TY - JOUR
T1 - Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming
AU - Algaifi, Hassan Amer
AU - Alqarni, Ali S.
AU - Alyousef, Rayed
AU - Bakar, Suhaimi Abu
AU - Ibrahim, M. H.Wan
AU - Shahidan, Shahiron
AU - Ibrahim, Mohammed
AU - Salami, Babatunde Abiodun
N1 - Publisher Copyright:
© 2021 THE AUTHORS
PY - 2021/11/20
Y1 - 2021/11/20
N2 - 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.
AB - 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.
KW - Bacterial concrete
KW - Bio-inspired self-healing
KW - Compressive strength prediction
KW - Gene expression programming modelling
KW - Microbial calcium carbonate
UR - http://www.scopus.com/inward/record.url?scp=85105319652&partnerID=8YFLogxK
U2 - 10.1016/j.asej.2021.04.008
DO - 10.1016/j.asej.2021.04.008
M3 - Article
AN - SCOPUS:85105319652
SN - 2090-4479
VL - 12
SP - 3629
EP - 3639
JO - Ain Shams Engineering Journal
JF - Ain Shams Engineering Journal
IS - 4
ER -