Journal ArticleDOI
Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges 1
TLDR
In this paper, the authors highlight recent accomplishments in the life-cycle performance assessment, maintenance, monitoring, management and optimisation of structural systems under uncertainty, and identify challenges in this area.Abstract:
Our knowledge to model, analyse, design, maintain, monitor, manage, predict and optimise the life-cycle performance of structures and infrastructures under uncertainty is continually growing. However, in many countries, including the United States, the civil infrastructure is no longer within desired levels of performance and safety. Decisions regarding civil infrastructure systems should be supported by an integrated reliability-based life-cycle multi-objective optimisation framework by considering, among other factors, the likelihood of successful performance and the total expected cost accrued over the entire life-cycle. The primary objective of this paper is to highlight recent accomplishments in the life-cycle performance assessment, maintenance, monitoring, management and optimisation of structural systems under uncertainty. Challenges are also identified.read more
Citations
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Journal ArticleDOI
Time-dependent performance indicators of damaged bridge superstructures
Duygu Saydam,Dan M. Frangopol +1 more
TL;DR: In this article, the authors present a framework for computation of time-dependent performance indicators of civil structures and infrastructures, with emphasis on bridges, including reliability, vulnerability, robustness and redundancy.
Journal ArticleDOI
Probabilistic optimization framework for inspection/repair planning of fatigue-critical details using dynamic Bayesian networks
David Y. Yang,Dan M. Frangopol +1 more
TL;DR: A novel framework for optimizing inspection/repair planning is developed by using efficient Bayesian updating with dynamic Bayesian network, which allows for time-dependent and adaptive repair actions considering fatigue damage development, available inspection results, and previous repair actions.
Journal ArticleDOI
Optimal policy for structure maintenance: A deep reinforcement learning framework
Shiyin Wei,Yuequan Bao,Hui Li +2 more
TL;DR: A novel automated DRL framework to obtain an optimized structural maintenance policy is proposed and shows that the DRL is efficient at finding the optimal policy for maintenance tasks for both simple and complex structures.
Journal ArticleDOI
Connectivity-Based Optimal Scheduling for Maintenance of Bridge Networks
Paolo Bocchini,Dan M. Frangopol +1 more
TL;DR: The maximization of the total network connectivity is chosen as the objective of the optimization, together with the minimization ofThe total maintenance cost, to achieve efficiency and applicability to real cases.
Journal ArticleDOI
Optimizing Bridge Network Retrofit Planning Based on Cost-Benefit Evaluation and Multi-Attribute Utility Associated with Sustainability
TL;DR: In this paper, the authors discuss the vulnerability of bridge networks to extreme events, such as strong earthquakes, which pose an imminent threat to society, economy, and surrounding environment during their service life.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
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TL;DR: This research attacked the mode confusion problem by developing a modeling framework called “model schizophrenia” to estimate the posterior probability of various modeled errors.
American Association of State Highway and Transportation Officials. Highway Drainage Guidelines American Association of State Highway and Transportation Officials. LRFD Bridge Design Specifications
TL;DR: The 2003 AASHTO Subcommittee on Design, Task Force on Geometric Design was held in Charleston, South Carolina during the period July 13 through July 16, 2003 as mentioned in this paper and the purpose of the meeting was to review several documents undergoing revision, review the proposed revisions to the superelevation discussion in the Green Book, consider future research, and other administrative matters of concern to the Task Force Membership.
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