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Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm

This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The deve...

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Main Authors: Al-Sodani, Khaled A. Alawi, Adewumi, Adeshina Adewale, Mohd. Ariffin, Mohd. Azreen, Maslehuddin, Mohammed, Mohammad Ismail, Mohammad Ismail, Salami, Hamza Onoruoiza, Owolabi, Taoreed O., Mohamed, Hatim Dafalla
Format: Article
Language:English
Published: MDPI AG 2021
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/97526/1/MohdAzreen2021_ExperimentalandModellingofAlkaliActivatedMortar.pdf
http://eprints.utm.my/id/eprint/97526/
http://dx.doi.org/10.3390/ma14113049
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spelling my.utm.975262022-10-17T04:23:06Z http://eprints.utm.my/id/eprint/97526/ Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm Al-Sodani, Khaled A. Alawi Adewumi, Adeshina Adewale Mohd. Ariffin, Mohd. Azreen Maslehuddin, Mohammed Mohammad Ismail, Mohammad Ismail Salami, Hamza Onoruoiza Owolabi, Taoreed O. Mohamed, Hatim Dafalla TA Engineering (General). Civil engineering (General) This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete. MDPI AG 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/97526/1/MohdAzreen2021_ExperimentalandModellingofAlkaliActivatedMortar.pdf Al-Sodani, Khaled A. Alawi and Adewumi, Adeshina Adewale and Mohd. Ariffin, Mohd. Azreen and Maslehuddin, Mohammed and Mohammad Ismail, Mohammad Ismail and Salami, Hamza Onoruoiza and Owolabi, Taoreed O. and Mohamed, Hatim Dafalla (2021) Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm. Materials, 14 (11). pp. 1-25. ISSN 1996-1944 http://dx.doi.org/10.3390/ma14113049 DOI : 10.3390/ma14113049
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Al-Sodani, Khaled A. Alawi
Adewumi, Adeshina Adewale
Mohd. Ariffin, Mohd. Azreen
Maslehuddin, Mohammed
Mohammad Ismail, Mohammad Ismail
Salami, Hamza Onoruoiza
Owolabi, Taoreed O.
Mohamed, Hatim Dafalla
Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm
description This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete.
format Article
author Al-Sodani, Khaled A. Alawi
Adewumi, Adeshina Adewale
Mohd. Ariffin, Mohd. Azreen
Maslehuddin, Mohammed
Mohammad Ismail, Mohammad Ismail
Salami, Hamza Onoruoiza
Owolabi, Taoreed O.
Mohamed, Hatim Dafalla
author_facet Al-Sodani, Khaled A. Alawi
Adewumi, Adeshina Adewale
Mohd. Ariffin, Mohd. Azreen
Maslehuddin, Mohammed
Mohammad Ismail, Mohammad Ismail
Salami, Hamza Onoruoiza
Owolabi, Taoreed O.
Mohamed, Hatim Dafalla
author_sort Al-Sodani, Khaled A. Alawi
title Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm
title_short Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm
title_full Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm
title_fullStr Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm
title_full_unstemmed Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm
title_sort experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm
publisher MDPI AG
publishDate 2021
url http://eprints.utm.my/id/eprint/97526/1/MohdAzreen2021_ExperimentalandModellingofAlkaliActivatedMortar.pdf
http://eprints.utm.my/id/eprint/97526/
http://dx.doi.org/10.3390/ma14113049
_version_ 1748180471953489920
score 13.211869

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