TY  - JOUR
Y1  - 2021///
PB  - MDPI
SN  - 19961944
JF  - Materials
A1  - Amin, M.N.
A1  - Khan, K.
A1  - Aslam, F.
A1  - Shah, M.I.
A1  - Javed, M.F.
A1  - Musarat, M.A.
A1  - Usanova, K.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116913620&doi=10.3390%2fma14195659&partnerID=40&md5=052583377cbff27f1564d540e1a5806f
VL  - 14
AV  - none
N2  - The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength (f-1), splitting tensile strength (f-), and flexural strength (f1) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a kfold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of f1 1, fB and f1 of BA concrete, which can reduce the experimental workload in assessing the strength properties. The studyâ??s findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete. © 2021 by the authors.
IS  - 19
N1  - cited By 10
ID  - scholars14396
TI  - Multigene expression programming based forecasting the hardened properties of sustainable bagasse ash concrete
KW  - Agricultural wastes; Bagasse; Compressive strength; Concretes; Construction industry; Mean square error; Soft computing; Tensile strength
KW  -  Bagasse ashes; Construction sectors; Cross validation; Experimental investigations; Hardened properties; Multigene expression programming; Multigenes; Multiphysics model; Softcomputing techniques; Supervised machine learning
KW  -  Machine learning
ER  -