Updating structural reliability efficiently using load measurement

Koker, Nico de; Viljoen, Celeste; Lenner, Roman; Jacobsz, S.W.

doi:10.1016/j.strusafe.2020.101939

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…Die Verbindung der Berechnung von Versagenswahrscheinlichkeiten und experimentellen Traglastversuchen ist Gegenstand zahlreicher Veröffentlichungen [17, 35, 36, 42–44].…”

Section: Probabilistischen Berechnungenunclassified

Verringerung der Versagenswahrscheinlichkeit von Brücken durch experimentelle Traglastversuche

2020

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Verschiedene empirische Untersuchungen zeigen, dass die reale Tragfähigkeit von Brücken unter Verkehrslasten im Durchschnitt signifikant höher ist als die berechnete Tragfähigkeit der Brücken. Eine Ursache dafür ist z. B. die Vereinfachung der Berechnungsmodelle. Zusätzliche Reserven stecken u. a. in der Mitwirkung von Brückenelementen, die in den statischen Modellen zur Berechnung der Tragfähigkeit nicht berücksichtigt werden. Da zur Ermittlung der Versagenswahrscheinlichkeit von Brücken häufig die gleichen statischen Modelle verwendet werden wie für die deterministischen Tragfähigkeitsberechnungen, muss auch hier eine Unterschätzung der realen Tragfähigkeit und damit eine Überschätzung der Versagenswahrscheinlichkeit vorliegen. Im Rahmen dieses Beitrags soll diese Überschätzung quantifiziert und Anpassungsfaktoren vorgeschlagen werden, um eine realistischere Versagenswahrscheinlichkeit zu bestimmen. Diese Anpassungsfaktoren und die damit korrigierten Versagenswahrscheinlichkeiten können im Rahmen bestandsübergreifender Lebenszykluskostenanalysen verwendet werden.

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“…Die Verbindung der Berechnung von Versagenswahrscheinlichkeiten und experimentellen Traglastversuchen ist Gegenstand zahlreicher Veröffentlichungen [17, 35, 36, 42–44].…”

Section: Probabilistischen Berechnungenunclassified

Verringerung der Versagenswahrscheinlichkeit von Brücken durch experimentelle Traglastversuche

2020

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“…The ongoing revision of the Eurocodes has prompted scientific discussions regarding the partial factors associated with loads and resistances. This raises questions about the reliability of both existing and new constructions [1].…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of Steel Portal Frames Using Gan for Generating Synthetic Data Sample

Ljubinković,

Janković,

Gervásio

et al. 2024

ce papers

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This paper addresses the challenge of reliability estimation in structural engineering, where determining failure probabilities is often uncertain due to insufficient characterization of randomness and scale of design problems. Current approaches, like EN 1990, lack sufficient data for research and simulations, particularly for low failure probabilities (around 10‐4), making Monte Carlo simulations less accurate. The paper introduces Generative Adversarial Networks (GANs) as a solution to generate synthetic data to supplement existing examples. The study applies GANs to assess the reliability of steel portal‐framed industrial buildings and evaluate the safety of this structural solution according to Eurocodes.

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“…In line with the ongoing revision of the Eurocodes, there are scientific discussions regarding the partial factors associated to the loads and resistances. This brings the question of the reliability of existing and new constructions [1].…”

Section: Introductionmentioning

confidence: 99%

Reliability assessment of steel‐framed industrial buildings

da Silva,

Ljubinković,

Janković

et al. 2023

ce papers

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The classical reliability problem seeking an estimation of the probability of failure is usually undefined due to: i) its scale (size of the design problems), and ii) deficient characterization of the randomness (lack of statistical characterization of the basic random variables). A common challenge in current approaches for the assessment of structural reliability adopted in EN 1990 is the lack of data examples that could support additional research, simulation, and experiments. As the failure probability in Eurocode is about 10‐4, millions of simulations are required, which limits the accuracy of commonly used Monte Carlo simulations (MCS). Nowadays, there are different intelligent data management approaches to augment existing data points by creating synthetic examples that can then be further used in specific behavior prediction. One such intelligent approach is the recently developed Generative Adversarial Networks (GAN), which has found its use mainly in image augmentation, but also in other applications where finding new data examples is challenging, e.g., credit card fraud detection, defect detection, etc. In this paper, a reliability assessment of steel‐framed industrial buildings is presented. For that purpose, a set of portal frames is first selected as a case study and designed using the actual design codes, whereby the critical structural members and failure modes are identified. Subsequently, based on these representative case studies, the database is expanded using GAN, and the current state of reliability for the selected type of buildings is assessed, aiming to provide a statement of the current safety of industrial sheds using the Eurocodes.

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Updating structural reliability efficiently using load measurement

Cited by 12 publications

References 31 publications

Verringerung der Versagenswahrscheinlichkeit von Brücken durch experimentelle Traglastversuche

Verringerung der Versagenswahrscheinlichkeit von Brücken durch experimentelle Traglastversuche

Reliability Assessment of Steel Portal Frames Using Gan for Generating Synthetic Data Sample

Reliability assessment of steel‐framed industrial buildings

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