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Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming

The impact of microbial calcium carbonate on concrete strength has been extensively evaluated in the literature. However, there is no predicted equation for the compressive strength of concrete incorporating ureolytic bacteria. Therefore, in the present study, 69 experimental tests were taken into a...

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Main Authors: Algaifi, Hassan Amer, Alqarni, Ali S., Alyousef, Rayed, Abu Bakar, Suhaimi, Wan Ibrahim, M.H., Shahidan, Shahiron, Ibrahim, Mohammed, Salami, Babatunde Abiodun
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/95510/1/SuhaimiAbu2021_MathematicalPredictionoftheCompressiveStrength.pdf
http://eprints.utm.my/id/eprint/95510/
http://dx.doi.org/10.1016/j.asej.2021.04.008
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spelling my.utm.955102022-05-31T12:45:41Z http://eprints.utm.my/id/eprint/95510/ Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming Algaifi, Hassan Amer Alqarni, Ali S. Alyousef, Rayed Abu Bakar, Suhaimi Wan Ibrahim, M.H. Shahidan, Shahiron Ibrahim, Mohammed Salami, Babatunde Abiodun TA Engineering (General). Civil engineering (General) The impact of microbial calcium carbonate on concrete strength has been extensively evaluated in the literature. However, there is no predicted equation for the compressive strength of concrete incorporating ureolytic bacteria. Therefore, in the present study, 69 experimental tests were taken into account to introduce a new predicted mathematical formula for compressive strength of bacterial concrete with different concentrations of calcium nitrate tetrahydrate, urea, yeast extract, bacterial cells and time using Gene Expression Programming (GEP) modelling. Based on the results, statistical indicators (MAE, RAE, RMSE, RRSE, R and R2) proved the capability of the GEP 2 model to predict compressive strength in which minimum error and high correlation were achieved. Moreover, both predicted and actual results indicated that compressive strength decreased with the increase in nutrient concentration. In contrast, the compressive strength increased with increased bacterial cells concentration. It could be concluded that GEP2 were found to be reliable and accurate compared to that of the experimental results. Elsevier B.V. 2021-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95510/1/SuhaimiAbu2021_MathematicalPredictionoftheCompressiveStrength.pdf Algaifi, Hassan Amer and Alqarni, Ali S. and Alyousef, Rayed and Abu Bakar, Suhaimi and Wan Ibrahim, M.H. and Shahidan, Shahiron and Ibrahim, Mohammed and Salami, Babatunde Abiodun (2021) Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming. Ain Shams Engineering Journal, 12 (4). pp. 3629-3639. ISSN 2090-4479 http://dx.doi.org/10.1016/j.asej.2021.04.008 DOI:10.1016/j.asej.2021.04.008
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)
Algaifi, Hassan Amer
Alqarni, Ali S.
Alyousef, Rayed
Abu Bakar, Suhaimi
Wan Ibrahim, M.H.
Shahidan, Shahiron
Ibrahim, Mohammed
Salami, Babatunde Abiodun
Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming
description The impact of microbial calcium carbonate on concrete strength has been extensively evaluated in the literature. However, there is no predicted equation for the compressive strength of concrete incorporating ureolytic bacteria. Therefore, in the present study, 69 experimental tests were taken into account to introduce a new predicted mathematical formula for compressive strength of bacterial concrete with different concentrations of calcium nitrate tetrahydrate, urea, yeast extract, bacterial cells and time using Gene Expression Programming (GEP) modelling. Based on the results, statistical indicators (MAE, RAE, RMSE, RRSE, R and R2) proved the capability of the GEP 2 model to predict compressive strength in which minimum error and high correlation were achieved. Moreover, both predicted and actual results indicated that compressive strength decreased with the increase in nutrient concentration. In contrast, the compressive strength increased with increased bacterial cells concentration. It could be concluded that GEP2 were found to be reliable and accurate compared to that of the experimental results.
format Article
author Algaifi, Hassan Amer
Alqarni, Ali S.
Alyousef, Rayed
Abu Bakar, Suhaimi
Wan Ibrahim, M.H.
Shahidan, Shahiron
Ibrahim, Mohammed
Salami, Babatunde Abiodun
author_facet Algaifi, Hassan Amer
Alqarni, Ali S.
Alyousef, Rayed
Abu Bakar, Suhaimi
Wan Ibrahim, M.H.
Shahidan, Shahiron
Ibrahim, Mohammed
Salami, Babatunde Abiodun
author_sort Algaifi, Hassan Amer
title Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming
title_short Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming
title_full Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming
title_fullStr Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming
title_full_unstemmed Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming
title_sort mathematical prediction of the compressive strength of bacterial concrete using gene expression programming
publisher Elsevier B.V.
publishDate 2021
url http://eprints.utm.my/id/eprint/95510/1/SuhaimiAbu2021_MathematicalPredictionoftheCompressiveStrength.pdf
http://eprints.utm.my/id/eprint/95510/
http://dx.doi.org/10.1016/j.asej.2021.04.008
_version_ 1735386813202169856
score 13.211869

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