Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses
بهینه ساز مبتنی بر یادگیری تقویتی برای بهبود پیش بینی پاسخ های ناشی از tunneling-2020
Prediction of ground responses is important for improving performance of tunneling. This study proposes a novel reinforcement learning (RL) based optimizer with the integration of deep-Q network (DQN) and particle swarm optimization (PSO). Such optimizer is used to improve the extreme learning machine (ELM) based tunnelinginduced settlement prediction model. Herein, DQN-PSO optimizer is used to optimize the weights and biases of ELM. Based on the prescribed states, actions, rewards, rules and objective functions, DQN-PSO optimizer evaluates the rewards of actions at each step, thereby guides particles which action should be conducted and when should take this action. Such hybrid model is applied in a practical tunnel project. Regarding the search of global best weights and biases of ELM, the results indicate the DQN-PSO optimizer obviously outperforms conventional metaheuristic optimization algorithms with higher accuracy and lower computational cost. Meanwhile, this model can identify relationships among influential factors and ground responses through selfpracticing. The ultimate model can be expressed with an explicit formulation and used to predict tunnelinginduced ground response in real time, facilitating its application in engineering practice.
Keywords: Tunnel | Ground response | Reinforcement learning | Extreme learning machine | Optimization