The ordinary cemented tailings backfill(CTB)is a cement-based composite prepared from tailings,cementitious materials,and water.In this study,a series of laboratory tests,including uniaxial compression,digital image c...The ordinary cemented tailings backfill(CTB)is a cement-based composite prepared from tailings,cementitious materials,and water.In this study,a series of laboratory tests,including uniaxial compression,digital image correlation measurement,and scanning electron microscope characteristics of fiber-reinforced CTB(FRCTB),was conducted to obtain the uniaxial compressive strength(UCS),failure evolution,and microstructural characteristics of FRCTB specimens.The results show that adding fibers could increase the UCS values of the CTB by 6.90%to 32.76%.The UCS value of the FRCTB increased with the increase in the polypropylene(PP)fiber content.Moreover,the reinforcement effect of PP fiber on the CTB was better than that of glass fiber.The addition of fiber could increase the peak strain of the FRCTB by0.39%to 1.45%.The peak strain of the FRCTB increased with the increase in glass fiber content.The failure pattern of the FRCTB was coupled with tensile and shear failure.The addition of fiber effectively inhibited the propagation of cracks,and the bridging effect of cracks by the fiber effectively improved the mechanical properties of the FRCTB.The findings in this study can provide a basis for the backfilling design and optimization of mine backfilling methods.展开更多
To study the mechanical responses of large cross-section tunnel reinforced by pretensioned rock bolts and anchor cables, an analytical model is proposed. Considering the interaction between rock mass and bolt-cable su...To study the mechanical responses of large cross-section tunnel reinforced by pretensioned rock bolts and anchor cables, an analytical model is proposed. Considering the interaction between rock mass and bolt-cable support, the strain softening characteristic of rock mass, the elastic-plastic characteristic of bolt-cable support, and the delay effect of installation are considered in the model. To solve the different mechanical cases of tunneling reinforced by bolt-cable support, an analytical approach has been put forward to get the solutions of stress and displacement associated with tunneling. The proposed analytical model is verified by numerical simulation. Moreover, parametric analysis is performed to study the effects of pretension force,cross-section area, length, and supporting density of bolt-cable support on tunnel reinforcement, which can provide references for determining these parameters in tunnel design. Based on the analytical model, a new Ground Response Curve(GRC)considering the reinforcement of bolt-cable support is obtained, which shows the pretension forces and the timely installation are important in bolt-cable support. In addition, the proposed model is applied to the analysis of the Great Wall Station Tunnel, a high-speed railway tunnel with a super large cross-section, which shows that the analytical model of bolt-cable support was a useful tool for preliminary design of large cross-section tunnel.展开更多
The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We pr...The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.展开更多
基金financially supported by the National Natural Science Foundation of China(No.51804017)the Fundamental Research Funds for Central Universities,China(No.FRF-TP-20-001A2)the State Key Laboratory of Silicate Materials for Architectures(Wuhan University of Technology)(No.SYSJJ2021-04)。
文摘The ordinary cemented tailings backfill(CTB)is a cement-based composite prepared from tailings,cementitious materials,and water.In this study,a series of laboratory tests,including uniaxial compression,digital image correlation measurement,and scanning electron microscope characteristics of fiber-reinforced CTB(FRCTB),was conducted to obtain the uniaxial compressive strength(UCS),failure evolution,and microstructural characteristics of FRCTB specimens.The results show that adding fibers could increase the UCS values of the CTB by 6.90%to 32.76%.The UCS value of the FRCTB increased with the increase in the polypropylene(PP)fiber content.Moreover,the reinforcement effect of PP fiber on the CTB was better than that of glass fiber.The addition of fiber could increase the peak strain of the FRCTB by0.39%to 1.45%.The peak strain of the FRCTB increased with the increase in glass fiber content.The failure pattern of the FRCTB was coupled with tensile and shear failure.The addition of fiber effectively inhibited the propagation of cracks,and the bridging effect of cracks by the fiber effectively improved the mechanical properties of the FRCTB.The findings in this study can provide a basis for the backfilling design and optimization of mine backfilling methods.
基金supported by the National Key Research and Development Program of China (Grant No. 2017YFC0805401)the National Natural Science Foundation of China (Grant No. 51738002)+1 种基金the China Railway Corporation Research and Development Program of Science and Technology (Grant No. 2014004-C)the Fundamental Research Funds for the Central Universities (Grant No. C17JB00030)。
文摘To study the mechanical responses of large cross-section tunnel reinforced by pretensioned rock bolts and anchor cables, an analytical model is proposed. Considering the interaction between rock mass and bolt-cable support, the strain softening characteristic of rock mass, the elastic-plastic characteristic of bolt-cable support, and the delay effect of installation are considered in the model. To solve the different mechanical cases of tunneling reinforced by bolt-cable support, an analytical approach has been put forward to get the solutions of stress and displacement associated with tunneling. The proposed analytical model is verified by numerical simulation. Moreover, parametric analysis is performed to study the effects of pretension force,cross-section area, length, and supporting density of bolt-cable support on tunnel reinforcement, which can provide references for determining these parameters in tunnel design. Based on the analytical model, a new Ground Response Curve(GRC)considering the reinforcement of bolt-cable support is obtained, which shows the pretension forces and the timely installation are important in bolt-cable support. In addition, the proposed model is applied to the analysis of the Great Wall Station Tunnel, a high-speed railway tunnel with a super large cross-section, which shows that the analytical model of bolt-cable support was a useful tool for preliminary design of large cross-section tunnel.
基金supported by Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows (No. BX202210)。
文摘The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.
