Use of environmentally friendly approaches with the purpose of strengthening soil layers along with finding correlations between the mechanical characteristics of fiber-reinforced soils such as indirect tensile streng...Use of environmentally friendly approaches with the purpose of strengthening soil layers along with finding correlations between the mechanical characteristics of fiber-reinforced soils such as indirect tensile strength(ITS)and California bearing ratio(CBR)and as well as the evaluation of shear strength parameters obtained from the triaxial test would be very effective at geotechnical construction sites.This research was aimed at investigating the influence of natural fibers as sustainable ones including basalt(BS)and bagasse(BG)as well as synthetic polyester(PET)fibers on the strength behavior of clayey soil.To this end,the effects of various fiber contents(0.5%,1%and 2%)and lengths(2.5 mm,5 mm and 7.5 mm)were experimentally evaluated.By conducting ITS and CBR tests,it was found that increasing fiber content and length had a significant influence on CBR and ITS values.Moreover,2%of 7.5 mm-long fibers led to the largest values of CBR and ITS.The CBR values of soil reinforced with PET,BS,and BG fibers were determined as 19.17%,15.43%and 13.16%,respectively.The ITS values of specimens reinforced with PET,BS,and BG fibers were reported as 48.57 kPa,60.7 kPa and 47.48 kPa,respectively.The results of the triaxial compression test revealed that with the addition of BS fibers,the internal friction angle increased by about 100%,and with the addition of PET fibers,the cohesion increased by about 70%.Moreover,scanning electron microscope(SEM)analysis was employed to confirm the findings.The relationship between CBR and ITS values,obtained via statistical analysis and used for the optimum design of road pavement layers,demonstrated that these parameters had high correlation coefficients.The outcomes of multiple linear regression and sensitivity analysis also confirmed that the fiber content had a greater effect on CBR and ITS values than fiber length.展开更多
In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and...In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and environmental uncertainties,advanced landing control techniques are demanded to meet the high requirements of real-time performance and autonomy in the Moon landing missions.Deep reinforcement learning(DRL)algorithms have been recently developed for real-time optimal control but suffer the obstacles of slow convergence and difficult reward function design.To address these problems,a DRL algorithm is developed using an actor-indirect method architecture to achieve the optimal control of the Moon landing mission.In this DRL algorithm,an indirect method is employed to generate the optimal control actions for the deep neural network(DNN)learning,while the trained DNNs provide good initial guesses for the indirect method to promote the efficiency of training data generation.Through sufficient learning of the state-action relationship,the trained DNNs can approximate the optimal actions and steer the spacecraft to the target in real time.Additionally,a nonlinear feedback controller is developed to improve the terminal landing accuracy.Numerical simulations are given to verify the effectiveness of the proposed DRL algorithm and demonstrate the performance of the developed optimal landing controller.展开更多
文摘Use of environmentally friendly approaches with the purpose of strengthening soil layers along with finding correlations between the mechanical characteristics of fiber-reinforced soils such as indirect tensile strength(ITS)and California bearing ratio(CBR)and as well as the evaluation of shear strength parameters obtained from the triaxial test would be very effective at geotechnical construction sites.This research was aimed at investigating the influence of natural fibers as sustainable ones including basalt(BS)and bagasse(BG)as well as synthetic polyester(PET)fibers on the strength behavior of clayey soil.To this end,the effects of various fiber contents(0.5%,1%and 2%)and lengths(2.5 mm,5 mm and 7.5 mm)were experimentally evaluated.By conducting ITS and CBR tests,it was found that increasing fiber content and length had a significant influence on CBR and ITS values.Moreover,2%of 7.5 mm-long fibers led to the largest values of CBR and ITS.The CBR values of soil reinforced with PET,BS,and BG fibers were determined as 19.17%,15.43%and 13.16%,respectively.The ITS values of specimens reinforced with PET,BS,and BG fibers were reported as 48.57 kPa,60.7 kPa and 47.48 kPa,respectively.The results of the triaxial compression test revealed that with the addition of BS fibers,the internal friction angle increased by about 100%,and with the addition of PET fibers,the cohesion increased by about 70%.Moreover,scanning electron microscope(SEM)analysis was employed to confirm the findings.The relationship between CBR and ITS values,obtained via statistical analysis and used for the optimum design of road pavement layers,demonstrated that these parameters had high correlation coefficients.The outcomes of multiple linear regression and sensitivity analysis also confirmed that the fiber content had a greater effect on CBR and ITS values than fiber length.
基金This work is supported by the National Natural Science Foundation of China(Grants Nos.11672146 and 11432001).
文摘In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and environmental uncertainties,advanced landing control techniques are demanded to meet the high requirements of real-time performance and autonomy in the Moon landing missions.Deep reinforcement learning(DRL)algorithms have been recently developed for real-time optimal control but suffer the obstacles of slow convergence and difficult reward function design.To address these problems,a DRL algorithm is developed using an actor-indirect method architecture to achieve the optimal control of the Moon landing mission.In this DRL algorithm,an indirect method is employed to generate the optimal control actions for the deep neural network(DNN)learning,while the trained DNNs provide good initial guesses for the indirect method to promote the efficiency of training data generation.Through sufficient learning of the state-action relationship,the trained DNNs can approximate the optimal actions and steer the spacecraft to the target in real time.Additionally,a nonlinear feedback controller is developed to improve the terminal landing accuracy.Numerical simulations are given to verify the effectiveness of the proposed DRL algorithm and demonstrate the performance of the developed optimal landing controller.
