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Advances in Civil Engineering
Volume 2016, Article ID 2861380, 8 pages
http://dx.doi.org/10.1155/2016/2861380
Research Article

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

1Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang 550000, Vietnam
2Faculty of Project Management, The University of Danang, University of Science and Technology, 54 Nguyen Luong Bang, Danang 550000, Vietnam
3Faculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang, Vietnam

Received 3 June 2016; Revised 22 September 2016; Accepted 26 September 2016

Academic Editor: Ghassan Chehab

Copyright © 2016 Nhat-Duc Hoang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.