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Journal ArticleDOI

Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges 1

Dan M. Frangopol
- 01 Jun 2011 - 
- Vol. 7, Iss: 6, pp 389-413
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.

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Citations
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Resilience of aging structures and infrastructure systems with emphasis on seismic resilience of bridges and road networks: Review

TL;DR: In this paper , the authors present an overview of the main principles and concepts, methods and strategies, advances and accomplishments in the field of life-cycle reliability, risk and resilience of structures and infrastructure systems, with emphasis on seismic resilience of bridges and road networks.
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Optimized Design of Concrete Structures considering Environmental Aspects

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Vibration-based Bayesian model updating of civil engineering structures applying Gaussian process metamodel:

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TL;DR: The proposed instantaneous-resilience metric combines the robustness measure with the structural redundancy measure to consider various scenarios of load path distribution and its potential use in highway network performance assessment is illustrated with a simple hypothetical network system.
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.
Journal ArticleDOI

A fast and elitist multiobjective genetic algorithm: NSGA-II

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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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