Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This...

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Autores principales:	Yue Xu, Waqas Ahmad, Ayaz Ahmad, Krzysztof Adam Ostrowski, Marta Dudek, Fahid Aslam, Panuwat Joyklad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	support vector regression AdaBoost random forest machine learning high-performance concrete Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/f6e3885292c4461e8017c7ae12b9a985
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spelling	oai:doaj.org-article:f6e3885292c4461e8017c7ae12b9a9852021-11-25T18:15:50ZComputation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques10.3390/ma142270341996-1944https://doaj.org/article/f6e3885292c4461e8017c7ae12b9a9852021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/22/7034https://doaj.org/toc/1996-1944The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R<sup>2</sup>), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R<sup>2</sup> value of 0.93, compared to the support vector regression and AdaBoost models, with R<sup>2</sup> values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.Yue XuWaqas AhmadAyaz AhmadKrzysztof Adam OstrowskiMarta DudekFahid AslamPanuwat JoykladMDPI AGarticlesupport vector regressionAdaBoostrandom forestmachine learninghigh-performance concreteTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 7034, p 7034 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	support vector regression AdaBoost random forest machine learning high-performance concrete Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	support vector regression AdaBoost random forest machine learning high-performance concrete Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Yue Xu Waqas Ahmad Ayaz Ahmad Krzysztof Adam Ostrowski Marta Dudek Fahid Aslam Panuwat Joyklad Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques
description	The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R<sup>2</sup>), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R<sup>2</sup> value of 0.93, compared to the support vector regression and AdaBoost models, with R<sup>2</sup> values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.
format	article
author	Yue Xu Waqas Ahmad Ayaz Ahmad Krzysztof Adam Ostrowski Marta Dudek Fahid Aslam Panuwat Joyklad
author_facet	Yue Xu Waqas Ahmad Ayaz Ahmad Krzysztof Adam Ostrowski Marta Dudek Fahid Aslam Panuwat Joyklad
author_sort	Yue Xu
title	Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques
title_short	Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques
title_full	Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques
title_fullStr	Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques
title_full_unstemmed	Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques
title_sort	computation of high-performance concrete compressive strength using standalone and ensembled machine learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f6e3885292c4461e8017c7ae12b9a985
work_keys_str_mv	AT yuexu computationofhighperformanceconcretecompressivestrengthusingstandaloneandensembledmachinelearningtechniques AT waqasahmad computationofhighperformanceconcretecompressivestrengthusingstandaloneandensembledmachinelearningtechniques AT ayazahmad computationofhighperformanceconcretecompressivestrengthusingstandaloneandensembledmachinelearningtechniques AT krzysztofadamostrowski computationofhighperformanceconcretecompressivestrengthusingstandaloneandensembledmachinelearningtechniques AT martadudek computationofhighperformanceconcretecompressivestrengthusingstandaloneandensembledmachinelearningtechniques AT fahidaslam computationofhighperformanceconcretecompressivestrengthusingstandaloneandensembledmachinelearningtechniques AT panuwatjoyklad computationofhighperformanceconcretecompressivestrengthusingstandaloneandensembledmachinelearningtechniques
_version_	1718411415809687552

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

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