Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate

Asad S. Albostami, Rwayda Kh. S. Al-Hamd*, Saif Alzabeebee, Andrew Minto, Suraparb Keawsawasvong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
29 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)183-196
Number of pages14
JournalAsian Journal of Civil Engineering
Volume25
Issue number1
Early online date5 Jul 2023
DOIs
Publication statusPublished - 1 Jan 2024

Keywords

  • Self-compacting concrete
  • Recycled plastic aggregates
  • Multi-objective genetic algorithm evolutionary polynomial regression
  • Gene expression programming
  • Soft computing

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