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Probabilistic modeling of heteroscedastic laboratory experiments using Gaussian process regression

Lucie Tabor, James Alexandre Goulet, Jean-Philippe Charron et Clelia Desmettre

Article de revue (2018)

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Abstract

This paper proposes an extension to Gaussian process regression (GPR) for data sets composed of only a few replicated specimens and displaying a heteroscedastic behavior. Because there are several factors that are out of the control of experimenters, it is often impossible to reproduce identical specimens for a same experiment. Moreover, observations from laboratory experiments typically display a heteroscedastic interspecimen variability. Because experiments and specimen manufacturing are expensive, it is uncommon to have more than three specimens to build a model for the observed responses. The method proposed in this paper uses GPR to predict each tested specimen using a shared prior structure and models the global heteroscedastic behavior by combining observations using conjugate prior distributions. An application of the method to high-performance fiber-reinforced concrete experiments highlights fiber addition benefits for reducing water permeability caused by macrocracks.

Sujet(s): 1000 Génie civil > 1000 Génie civil
Département: Département des génies civil, géologique et des mines
URL de PolyPublie: https://publications.polymtl.ca/3026/
Titre de la revue: Journal of Engineering Mechanics (vol. 144, no 6)
Maison d'édition: ASCE
DOI: 10.1061/(asce)em.1943-7889.0001466
URL officielle: https://doi.org/10.1061/%28asce%29em.1943-7889.000...
Date du dépôt: 19 avr. 2018 12:44
Dernière modification: 26 sept. 2024 19:55
Citer en APA 7: Tabor, L., Goulet, J. A., Charron, J.-P., & Desmettre, C. (2018). Probabilistic modeling of heteroscedastic laboratory experiments using Gaussian process regression. Journal of Engineering Mechanics, 144(6), 1-10. https://doi.org/10.1061/%28asce%29em.1943-7889.0001466

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