TY - JOUR
T1 - Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate
AU - Albostami, Asad S.
AU - Al-Hamd, Rwayda Kh. S.
AU - Alzabeebee, Saif
AU - Minto, Andrew
AU - Keawsawasvong, Suraparb
N1 - © The Author(s) 2023.This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Data availability statement:
All data, models, or codes supporting this study's findings are available from the corresponding author upon reasonable request.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Self-compacting concrete (SCC) is a type of concrete known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP). The analysis results indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the Linear Regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R2) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA.
AB - Self-compacting concrete (SCC) is a type of concrete known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP). The analysis results indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the Linear Regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R2) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA.
U2 - 10.1007/s42107-023-00767-2
DO - 10.1007/s42107-023-00767-2
M3 - Article
SN - 1563-0854
VL - 25
SP - 183
EP - 196
JO - Asian Journal of Civil Engineering
JF - Asian Journal of Civil Engineering
IS - 1
ER -
