Academic Journal

Compressive strength prediction of rice husk ash using multiphysics genetic expression programming

Bibliographic Details
Title: Compressive strength prediction of rice husk ash using multiphysics genetic expression programming
Authors: Aslam, Fahid, Elkotb, Mohamed Abdelghany, Iqtidar, Ammar, Khan, Mohsin Ali, Javed, Muhmmad Faisal, Usanova, Kseniia Iurevna, Khan, M. Ijaz, Alamri, Sagr, Musarat, Muhammad Ali
Publisher Information: Elsevier BV
Publication Year: 2022
Collection: University of Malta: OAR@UM / L-Università ta' Malta
Subject Terms: Rice hulls, Rice -- Residues, Regression analysis -- Computer programs, Artificial intelligence, Machine learning, Concrete -- Additives
Description: 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. ; peer-reviewed
Document Type: article in journal/newspaper
Language: English
Relation: Aslam, F., Elkotb, M. A., Iqtidar, A., Khan, M. A., Javed, M. F., Usanova, K. I., . & Musarat, M. A. (2022). Compressive strength prediction of rice husk ash using multiphysics genetic expression programming. Ain Shams Engineering Journal, 13(3), 101593.; https://www.um.edu.mt/library/oar/handle/123456789/128747
DOI: 10.1016/j.asej.2021.09.020
Availability: https://www.um.edu.mt/library/oar/handle/123456789/128747
https://doi.org/10.1016/j.asej.2021.09.020
Rights: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
Accession Number: edsbas.799077FD
Database: BASE
Description
DOI:10.1016/j.asej.2021.09.020