This paper reports a case study of progressive failure on a high cut slope due to continuous excavation. The characteristics of each stage failure induced by excavation are analyzed through geological investigation and field monitoring. The slope safety situation was monitored and analyzed. The monitoring results provide the important guidance for slope dynamic design and construction. It is important that the reinforcement of pre-stressed anchoring cables for the slope should be carried out after the slope excavation accomplished as soon as possible. Slope construction monitoring plays an important role in ensuring high slope safety. The results revealed that the slope instability was triggered by the continuous excavation of the slope, and the failure mode of slope is progressive sliding.
A Reinforcement Learning (RL) method applied to the dynamic load allocation in AGC system is presented. The problem can be modeled as a Markov Decision Process (MDP). The Q-learning algorithm as a model-free learning algorithm is introduced. It learns an optimal action strategy by experience from exploring an unknown system and getting rewards. Rewards are chosen to express how well actions control the system. The applications of the Q-learning algorithm to the twoarea power system model and China Southern Power Grid model are presented. The case study shows that the Q-learning algorithm enhances the performance of AGC system under CPS.Index Terms--Reinforcement learning; Q-learning algorithm; dynamic load allocation; MDP; CPS.
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