Structural reliability software and calculation tools: a review

Chehade, Fatima El Hajj; Younès, Rafic

doi:10.1007/s41062-020-0282-4

Cited by 26 publications

(3 citation statements)

References 89 publications

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“…Furthermore, the assessment and prediction of the p f and p s of a deteriorating structural system require (a) formulation of the state functions of the individual components involved in the system, (b) identification of critical deterioration mechanisms affecting the state function of the components, (c) appropriate system modeling (e.g., series, parallel, and series-parallel systems), and (d) estimation of the correlation among the safety margins of individual components. El Hajj Chehade and Younes [33] and Shittu et al [34] reviewed the software programs used to compute the p f and p s of structural systems.…”

Section: General Concept Of Probability Of Failurementioning

confidence: 99%

Efficient Reliability-Based Inspection Planning for Deteriorating Bridges Using Extrapolation Approaches

et al. 2022

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In this study, inspection planning of deteriorating bridges is optimized to determine the inspection application times and methods based on various objectives. These objectives can be formulated by considering the probabilistic structural performance and service life after inspection and maintenance. Probabilistic structural performance and service life prediction are generally based on the probability of failure (or reliability). However, there are difficulties associated with optimizing inspection planning when a low probability of failure is estimated. In this study, we address inspection planning using extrapolation approaches to efficiently compute a low probability of failure. The inspection planning method proposed in this study determines the inspection application times for a given inspection method. We investigated the applicability of direct Monte Carlo simulation (MCS), subset simulation, and two extrapolation approaches (i.e., kernel density estimation (KDE) and KDE combined with generalized Pareto distribution (GPD)) for inspection planning. The probability of failure for optimum inspection planning was based on the damage detection-based state function and extended service life-based state function. These state functions were formulated by considering damage propagation, damage detection by inspections, and service life extensions by maintenance. Illustrative applications to general examples and an existing bridge are provided to investigate the effects of approaches for computing the failure probability on the accuracy and variation of the optimum inspection application times. Finally, the most appropriate approach for optimum inspection planning was determined considering the accuracy and reliability of the solution, computational efficiency, and the applicability of the probabilistic optimization process. The presented investigations revealed that KDE is more appropriate than MCS and the combination of KDE and GPD for optimum inspection planning.

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Section: General Concept Of Probability Of Failurementioning

confidence: 99%

Efficient Reliability-Based Inspection Planning for Deteriorating Bridges Using Extrapolation Approaches

et al. 2022

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“…However, the FORM and similar methods have drawbacks. The mapping of the failure function onto a standardized set and its subsequent minimization involves significant computational effort for nonlinear black-box numerical models [8,24]. Simultaneously evaluating probabilities for multiple failure criteria adds considerable computational complexity.…”

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confidence: 99%

“…These input random variables exert the most significant positive impact (24) and negative impact (21) on the affected pipe element number 529. According to the presented results, the following input random variables (conservatively including the nearest to acceptance limits of 95%) were also used in the FORM analysis as input random variables:…”

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confidence: 99%

Probabilistic Assessment of Structural Integrity

Alzbutas,

Dundulis

2024

Axioms

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A probability-based approach, combining deterministic and probabilistic methods, was developed for analyzing building and component failures, which are especially crucial for complex structures like nuclear power plants. This method links finite element and probabilistic software to assess structural integrity under static and dynamic loads. This study uses NEPTUNE software, which is validated, for a deterministic transient analysis and ProFES software for probabilistic models. In a case study, deterministic analyses with varied random variables were transferred to ProFES for probabilistic analyses of piping failure and wall damage. A Monte Carlo Simulation, First-Order Reliability Method, and combined methods were employed for probabilistic analyses under severe transient loading, focusing on a postulated accident at the Ignalina Nuclear Power Plant. The study considered uncertainties in material properties, component geometry, and loads. The results showed the Monte Carlo Simulation method to be conservative for high failure probabilities but less so for low probabilities. The Response Surface/Monte Carlo Simulation method explored the impact load–failure probability relationship. Given the uncertainties in material properties and loads in complex structures, a deterministic analysis alone is insufficient. Probabilistic analysis is imperative for extreme loading events and credible structural safety evaluations.

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An Object-Oriented Computer Program for Structural Reliability Analysis (BI): Components and Methods

Ghorbanzadeh,

Homami

2023

Iran J Sci Technol Trans Civ Eng

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Structural reliability software and calculation tools: a review

Cited by 26 publications

References 89 publications

Efficient Reliability-Based Inspection Planning for Deteriorating Bridges Using Extrapolation Approaches

Efficient Reliability-Based Inspection Planning for Deteriorating Bridges Using Extrapolation Approaches

Probabilistic Assessment of Structural Integrity

An Object-Oriented Computer Program for Structural Reliability Analysis (BI): Components and Methods

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