases in the high-frequency parts and increases in the lowfrequency parts with increasing distance from the source.Furthermore,the factors,such as train operating speed,operating mode,and hard combination type,will affect the vibration of the superstructure.The vibration response under the reversible operation of the train is greater than that of the unidirectional operation.The operating speed of the train is proportional to its vibration response.The vibration amplification area appears between the middle and the top of the superstructure at a higher train speed.Its vibration acceleration level will exceed the limit value of relevant regulations,and vibration-damping measures are required.Within the scope of application,this study provides some suggestions for constructing subway stations and superstructures.展开更多
Underground geotechnical engineering encounters persistent challenges in ensuring the stability and safety of surrounding rock structures, particularly within rocky tunnels. Rock reinforcement techniques, including th...Underground geotechnical engineering encounters persistent challenges in ensuring the stability and safety of surrounding rock structures, particularly within rocky tunnels. Rock reinforcement techniques, including the use of steel mesh, are critical to achieving this goal. However, there exists a knowledge gap regarding the comprehensive understanding of the mechanical behavior and failure mechanisms exhibited by steel mesh under diverse loading conditions. This study thoroughly explored the steel mesh's performance throughout the entire loading-failure process, innovating with detailed analysis and modeling techniques. By integrating advanced numerical modeling with laboratory experiments, the study examines the influence of varying reinforcement levels and geometric parameters on the steel mesh strength and deformation characteristics. Sensitivity analysis, employing gray correlation theory, identifies the key factors affecting the mesh performance, while a BP (Backpropagation) neural network model predicts maximum vertical deformation with high accuracy. The findings underscore the critical role of steel diameter and mesh spacing in optimizing peak load capacity, displacement, and energy absorption, offering practical guidelines for design improvements. The use of a Bayesian Regularization (BR) algorithm further enhances the predictive accuracy compared to traditional methods. This research provides new insights into optimizing steel mesh design for underground applications, offering an innovative approach to enhancing structural safety in geotechnical projects.展开更多
基金National Natural Science Foundation of China under Grant No.51578463。
文摘The vibration response and noise caused by subway trains can affect the safety and comfort of superstructures.To study the dynamic response characteristics of subway stations and superstructures under train loads with a hard combination,a numerical model is developed in this study.The indoor model test verified the accuracy of the numerical model.The influence laws of different hard combinations,train operating speeds and modes were studied and evaluated accordingly.The results show that the frequency corresponding to the peak vibration acceleration level of each floor of the superstructure property is concentrated at 10–20 Hz.The vibration response decreases in the high-frequency parts and increases in the lowfrequency parts with increasing distance from the source.Furthermore,the factors,such as train operating speed,operating mode,and hard combination type,will affect the vibration of the superstructure.The vibration response under the reversible operation of the train is greater than that of the unidirectional operation.The operating speed of the train is proportional to its vibration response.The vibration amplification area appears between the middle and the top of the superstructure at a higher train speed.Its vibration acceleration level will exceed the limit value of relevant regulations,and vibration-damping measures are required.Within the scope of application,this study provides some suggestions for constructing subway stations and superstructures.
基金funded by the National Natural Science Foundation of China(Grant No.52178396).
文摘Underground geotechnical engineering encounters persistent challenges in ensuring the stability and safety of surrounding rock structures, particularly within rocky tunnels. Rock reinforcement techniques, including the use of steel mesh, are critical to achieving this goal. However, there exists a knowledge gap regarding the comprehensive understanding of the mechanical behavior and failure mechanisms exhibited by steel mesh under diverse loading conditions. This study thoroughly explored the steel mesh's performance throughout the entire loading-failure process, innovating with detailed analysis and modeling techniques. By integrating advanced numerical modeling with laboratory experiments, the study examines the influence of varying reinforcement levels and geometric parameters on the steel mesh strength and deformation characteristics. Sensitivity analysis, employing gray correlation theory, identifies the key factors affecting the mesh performance, while a BP (Backpropagation) neural network model predicts maximum vertical deformation with high accuracy. The findings underscore the critical role of steel diameter and mesh spacing in optimizing peak load capacity, displacement, and energy absorption, offering practical guidelines for design improvements. The use of a Bayesian Regularization (BR) algorithm further enhances the predictive accuracy compared to traditional methods. This research provides new insights into optimizing steel mesh design for underground applications, offering an innovative approach to enhancing structural safety in geotechnical projects.
