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Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

Auteur(s):



Médium: article de revue
Langue(s): en 
Publié dans: Advances in Civil Engineering, , v. 2016
Page(s): 1-8
DOI: 10.1155/2016/2861380
Abstrait:

This research carries out a comparative study to investigate a machine learning solution that employs the Gaussian Process Regression (GPR) for modeling compressive strength of high-performance concrete (HPC). This machine learning approach is utilized to establish the nonlinear functional mapping between the compressive strength and HPC ingredients. To train and verify the aforementioned prediction model, a data set containing 239 HPC experimental tests, recorded from an overpass construction project in Danang City (Vietnam), has been collected for this study. Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network. Furthermore, GPR model is strongly recommended for estimating HPC strength because this method demonstrates good learning performance and can inherently express prediction outputs coupled with prediction intervals.

Copyright: © 2016 Nhat-Duc Hoang et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

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    sur cette fiche
  • Reference-ID
    10176854
  • Publié(e) le:
    07.12.2018
  • Modifié(e) le:
    11.07.2019