Dependent failure behavior modeling for risk and reliability: A systematic and critical literature review

Z Zeng, A Barros, D Coit - Reliability Engineering & System Safety, 2023 - Elsevier
This paper presents a systematic and critical literature review on dependent failure behavior
modeling in risk and reliability. A literature search is conducted systematically based on pre …

[HTML][HTML] Review of advanced road materials, structures, equipment, and detection technologies

JREE Office, MC Cavalli, D Chen, Q Chen… - Journal of Road …, 2023 - Elsevier
As a vital and integral component of transportation infrastructure, pavement has a direct and
tangible impact on socio-economic sustainability. In recent years, an influx of …

[HTML][HTML] Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems

G Arcieri, C Hoelzl, O Schwery, D Straub… - Reliability Engineering & …, 2023 - Elsevier
Abstract Structural Health Monitoring (SHM) describes a process for inferring quantifiable
metrics of structural condition, which can serve as input to support decisions on the …

POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance

G Arcieri, C Hoelzl, O Schwery, D Straub… - Machine Learning, 2024 - Springer
Abstract Partially Observable Markov Decision Processes (POMDPs) can model complex
sequential decision-making problems under stochastic and uncertain environments. A main …

[HTML][HTML] Models and methods for probabilistic safety assessment of steel structures subject to fatigue

J Maljaars, J Leander, A Nussbaumer, JD Sørensen… - Structural Safety, 2025 - Elsevier
We review of the state of the art in probabilistic modelling for fatigue reliability of civil
engineering and offshore structures. The modelling of randomness and uncertainty in …

A probabilistic analysis method based on Noisy-OR gate Bayesian network for hydrogen leakage of proton exchange membrane fuel cell

G Chen, G Li, M Xie, Q Xu, G Zhang - Reliability Engineering & System …, 2024 - Elsevier
The proton exchange membrane fuel cell (PEMFC) is one of the crucial power units of the
hydrogen vehicle. This work proposes a method based on the Noisy-OR gate Bayesian …

Deep reinforcement learning for intelligent risk optimization of buildings under hazard

GA Anwar, X Zhang - Reliability Engineering & System Safety, 2024 - Elsevier
Risk management often involves retrofit optimization to enhance the performance of
buildings against extreme events but may result in huge upfront mitigation costs. Existing …

A stochastic track maintenance scheduling model based on deep reinforcement learning approaches

JS Lee, IH Yeo, Y Bae - Reliability Engineering & System Safety, 2024 - Elsevier
A data-driven railway track maintenance scheduling framework based on a stochastic track
deterioration model and deep reinforcement learning approaches is proposed. Various track …

A novel completion status prediction for the aircraft mixed-model assembly lines: A study in dynamic Bayesian networks

Y Yao, J Zhang, S Jiang, Y Li, T Long - Advanced Engineering Informatics, 2024 - Elsevier
In the context of Industry 4.0, amidst the normalization of multi-variety and low-volume
custom flexible production models, aircraft mixed-model assembly lines (AMMALs) have …

IMP-MARL: a suite of environments for large-scale infrastructure management planning via MARL

P Leroy, PG Morato, J Pisane… - Advances in Neural …, 2024 - proceedings.neurips.cc
We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning
(MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a …