Applications of Reinforcement Learning for maintenance of engineering systems: A review
AP Marugán - Advances in Engineering Software, 2023 - Elsevier
Nowadays, modern engineering systems require sophisticated maintenance strategies to
ensure their correct performance. Maintenance has become one of the most important tasks …
ensure their correct performance. Maintenance has become one of the most important tasks …
Managing engineering systems with large state and action spaces through deep reinforcement learning
CP Andriotis, KG Papakonstantinou - Reliability Engineering & System …, 2019 - Elsevier
Decision-making for engineering systems management can be efficiently formulated using
Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs). Typical …
Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs). Typical …
Reinforcement learning in construction engineering and management: A review
The construction engineering and management (CEM) domain frequently meets complex
tasks due to the unavoidable complicated operation environments and the involvement of …
tasks due to the unavoidable complicated operation environments and the involvement of …
Life-cycle optimization of pavement overlay systems
H Zhang, GA Keoleian, MD Lepech… - Journal of infrastructure …, 2010 - ascelibrary.org
Preservation (maintenance and rehabilitation) strategy is the critical factor controlling
pavement performance. A life-cycle optimization (LCO) model was developed to determine …
pavement performance. A life-cycle optimization (LCO) model was developed to determine …
Incorporating network considerations into pavement management systems: A case for approximate dynamic programming
The objective of infrastructure management is to provide optimal maintenance, rehabilitation
and replacement (MR&R) policies for a system of facilities over a planning horizon. While …
and replacement (MR&R) policies for a system of facilities over a planning horizon. While …
An improved pavement maintenance optimization methodology: Integrating LCA and LCCA
Environmental damage cost (EDC) is traditionally ignored in the pavement cost evaluation.
This study used a combined life cycle assessment–life cycle cost analysis (LCA–LCCA) …
This study used a combined life cycle assessment–life cycle cost analysis (LCA–LCCA) …
Network-level pavement asset management system integrated with life-cycle analysis and life-cycle optimization
H Zhang, GA Keoleian, MD Lepech - Journal of infrastructure …, 2013 - ascelibrary.org
The authors have developed a new network-level pavement asset management system
(PAMS) utilizing life-cycle analysis and optimization methods. Integrated life-cycle …
(PAMS) utilizing life-cycle analysis and optimization methods. Integrated life-cycle …
A reinforcement learning method for multiasset roadway improvement scheduling considering traffic impacts
W Zhou, E Miller-Hooks… - Journal of …, 2022 - ascelibrary.org
Maintaining roadway pavements and bridge decks is key to providing high levels of service
for road users. However, improvement actions incur downtime. These actions are typically …
for road users. However, improvement actions incur downtime. These actions are typically …
Model uncertainty and the management of a system of infrastructure facilities
KD Kuhn, SM Madanat - Transportation Research Part C: Emerging …, 2005 - Elsevier
The network-level infrastructure management problem involves selecting and scheduling
maintenance, repair, and rehabilitation (MR&R) activities on networks of infrastructure …
maintenance, repair, and rehabilitation (MR&R) activities on networks of infrastructure …
Model-free reinforcement learning with model-based safe exploration: Optimizing adaptive recovery process of infrastructure systems
M Memarzadeh, M Pozzi - Structural Safety, 2019 - Elsevier
Extreme events represent not only some of the most damaging events in our society and
environment, but also the most difficult to predict. Model-based predictions of the disruptions …
environment, but also the most difficult to predict. Model-based predictions of the disruptions …