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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/2861380 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558760688615424 |
|---|---|
| author | Nhat-Duc Hoang Anh-Duc Pham Quoc-Lam Nguyen Quang-Nhat Pham |
| author_facet | Nhat-Duc Hoang Anh-Duc Pham Quoc-Lam Nguyen Quang-Nhat Pham |
| author_sort | Nhat-Duc Hoang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e97d84e785f34347ad625f1746ce7887 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-e97d84e785f34347ad625f1746ce78872025-02-03T01:31:40ZengWileyAdvances in Civil Engineering1687-80861687-80942016-01-01201610.1155/2016/28613802861380Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression ModelNhat-Duc Hoang0Anh-Duc Pham1Quoc-Lam Nguyen2Quang-Nhat Pham3Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang 550000, VietnamFaculty of Project Management, The University of Danang, University of Science and Technology, 54 Nguyen Luong Bang, Danang 550000, VietnamFaculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang, VietnamFaculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang, VietnamThis 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.http://dx.doi.org/10.1155/2016/2861380 |
| spellingShingle | Nhat-Duc Hoang Anh-Duc Pham Quoc-Lam Nguyen Quang-Nhat Pham Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model Advances in Civil Engineering |
| title | Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model |
| title_full | Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model |
| title_fullStr | Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model |
| title_full_unstemmed | Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model |
| title_short | Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model |
| title_sort | estimating compressive strength of high performance concrete with gaussian process regression model |
| url | http://dx.doi.org/10.1155/2016/2861380 |
| work_keys_str_mv | AT nhatduchoang estimatingcompressivestrengthofhighperformanceconcretewithgaussianprocessregressionmodel AT anhducpham estimatingcompressivestrengthofhighperformanceconcretewithgaussianprocessregressionmodel AT quoclamnguyen estimatingcompressivestrengthofhighperformanceconcretewithgaussianprocessregressionmodel AT quangnhatpham estimatingcompressivestrengthofhighperformanceconcretewithgaussianprocessregressionmodel |