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

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 b...

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Bibliographic Details
Published in:Advances in civil engineering 2016-01, Vol.2016 (2016), p.1-8
Main Authors: Pham, Quang-Nhat, Nguyen, Quoc-Lam, Pham, Anh-Duc, Hoang, Nhat-Duc
Format: Article
Language:English
Subjects:
Civil engineering
Concrete
Datasets
Methods
Neural networks
R&D
Research & development
Research methodology
Studies
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Summary: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.
ISSN:1687-8086
1687-8094
DOI:10.1155/2016/2861380