Reliability assessment of large hydraulic structures with spatially distributed measurements

Geyer, Sebastian; Papaioannou, Iason; Kunz, Claus; Straub, Daniel

doi:10.1080/15732479.2019.1652331

Cited by 11 publications

(12 citation statements)

References 16 publications

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“…In case (a), the characteristic value can be estimated based on the sample mean, sample standard deviation and the number of samples 𝑛. The underlying theory is a Bayesian approach and the calculated value is the 5% quantile value of the posterior predictive distribution [2,33]. We show in the following that the hierarchical approach presented in this paper is a generalization of case (a) in Annex D.7 of EN 1990.…”

Section: Connection To the Bayesian Approach Of En 1990mentioning

confidence: 95%

“…In the general case, such an update needs to be done numerically with methods usually based on Monte Carlo sampling, including Markov chain Monte Carlo methods [8], sequential Monte Carlo methods [9,10] and subset simulation [11][12][13]. However, Gaussian RFs enable the use of conjugate priors to learn the RF parameters via a closed-form update in a Bayesian analysis [2,14].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian analysis of hierarchical random fields for material modeling

Geyer

Papaioannou

Straub

2021

Probabilistic Engineering Mechanics

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Section: Connection To the Bayesian Approach Of En 1990mentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Bayesian analysis of hierarchical random fields for material modeling

Geyer

Papaioannou

Straub

2021

Probabilistic Engineering Mechanics

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“…Finally, also (Geyer et al, 2020) illustrate how the use of random fields enables a more detailed assessment of the condition of a structure. This is enabled by not only using measurements of the material properties, but combining these with information on their spatial locations.…”

Section: Application Of Modelling Spatial Variability In Structural R...mentioning

confidence: 99%

Random field modelling of spatial variability in concrete – a review

Botte,

Vereecken,

Caspeele

2023

Structure and Infrastructure Engineering

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Concrete is a heterogeneous material, consisting of different constituents such as aggregates of different sizes and cement paste. Due to the heterogeneity of the concrete, its properties such as strength, stiffness, permeability, etc. show variations in space. In scientific literature, several investigations have shown the influence of spatial variation of material properties on the reliability of reinforced concrete structures, and it was concluded that spatial variability and spatial correlation should be accounted for to obtain accurate estimations of e.g. the load-bearing capacity of degrading concrete structures.For this purpose, random fields are often applied in literature. To apply these random fields in a structural reliability analysis, a correlation model and corresponding correlation length need to be defined. Nevertheless, experimental results are not always available to derive the correlation model, and hence some modelling assumptions are required. However, there is no consensus yet in literature on which correlation model to apply for the different concrete properties. This paper provides an overview of different correlation models applied in literature in relation to the analysis of reinforced concrete structures.

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“…Sea levels, tides and changes therein are important parameters for the design of coastal and hydraulic structures such as locks, regulators and storm surge barriers. The design of flood retention structures requires estimates of the anticipated ranges of high water levels, in most locales caused by a combination of tide and surge (Geyer et al, 2020;Kunz, 2023;Mooyaart et al, 2023;Voortman, 2003;Voortman & Vrijling, 2004;Vrijling & Van Beurden, 1990;Wahl et al, 2017;Voortman et al, 2009;Webbers et al, 2003;Hawkes et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Robust validation of trends and cycles in sea level and tidal amplitude in the Dutch North Sea

Voortman

2023

JCHS

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In this paper I present a new method for analyzing long series of observed sea levels. My method provides insight in sea level and the changes over time that are required by practicing engineers, assigned the design of new, modified or renovated structures along the coast. After extensively reviewing earlier research of sea level and tide, I present my method. The analysis relies on the application of classic harmonic analysis, which is made operational in a script in the programming language Python. Rigorous statistical testing is introduced to test the significance of trends and cycles in sea level and tide. This application of harmonic analysis and the introduction of formal statistical testing appear both to be new in this field. The method is tested on six locations in the Dutch North Sea, all with continuous sea level records of at least 130 years. The results of the statistical quality tests are shown in this paper. Subsequently, I show my findings related to mean sea level, lunar and solar tide and wind setup. Subsequently I analyze the long-term trends and cycles in Mean Sea Level. Long-period cycles with periods equal to the oceanic perigean and nodal tide are found to be important for a correct interpretation of sea level over time. Statistical tests show that acceleration of the rate of sea level is not significant up to 2021; the last year in the dataset. I compare my results with contemporary projections of sea level rise. The comparison reveals that in the Dutch North Sea the projected rates of rise are a factor two or more higher than the empirical rates established in this paper.

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Reliability assessment of large hydraulic structures with spatially distributed measurements

Cited by 11 publications

References 16 publications

Bayesian analysis of hierarchical random fields for material modeling

Bayesian analysis of hierarchical random fields for material modeling

Random field modelling of spatial variability in concrete – a review

Robust validation of trends and cycles in sea level and tidal amplitude in the Dutch North Sea

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