Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete

Amir Andalib,Alireza Lork,Babak Aminnejad,Cheng-Jian Lin

doi:10.1155/2022/9887803

Abstract

Ever since their presentation in the late 80s, self-compacting concrete (SCC) has been well received by researchers. SCC can flow under their weight and exhibit high workability. Nonetheless, their nonlinear behavior has made the prediction of their mix properties more demanding. Furthermore, the complex relationship between mixed proportions and rheological and mechanical properties of SCC renders their behavior prediction challenging. Soft computing approaches have been shown to optimize and reduce uncertainties, and therefore in this paper, we aim to address these challenges by employing artificial neural network (ANN) models optimized using the grey wolf optimizer (GWO) algorithm. The optimized model proved to be more accurate than genetic algorithms and multiple linear regression models. The results indicate that the four most influential parameters on the compressive strength of SCC are the cement content, ground granulated blast furnace slag, rice husk ash, and fly ash.

Highlights

A proper self-compacting concrete mix design requires balancing two conflicting objectives: deformability and stability, that is, acceptable rheological behavior and appropriate mechanical characteristics
Dataset. e database in this paper uses the dataset obtained by Asteris and Kolovos [1]. e dataset includes 205 experimental self-compacting concrete (SCC) compressive strength results collected from several articles by Asteris and Kolovos [31,32,33,34,35,36,37,38]
As suggested by Asteris and Kolovos [1], the influencing parameters on compressive strength of SCC are the 11 parameters of cement (C), silica fume (SF), rice husk ash (RHA), limestone powder (LP), ground granulated blast furnace slag (GGBFS), coarse aggregate (CA), fine aggregate (FA), fly ash (F), water (W), viscosity modifying admixtures (VMA), and new generation superplasticizers (SP) as chemical admixtures. e SCC specimens’ 28-day compressive strength is taken as the target value of [1]. e inputs are in the units of kg/m3, and the compressive strength is in MPa. e maximum, α C1 a1 β

Summary

Introduction

A proper self-compacting concrete mix design requires balancing two conflicting objectives: deformability and stability, that is, acceptable rheological behavior and appropriate mechanical characteristics. E optimum balance of coarse and fine aggregates and chemical admixtures ensures the greater cohesiveness of self-compacting concrete External variations such as changes in the production process of cement and mineral additives and the type of aggregates can trigger significant variations in the properties of fresh self-compacting concrete. Some researchers have used statistical models such as linear regression to predict the compressive strength of SCC [2], while others used numerical methods to this end [3]. Zhang et al developed a random forest model based on the beetle antenna search algorithm to predict the compressive strength of SCC [8], while Nehdi et al utilized a neural network approach to this end [2]. E proposed ANN-GWO model addresses all significant mix design parameters that influence SCC’s compressive strength.

Background

Methods and Materials

Findings

Comparison of Different Approaches