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

Author(s):



Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2016
Page(s): 1-8
DOI: 10.1155/2016/2861380
Abstract:

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:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
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  • Reference-ID
    10176854
  • Published on:
    07/12/2018
  • Last updated on:
    02/06/2021