This study proposes a new post-tensioned precast bridge column(PT-PBC)with a socket connection.Compared to conventional PBCs connected by PT tendons,the combination of the PT tendons with the socket connection can avo...This study proposes a new post-tensioned precast bridge column(PT-PBC)with a socket connection.Compared to conventional PBCs connected by PT tendons,the combination of the PT tendons with the socket connection can avoid tensioning the PT tendons on site,which further accelerates construction speed while improving construction quality and safety.In addition,compared to conventional PBCs with a socket connection,a rocking interface can avoid the formation of a plastic hinge in a column,which greatly alleviates seismic damage to that area.One specimen for quasi-static testing is used to validate the feasibility of this connection type.Subsequently,finite element models(FEM)are established to systematically predict the responses of the proposed columns under lateral cyclic loading.The accuracy of the FEM is verified through quasistatic testing.Next,the influences of the key design parameters of the PT-PBC,including the area ratio and prestress level of the PT tendons,the area ratio of energy dissipation(ED)steel rebars,and the total axial compression ratio on the seismic performances of PT-PBC are systematically investigated.The use of shape memory alloy(SMA)rods as energy dissipation devices and their performances also are investigated.The results show that increasing the area ratio and prestress level of PT tendons has an overall positive impact on the self-centering capacity of the column.The prestress level of PT tendons should be kept between 35%and 55%,depending on different conditions.The total compression axial ratio of the columns should be maintained between 0.3 and 0.4.Both ED steel rebars and SMA rods can boost the column’s energy dissipation capacity,while SMA rods can reduce residual deformation due to their inherent mechanical properties.展开更多
The deep learning algorithm,which has been increasingly applied in the field of petroleum geophysical prospecting,has achieved good results in improving efficiency and accuracy based on test applications.To play a gre...The deep learning algorithm,which has been increasingly applied in the field of petroleum geophysical prospecting,has achieved good results in improving efficiency and accuracy based on test applications.To play a greater role in actual production,these algorithm modules must be integrated into software systems and used more often in actual production projects.Deep learning frameworks,such as TensorFlow and PyTorch,basically take Python as the core architecture,while the application program mainly uses Java,C#,and other programming languages.During integration,the seismic data read by the Java and C#data interfaces must be transferred to the Python main program module.The data exchange methods between Java,C#,and Python include shared memory,shared directory,and so on.However,these methods have the disadvantages of low transmission efficiency and unsuitability for asynchronous networks.Considering the large volume of seismic data and the need for network support for deep learning,this paper proposes a method of transmitting seismic data based on Socket.By maximizing Socket’s cross-network and efficient longdistance transmission,this approach solves the problem of inefficient transmission of underlying data while integrating the deep learning algorithm module into a software system.Furthermore,the actual production application shows that this method effectively solves the shortage of data transmission in shared memory,shared directory,and other modes while simultaneously improving the transmission efficiency of massive seismic data across modules at the bottom of the software.展开更多
基金Natural Science Foundation of China under Grant No.52178449,the Beijing Natural Science Foundation under Grant No.8234060the Innovation Center of Beijing Association for Science and Technology。
文摘This study proposes a new post-tensioned precast bridge column(PT-PBC)with a socket connection.Compared to conventional PBCs connected by PT tendons,the combination of the PT tendons with the socket connection can avoid tensioning the PT tendons on site,which further accelerates construction speed while improving construction quality and safety.In addition,compared to conventional PBCs with a socket connection,a rocking interface can avoid the formation of a plastic hinge in a column,which greatly alleviates seismic damage to that area.One specimen for quasi-static testing is used to validate the feasibility of this connection type.Subsequently,finite element models(FEM)are established to systematically predict the responses of the proposed columns under lateral cyclic loading.The accuracy of the FEM is verified through quasistatic testing.Next,the influences of the key design parameters of the PT-PBC,including the area ratio and prestress level of the PT tendons,the area ratio of energy dissipation(ED)steel rebars,and the total axial compression ratio on the seismic performances of PT-PBC are systematically investigated.The use of shape memory alloy(SMA)rods as energy dissipation devices and their performances also are investigated.The results show that increasing the area ratio and prestress level of PT tendons has an overall positive impact on the self-centering capacity of the column.The prestress level of PT tendons should be kept between 35%and 55%,depending on different conditions.The total compression axial ratio of the columns should be maintained between 0.3 and 0.4.Both ED steel rebars and SMA rods can boost the column’s energy dissipation capacity,while SMA rods can reduce residual deformation due to their inherent mechanical properties.
基金supported by the PetroChina Prospective,Basic,and Strategic Technology Research Project(No.2021ZG03-02 and No.2023DJ8402)。
文摘The deep learning algorithm,which has been increasingly applied in the field of petroleum geophysical prospecting,has achieved good results in improving efficiency and accuracy based on test applications.To play a greater role in actual production,these algorithm modules must be integrated into software systems and used more often in actual production projects.Deep learning frameworks,such as TensorFlow and PyTorch,basically take Python as the core architecture,while the application program mainly uses Java,C#,and other programming languages.During integration,the seismic data read by the Java and C#data interfaces must be transferred to the Python main program module.The data exchange methods between Java,C#,and Python include shared memory,shared directory,and so on.However,these methods have the disadvantages of low transmission efficiency and unsuitability for asynchronous networks.Considering the large volume of seismic data and the need for network support for deep learning,this paper proposes a method of transmitting seismic data based on Socket.By maximizing Socket’s cross-network and efficient longdistance transmission,this approach solves the problem of inefficient transmission of underlying data while integrating the deep learning algorithm module into a software system.Furthermore,the actual production application shows that this method effectively solves the shortage of data transmission in shared memory,shared directory,and other modes while simultaneously improving the transmission efficiency of massive seismic data across modules at the bottom of the software.
