Piles socketed in soft rock were traditionally regarded as end bearing piles, and the loads transferred from superstructure were assumed in design to be shouldered totally by the piles. This paper was designated to ...Piles socketed in soft rock were traditionally regarded as end bearing piles, and the loads transferred from superstructure were assumed in design to be shouldered totally by the piles. This paper was designated to deal with the interaction between the piles socketed in weak rock and surrounding soil through field measurement. The pile head reaction and ground pressure under piled raft foundation were monitored, respectively. The analysis of the data measured in situ shows the characteristics of the pile embedded in weak rock are similar to that of friction pile to some extent. The rock socketed pile, together with the surrounding soil, shoulders the weight of the superstructure. It is suggested that soil bearing should be considered in designing the soft rock socketed piles, which can make the design more economical.展开更多
A new comprehensive set of data（n = 178） is compiled by adding a data set（n = 72） collected by Arioglu et al.（2007） to the data set（n = 106） presented in Rezazadeh and Eslami（2017）. Then, the compiled data s...A new comprehensive set of data（n = 178） is compiled by adding a data set（n = 72） collected by Arioglu et al.（2007） to the data set（n = 106） presented in Rezazadeh and Eslami（2017）. Then, the compiled data set is evaluated regardless of the variation in lithology/strength. The proposed empirical equation in this study comprises a wider range of uniaxial compressive strength（UCS）（0.15 MPa 〈 σ_（rc） 〈156 MPa） and various rock types. Rock mass cuttability index（RMCI） is correlated with shaft resistance（r_s） to predict the shaft resistance of rock-socketed piles. The prediction capacity of the RMCI versus r_s equation is also found to be in a fair good agreement with the presented data in Rezazadeh and Eslami（2017）. Since the RMCI is a promising parameter in the prediction of shaft resistance, the researchers in the rock-socketed pile design area should consider this parameter in the further investigations.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
The proliferation of heterogeneous networks,such as the Internet of Things(IoT),unmanned aerial vehicle(UAV)networks,and edge networks,has increased the complexity of network operation and administration,driving the e...The proliferation of heterogeneous networks,such as the Internet of Things(IoT),unmanned aerial vehicle(UAV)networks,and edge networks,has increased the complexity of network operation and administration,driving the emergence of digital twin networks(DTNs)that create digital-physical network mappings.While DTNs enable performance analysis through emulation testbeds,current research focuses on network-level systems,neglecting equipment-level emulation of critical components like core switches and routers.To address this issue,we propose v Fabric(short for virtual switch),a digital twin emulator for high-capacity core switching equipment.This solution implements virtual switching and network processor(NP)chip models through specialized processes,deployable on single or distributed servers via socket communication.The v Fabric emulator can realize the accurate emulation for the core switching equipment with 720 ports and 100 Gbit/s per port on the largest scale.To our knowledge,this represents the first digital twin emulation framework specifically designed for large-capacity core switching equipment in communication networks.展开更多
文摘Piles socketed in soft rock were traditionally regarded as end bearing piles, and the loads transferred from superstructure were assumed in design to be shouldered totally by the piles. This paper was designated to deal with the interaction between the piles socketed in weak rock and surrounding soil through field measurement. The pile head reaction and ground pressure under piled raft foundation were monitored, respectively. The analysis of the data measured in situ shows the characteristics of the pile embedded in weak rock are similar to that of friction pile to some extent. The rock socketed pile, together with the surrounding soil, shoulders the weight of the superstructure. It is suggested that soil bearing should be considered in designing the soft rock socketed piles, which can make the design more economical.
基金support of Yapi Merkezi Construction and Industry Inc.,Istanbul,Turkey
文摘A new comprehensive set of data（n = 178） is compiled by adding a data set（n = 72） collected by Arioglu et al.（2007） to the data set（n = 106） presented in Rezazadeh and Eslami（2017）. Then, the compiled data set is evaluated regardless of the variation in lithology/strength. The proposed empirical equation in this study comprises a wider range of uniaxial compressive strength（UCS）（0.15 MPa 〈 σ_（rc） 〈156 MPa） and various rock types. Rock mass cuttability index（RMCI） is correlated with shaft resistance（r_s） to predict the shaft resistance of rock-socketed piles. The prediction capacity of the RMCI versus r_s equation is also found to be in a fair good agreement with the presented data in Rezazadeh and Eslami（2017）. Since the RMCI is a promising parameter in the prediction of shaft resistance, the researchers in the rock-socketed pile design area should consider this parameter in the further investigations.
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.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant Nos.62171085,62272428,62001087,U20A20156,and 61871097the ZTE Industry-University-Institute Cooperation Funds under Grant No.HC-CN-20220722010。
文摘The proliferation of heterogeneous networks,such as the Internet of Things(IoT),unmanned aerial vehicle(UAV)networks,and edge networks,has increased the complexity of network operation and administration,driving the emergence of digital twin networks(DTNs)that create digital-physical network mappings.While DTNs enable performance analysis through emulation testbeds,current research focuses on network-level systems,neglecting equipment-level emulation of critical components like core switches and routers.To address this issue,we propose v Fabric(short for virtual switch),a digital twin emulator for high-capacity core switching equipment.This solution implements virtual switching and network processor(NP)chip models through specialized processes,deployable on single or distributed servers via socket communication.The v Fabric emulator can realize the accurate emulation for the core switching equipment with 720 ports and 100 Gbit/s per port on the largest scale.To our knowledge,this represents the first digital twin emulation framework specifically designed for large-capacity core switching equipment in communication networks.
