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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Autores principales:	Hassan Amer Algaifi, Ali S. Alqarni, Rayed Alyousef, Suhaimi Abu Bakar, M.H. Wan Ibrahim, Shahiron Shahidan, Mohammed Ibrahim, Babatunde Abiodun Salami
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Microbial calcium carbonate Bacterial concrete Compressive strength prediction Gene expression programming modelling Bio-inspired self-healing Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/4ed1554247314405ab35f78c4be9a239
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spelling	oai:doaj.org-article:4ed1554247314405ab35f78c4be9a2392021-11-22T04:21:45ZMathematical prediction of the compressive strength of bacterial concrete using gene expression programming2090-447910.1016/j.asej.2021.04.008https://doaj.org/article/4ed1554247314405ab35f78c4be9a2392021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2090447921001775https://doaj.org/toc/2090-4479The 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.Hassan Amer AlgaifiAli S. AlqarniRayed AlyousefSuhaimi Abu BakarM.H. Wan IbrahimShahiron ShahidanMohammed IbrahimBabatunde Abiodun SalamiElsevierarticleMicrobial calcium carbonateBacterial concreteCompressive strength predictionGene expression programming modellingBio-inspired self-healingEngineering (General). Civil engineering (General)TA1-2040ENAin Shams Engineering Journal, Vol 12, Iss 4, Pp 3629-3639 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbial calcium carbonate Bacterial concrete Compressive strength prediction Gene expression programming modelling Bio-inspired self-healing Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Microbial calcium carbonate Bacterial concrete Compressive strength prediction Gene expression programming modelling Bio-inspired self-healing Engineering (General). Civil engineering (General) TA1-2040 Hassan Amer Algaifi Ali S. Alqarni Rayed Alyousef Suhaimi Abu Bakar M.H. Wan Ibrahim Shahiron Shahidan Mohammed Ibrahim Babatunde Abiodun Salami 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	Hassan Amer Algaifi Ali S. Alqarni Rayed Alyousef Suhaimi Abu Bakar M.H. Wan Ibrahim Shahiron Shahidan Mohammed Ibrahim Babatunde Abiodun Salami
author_facet	Hassan Amer Algaifi Ali S. Alqarni Rayed Alyousef Suhaimi Abu Bakar M.H. Wan Ibrahim Shahiron Shahidan Mohammed Ibrahim Babatunde Abiodun Salami
author_sort	Hassan Amer Algaifi
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
publishDate	2021
url	https://doaj.org/article/4ed1554247314405ab35f78c4be9a239
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Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming

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