Optimizer-free estimation of transition rates for infrastructure deterioration: A deep learning surrogate for maximum likelihood estimation
Authors:
- Daijiro MizutaniDMDaijiro Mizutani
- Natsuki YamashitaNYNatsuki Yamashita
- Stephen WuSWStephen Wu
- Yu OtakeYOYu Otake
Open Access
- 15 September 2026
- journal article
- Published by Elsevier in Reliability Engineering & System Safety
- Vol. 278
Abstract
No abstract availableFunding Information
- Public Works Research Institute (JPJ012187)
- Council for Science, Technology and Innovation
- Japan Society for the Promotion of Science (JP24K00998)
- Japan Society for the Promotion of Science (JP25K08229)
This publication has 34 references indexed in Scilit:
- Linking functional and structural pavement deterioration processesConstruction and Building Materials, 2025
- Ranking bridge intervention scenarios with a risk-based predictive decision support systemAutomation in Construction, 2025
- Scalable probabilistic deterioration model based on visual inspections and structural attributes from large networks of bridgesAdvanced Engineering Informatics, 2024
- Systematic and scientometric analyses of predictors for modelling water pipes deteriorationAutomation in Construction, 2023
- Bayesian-optimized deep learning model to segment deterioration patterns underneath bridge decks photographed by unmanned aerial vehicleAutomation in Construction, 2022
- Integrating BIM and AI for Smart Construction Management: Current Status and Future DirectionsArchives of Computational Methods in Engineering, 2022
- Project Level Management Decisions in Construction and Rehabilitation of Flexible PavementsAutomation in Construction, 2022
- Digital Twin for maintenance: A literature reviewComputers in Industry, 2020
- Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementationReliability Engineering & System Safety, 2014
- Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: TheoryReliability Engineering & System Safety, 2014