Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses. (August 2020)

• Record Type: Journal Article
• Authors: Zhang, Pin; Li, Heng; Ha, Q.P.; Yin, Zhen-Yu; Chen, Ren-Peng
• Is Part Of: Advanced engineering informatics. Volume 45(2020)
• Publication Date: 2020-08
• Subjects: Tunnel -- Ground response -- Reinforcement learning -- Extreme learning machine -- Optimization; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101097
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: A reinforcement learning (RL) based optimizer is proposed. Extreme learning machine based tunneling-induced settlement prediction model is established. Framework of hybrid RL based optimizer and machine learning algorithms is proposed. Abstract: 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 tunneling-induced 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 self-practicing. The ultimate model can be expressed with an explicit formulation and used to predict tunneling-induced ground response in real time, facilitating its application in engineering practice.