Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses

Pin Zhang, Heng Li, Q. P. Ha, Zhen Yu Yin, Ren Peng Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

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.

Original languageEnglish
Article number101097
JournalAdvanced Engineering Informatics
Volume45
DOIs
Publication statusPublished - Aug 2020

Keywords

  • Extreme learning machine
  • Ground response
  • Optimization
  • Reinforcement learning
  • Tunnel

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence

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