Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model
Joint Authors
Pham, Anh-Duc
Nguyen, Quoc-Lam
Pham, Quang-Nhat
Hoang, Nhat-Duc
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-12
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Hoang, Nhat-Duc& Pham, Anh-Duc& Nguyen, Quoc-Lam& Pham, Quang-Nhat. 2016. Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model. Advances in Civil Engineering،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1094850
Modern Language Association (MLA)
Hoang, Nhat-Duc…[et al.]. Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model. Advances in Civil Engineering No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1094850
American Medical Association (AMA)
Hoang, Nhat-Duc& Pham, Anh-Duc& Nguyen, Quoc-Lam& Pham, Quang-Nhat. Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model. Advances in Civil Engineering. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1094850
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094850