eprintid: 16812
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/68/12
datestamp: 2023-12-19 03:23:19
lastmod: 2023-12-19 03:23:19
status_changed: 2023-12-19 03:06:56
type: article
metadata_visibility: show
creators_name: Aslam, F.
creators_name: Elkotb, M.A.
creators_name: Iqtidar, A.
creators_name: Khan, M.A.
creators_name: Javed, M.F.
creators_name: Usanova, K.I.
creators_name: Khan, M.I.
creators_name: Alamri, S.
creators_name: Musarat, M.A.
title: Compressive strength prediction of rice husk ash using multiphysics genetic expression programming
ispublished: pub
keywords: Compressive strength; Forecasting; Genetic algorithms; Genetic programming; Machine learning, Genetic Expression Programming; Machine-learning; Multi-physics; Multiphysics model; Programming models; Programming technique; Regression modelling; Rice husk; Rice-husk ash; Strength prediction, Regression analysis
note: cited By 31
abstract: Rice husk ash (RHA) is obtained by burning rice husks. An advanced programming technique known as genetic expression programming (GEP) is used in this research for developing an empirical multiphysics model for predicting the compressive strength of RHA incorporated concrete. A vast database comprising of 250 data points is obtained from the extensive and consistent literature review. Different parameters such as age, RHA content, cement content, water content, amount of superplasticizer and aggregate content are used as inputs. A closed-form equation solution was obtained to predict the compressive strength of RHA based on input parameters. The performance of GEP is evaluated by comparing it with regression models. Statistical parameter R2 is used to assess the results predicted by GEP and regression models. Statistical and parametric analysis is also carried out to determine the influence of inputs on the outcome. The GEP model performed better in all terms as compared to other models. © 2021 THE AUTHORS
date: 2022
publisher: Ain Shams University
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118766459&doi=10.1016%2fj.asej.2021.09.020&partnerID=40&md5=87657372a1959d8b9e651dc0d9b883e5
id_number: 10.1016/j.asej.2021.09.020
full_text_status: none
publication: Ain Shams Engineering Journal
volume: 13
number: 3
refereed: TRUE
issn: 20904479
citation:   Aslam, F. and Elkotb, M.A. and Iqtidar, A. and Khan, M.A. and Javed, M.F. and Usanova, K.I. and Khan, M.I. and Alamri, S. and Musarat, M.A.  (2022) Compressive strength prediction of rice husk ash using multiphysics genetic expression programming.  Ain Shams Engineering Journal, 13 (3).   ISSN 20904479