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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2020
عنوان انگلیسی مقاله:
Optimal policy for structure maintenance: A deep reinforcement learning framework
ترجمه فارسی عنوان مقاله:
سیاست بهینه برای نگهداری ساختار: یک چارچوب یادگیری تقویت عمیق
منبع:
Sciencedirect - Elsevier - Structural Safety, 83 (2020) 101906: doi:10:1016/j:strusafe:2019:101906
نویسنده:
Shiyin Weia,b,c, Yuequan Baoa,b,c, Hui Lia,b,c,⁎
چکیده انگلیسی:
The cost-effective management of aged infrastructure is an issue of worldwide concern. Markov decision process
(MDP) models have been used in developing structural maintenance policies. Recent advances in the artificial
intelligence (AI) community have shown that deep reinforcement learning (DRL) has the potential to solve large
MDP optimization tasks. This paper proposes a novel automated DRL framework to obtain an optimized
structural maintenance policy. The DRL framework contains a decision maker (AI agent) and the structure that
needs to be maintained (AI task environment). The agent outputs maintenance policies and chooses maintenance
actions, and the task environment determines the state transition of the structure and returns rewards to the
agent under given maintenance actions. The advantages of the DRL framework include: (1) a deep neural network
(DNN) is employed to learn the state-action Q value (defined as the predicted discounted expectation of the
return for consequences under a given state-action pair), either based on simulations or historical data, and the
policy is then obtained from the Q value; (2) optimization of the learning process is sample-based so that it can
learn directly from real historical data collected from multiple bridges (i.e., big data from a large number of
bridges); and (3) a general framework is used for different structure maintenance tasks with minimal changes to
the neural network architecture. Case studies for a simple bridge deck with seven components and a long-span
cable-stayed bridge with 263 components are performed to demonstrate the proposed procedure. The results
show that the DRL is efficient at finding the optimal policy for maintenance tasks for both simple and complex
structures.
Keywords: Bridge maintenance policy | Deep reinforcement learning (DRL) | Markov decision process (MDP) | Deep Q-network (DQN) | Convolutional neural network (CNN)
قیمت: رایگان
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