Risk acceptance and maintenance optimization of aging civil engineering infrastructures

Rackwitz, R.; Joanni, Andreas

doi:10.1016/j.strusafe.2008.07.001

Cited by 38 publications

(7 citation statements)

References 15 publications

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“…The authors extend these results to optimal repair and retrofit of existing structures in Streicher et al (2008), and present a renewal model for cost-benefit optimization including three different maintenance strategies (Rackwitz and Joanni, 2009).…”

Section: Risk Optimization Literature Reviewmentioning

confidence: 89%

A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

Kroetz

Moustapha

Beck

et al. 2020

Reliability Engineering & System Safety

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Several methods have been proposed in the literature to solve reliability-based optimization problems, where failure probabilities are design constraints. However, few methods address the problem of life-cycle cost or risk optimization, where failure probabilities are part of the objective function. Moreover, few papers in the literature address time-variant reliability problems in lifecycle cost or risk optimization formulations; in particular, because most often computationally expensive Monte Carlo simulation is required. This paper proposes a numerical framework for solving general risk optimization problems involving time-variant reliability analysis. To alleviate the computational burden of Monte Carlo simulation, two adaptive coupled surrogate models are used: the first one to approximate the objective function, and the second one to approximate the quasi-static limit state function. An iterative procedure is implemented for choosing additional support points to increase the accuracy of the surrogate models. Three application problems are used to illustrate the proposed approach. Two examples involve random load and random resistance degradation processes. The third problem is related to load-path dependent failures. This subject had not yet been addressed in the context of risk-based optimization. It is shown herein that accurate solutions are obtained, with extremely limited numbers of objective function and limit state functions calls.

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Section: Risk Optimization Literature Reviewmentioning

confidence: 89%

A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

Kroetz

Moustapha

Beck

et al. 2020

Reliability Engineering & System Safety

View full text Add to dashboard Cite

show abstract

“…The risk optimization formulation addresses the safety-economy tradeoff in structural design [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. The optimal reliabilities 𝛽 * are a sub-product of the analysis.…”

Section: Risk Optimizationmentioning

confidence: 99%

“…Risk optimization [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]74] increases the scope of RBDO by including the expected consequences of failure. Each failure event described in Figure 4 has different consequences.…”

Section: Risk Optimizationmentioning

confidence: 99%

Optimal design of redundant structural systems: fundamentals

Beck

2020

Engineering Structures

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“…Such an effort was made in the case of the development and application of the so-called Life-Quality Index (e.g. [26][27][28][29]). The latter is able to provide a rational basis to account for the cost of saving lives [26], but, as shown by Goda and Hong [30], has certain limitations, since it does not control the optimal seismic design in some cases.…”

Section: Introductionmentioning

confidence: 99%

A five-grade grading system for the evaluation and communication of short-term and long-term risk posed by natural hazards

Babič

Dolšek

2019

Structural Safety

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Risk acceptance and maintenance optimization of aging civil engineering infrastructures

Cited by 38 publications

References 15 publications

A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

Optimal design of redundant structural systems: fundamentals

A five-grade grading system for the evaluation and communication of short-term and long-term risk posed by natural hazards

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