Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/101078
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Title: Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses
Authors: Zhang, P 
Li, H 
Ha, QP
Yin, ZY 
Chen, RP
Issue Date: Aug-2020
Source: Advanced engineering informatics, Aug. 2020, v. 45, 101097
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.
Keywords: Extreme learning machine
Ground response
Optimization
Reinforcement learning
Tunnel
Publisher: Elsevier
Journal: Advanced engineering informatics 
EISSN: 1474-0346
DOI: 10.1016/j.aei.2020.101097
Rights: © 2020 Elsevier Ltd. All rights reserved.
© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
The following publication Zhang, P., Li, H., Ha, Q. P., Yin, Z. Y., & Chen, R. P. (2020). Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses. Advanced Engineering Informatics, 45, 101097 is available at https://doi.org/10.1016/j.aei.2020.101097.
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