eprintid: 14396
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/43/96
datestamp: 2023-11-10 03:28:58
lastmod: 2023-11-10 03:28:58
status_changed: 2023-11-10 01:56:48
type: article
metadata_visibility: show
creators_name: Amin, M.N.
creators_name: Khan, K.
creators_name: Aslam, F.
creators_name: Shah, M.I.
creators_name: Javed, M.F.
creators_name: Musarat, M.A.
creators_name: Usanova, K.
title: Multigene expression programming based forecasting the hardened properties of sustainable bagasse ash concrete
ispublished: pub
keywords: Agricultural wastes; Bagasse; Compressive strength; Concretes; Construction industry; Mean square error; Soft computing; Tensile strength, Bagasse ashes; Construction sectors; Cross validation; Experimental investigations; Hardened properties; Multigene expression programming; Multigenes; Multiphysics model; Softcomputing techniques; Supervised machine learning, Machine learning
note: cited By 10
abstract: 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 study�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.
date: 2021
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116913620&doi=10.3390%2fma14195659&partnerID=40&md5=052583377cbff27f1564d540e1a5806f
id_number: 10.3390/ma14195659
full_text_status: none
publication: Materials
volume: 14
number: 19
refereed: TRUE
issn: 19961944
citation:   Amin, M.N. and Khan, K. and Aslam, F. and Shah, M.I. and Javed, M.F. and Musarat, M.A. and Usanova, K.  (2021) Multigene expression programming based forecasting the hardened properties of sustainable bagasse ash concrete.  Materials, 14 (19).   ISSN 19961944