2020
DOI: 10.1111/mice.12558
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Deep reinforcement learning for long‐term pavement maintenance planning

Abstract: Inappropriate maintenance and rehabilitation strategies cause many problems such as maintenance budget waste, ineffective pavement distress treatments, and so forth. A method based on a machine learning algorithm called deep reinforcement learning (DRL) was developed in this presented research in order to learn better maintenance strategies that maximize the long-term cost-effectiveness in maintenance decisionmaking through trial and error. In this method, each single-lane pavement segment can have different t… Show more

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Cited by 114 publications
(36 citation statements)
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“…In recent years, ML techniques have been increasingly employed in infrastructure risk management, including optimized infrastructure maintenance strategies detection (Yao, Dong, Jiang, & Ni, 2020), failures detection in buildings (Rafiei & Adeli, 2017c) and infrastructure networks (M. Wang & Cheng, 2020), safety of structures and infrastructures monitoring (Rafiei & Adeli, 2018), and postdisaster damage and loss estimation (Pan, Lin, & Liao, 2019). In particular, ML techniques have shown promising results in infrastructure risk assessment.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In recent years, ML techniques have been increasingly employed in infrastructure risk management, including optimized infrastructure maintenance strategies detection (Yao, Dong, Jiang, & Ni, 2020), failures detection in buildings (Rafiei & Adeli, 2017c) and infrastructure networks (M. Wang & Cheng, 2020), safety of structures and infrastructures monitoring (Rafiei & Adeli, 2018), and postdisaster damage and loss estimation (Pan, Lin, & Liao, 2019). In particular, ML techniques have shown promising results in infrastructure risk assessment.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The method corresponding to an off-policy configuration is superior in that it can utilize historical data of past maintenance by human experts to implement an optimized decision-making policy that is different from the policy in the past history immediately after offline training. DQN applications to maintenance include road pavement maintenance [29], bridge maintenance [30], and general multi-component condition-based maintenance [31]. In [31], stochastic and economic dependencies among multiple components are taken into account by DQN.…”
Section: (Deep) Reinforcement Learning For Maintenance Planningmentioning
confidence: 99%
“…(Ghosh‐Dastidar & Adeli, 2003; X. Jiang & Adeli, 2005) and subsequently investigated using advanced models such as graphic DL (Y. Zhang, Cheng, et al., 2019) and 3D CNN (Tang et al., 2020). Combined with another sibling (AI technique reinforcement learning (RL)) to yield DRL, DL has also been applied to operational control and planning tasks in transportation such as traffic signal control (Wu et al., 2019) and pavement maintenance planning (Yao et al., 2020). Besides these successful applications, DRL has been used in multiple complex CAV driving control tasks including lane‐keeping and obstacle avoidance (El Sallab et al., 2017; S. Chen, Leng, et al., 2020), lane‐changing (Dong, Chen, Li, Du, et al., 2021; Dong, Chen, Li, Ha, et al., 2020; Huegle et al., 2020), merging maneuvers (Saxena et al., 2019), crossing traffic avoidance (Wang et al., 2020), and roundabout driving (J. Chen, Yuan, et al., 2019).…”
Section: Introductionmentioning
confidence: 99%