Bayesian updated correlation length of spatial concrete properties using limited data

Criel, Pieterjan; Caspeele, Robby; Taerwe, Luc

doi:10.12989/cac.2014.13.5.659

Cited by 9 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different correlation lengths cause a difference in the results. However, in practice correlation lengths are often assumed based on literature and these assumed values for the correlation length of concrete properties are not consistent with each other [32]. The suggested values for the correlation length could be the initial values for further investigations.…”

Section: Discussionmentioning

confidence: 99%

“…Nonetheless, in most literature, the correlation length is assumed and these assumptions are not consistent with each other [32], the reason for this is the lack of available data [30,31]. The probability of failure can be underestimated resulting in an unconservative design if this value is ignored or implicitly assumed to be infinite [12,16,33,35].…”

Section: Compressive Failurementioning

confidence: 99%

See 1 more Smart Citation

Numerical Simulation for 3d Printed Wall Structure During the Process of Printing Considering Uncertainty

Mengesha¹,

Schmidt²,

Göbel³

et al. 2021

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

View full text Add to dashboard Cite

3D concrete printing technology is getting increasing recognition in the construction industry. The extrusion-based printing method represents the most popular and promising one among the 3D printing techniques for concrete. However, mostly time-consuming trial-anderror explorations, i.e. mainly experimental studies have been performed so far. By utilizing numerical simulation, a fundamental understanding of the relations between process -process parameters -product properties could be achieved. Also, they enable us to study the dependencies of properties of the printed product on process parameters and material behavior. The extrusion-based 3D concrete printing process can be reliably controlled and optimized by taking into account the uncertain nature of the process and material parameters. In this study, the Finite Element (FE) method combined with a pseudo-density approach, following the soft-killing approaches in topology optimization is applied. The numerical simulations allow to reliably estimate the strength-based failure mechanisms that might occur during the 3D concrete printing of a wall structure by varying one of the printing process parameter printing velocity.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Compressive Failurementioning

confidence: 99%

Numerical Simulation for 3d Printed Wall Structure During the Process of Printing Considering Uncertainty

Mengesha¹,

Schmidt²,

Göbel³

et al. 2021

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

View full text Add to dashboard Cite

show abstract

“…In literature, different assumptions on the correlation models for corrosion variables can be found. Criel, Caspeele, & Taerwe (2014) summarize some correlation lengths commonly found in literature for corrosion and concrete variables, as presented in Table 1.…”

Section: Correlation Models For Random Fields Applied To Corrosion Va...mentioning

confidence: 99%

“…Concrete cover c 1 m -2 m -3.5 m (Li, Vrouwenvelder, Wijnants, & Walraven, 2004;Stewart & Mullard, 2007;Straub, 2011) Surface chloride concentration Cs 1 m -1.96 m -2 m -3.5 m (Duprat, 2007;Engelund, 1997;Straub, 2011;Vu, 2003) Concrete compressive strength fc 3.5 m (Duprat, 2007) The correlation models for the correlation coefficient ρ mentioned in (Criel et al, 2014) are the exponential correlation model (Equation ( 9)), the squared exponential model (Equation ( 10)) and the Matérn model (Equation ( 11)). The last one depends not only on the correlation length lc, but also on a smoothness parameter ν.…”

Section: Correlation Lengths Referencesmentioning

confidence: 99%

Influence of the correlation model on the failure probability of a reinforced concrete structure considering spatial variability

Vereecken

Botte

Lombaert

et al. 2021

Structure and Infrastructure Engineering

Self Cite

View full text Add to dashboard Cite

Reinforced concrete structures are often subjected to corrosion, inducing a decreasing reliability over time. This corrosion often exhibits spatial variation, which can be modelled by random fields. To improve the accuracy of the estimated reliability, measurements can be performed. In this work, focus will be on static strain data. Taking into account these measurements leads to updated distributions of the corrosion parameters and an updated reliability. However, modelling errors will arise due to an inadequate choice of the spatial correlation model. These are present since the perfect model is not known. By assuming an inaccurate correlation model, a bias on the distribution of the reliability index might arise. In this work, the influence of this modelling error is investigated by assuming a different correlation model in the calibration of the model and the generation of the data. Different correlation models are considered and the most robust spatial correlation model is searched for. This problem is considered first for a simply supported beam subjected to corrosion, with the aim of identifying the most robust model. Next, it is investigated whether this model also performs well for a bridge structure.

show abstract

“…Variables often modelled by random fields are the concrete cover, surface chloride concentration, concrete compressive strength, critical chloride concentration, diffusion coefficient of the concrete, water/cement ratio, Young's modulus of the concrete, Poisson coefficient of the concrete, tensile strength of concrete, etc. (Vu & Stewart, 2005;Straub, 2011;Tran et al, 2012;Criel et al, 2014;Chen et al, 2018;Liang et al, 2022;Feng et al, 2022). There can be a large spread on the correlation lengths used for one variable, for example with ranges from 1 m to 3.5 m for the concrete cover and surface chloride concentration (Engelund, 1997;Vu, 2003;Li et al, 2004;Duprat, 2007;Stewart & Mullard, 2007).…”

Section: Introductionmentioning

confidence: 99%

Numerical and Experimental Investigation of a Correlation Model to Describe Spatial Variability of Concrete Properties

Botte

Vereecken

Caspeele

2023

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

Self Cite

View full text Add to dashboard Cite

The heterogeneous character of concrete results in spatial variation of its material properties. Random field models are often used to account for this effect. Due to the large scatter on the correlation lengths suggested in literature, tests could be performed to determine the most appropriate correlation model and corresponding correlation length. Subsequently, different techniques can be employed to fit an analytical model to the experimental semivariogram resulting in the most appropriate correlation model and corresponding correlation length. However, the resulting correlation lengths can largely depend on the experimental design. In this work, the effect of several parameters and choices to be made by an engineer in deriving the correlation model based on experimental data from destructive tests has been investigated. It was found that the curve fitting method generally leads to better estimates of the scale of fluctuation compared to the maximum likelihood method. Moreover, there is a clear benefit of applying a bootstrapping procedure to the experimental data to estimate the covariance matrix adopted in the fitting procedures as well as to estimate the uncertainty related to the estimated parameters. When a measurement error is suspected to be present and cannot be neglected, the nugget should be estimated together with the variance and the scale of fluctuation. Furthermore, the Gaussian correlation model was found to be the most robust choice, even if the actual correlation model is not Gaussian. the latter was confirmed for actual experimental data on the material properties of concrete, where a linear model was found to fit the data best but the Gaussian model provided comparable results.

show abstract

Bayesian updated correlation length of spatial concrete properties using limited data

Cited by 9 publications

References 16 publications

Numerical Simulation for 3d Printed Wall Structure During the Process of Printing Considering Uncertainty

Numerical Simulation for 3d Printed Wall Structure During the Process of Printing Considering Uncertainty

Influence of the correlation model on the failure probability of a reinforced concrete structure considering spatial variability

Numerical and Experimental Investigation of a Correlation Model to Describe Spatial Variability of Concrete Properties

Contact Info

Product

Resources

About