BAT Algorithm-Based ANN to Predict the Compressive Strength of Concrete—A Comparative Study

Nasrin Aalimahmoody,Nasim Hasanzadeh-Inanlou,Chiara Bedon,Mehdi Nikoo,Amir Hasanzade-Inallu

doi:10.3390/infrastructures6060080

Abstract

The number of effective factors and their nonlinear behaviour—mainly the nonlinear effect of the factors on concrete properties—has led researchers to employ complex models such as artificial neural networks (ANNs). The compressive strength is certainly a prominent characteristic for design and analysis of concrete structures. In this paper, 1030 concrete samples from literature are considered to model accurately and efficiently the compressive strength. To this aim, a Feed-Forward (FF) neural network is employed to model the compressive strength based on eight different factors. More in detail, the parameters of the ANN are learned using the bat algorithm (BAT). The resulting optimized model is thus validated by comparative analyses towards ANNs optimized with a genetic algorithm (GA) and Teaching-Learning-Based-Optimization (TLBO), as well as a multi-linear regression model, and four compressive strength models proposed in literature. The results indicate that the BAT-optimized ANN is more accurate in estimating the compressive strength of concrete.

Highlights

Civil engineers have always been interested in estimating the properties of concrete as a composite material by utilizing analytical models, as well as investigating the effect of each component of the mix design on its properties
Artificial neural network models have proved to be superior for the determination of concrete compressive strength in Alto Sulcis Thermal Power Station in Italy [3], in-place concrete strength estimation to facilitate concrete form removal and scheduling for construction [4], prediction of compressive strength of concrete subject to lasting sulfate attack [5], determination of low, medium, and high-strength concrete strength [6], accurate assessment of compressive
The use of accurate models plays a vital role in the design and analysis of civil engineering structural members and systems

Summary

Introduction

Civil engineers have always been interested in estimating the properties of concrete as a composite material by utilizing analytical models, as well as investigating the effect of each component of the mix design on its properties. The first step in all rehabilitation projects is to obtain information about the current conditions of the structure and its analysis. In this regard, using field experiments to perform this evaluation is very important. Many studies on the use of artificial neural networks (ANNs) to assess the compressive strength of concrete (f’c ) have been conducted in recent decades. Artificial neural network models have proved to be superior for the determination of concrete compressive strength in Alto Sulcis Thermal Power Station in Italy [3], in-place concrete strength estimation to facilitate concrete form removal and scheduling for construction [4], prediction of compressive strength of concrete subject to lasting sulfate attack [5], determination of low-, medium-, and high-strength concrete strength [6], accurate assessment of compressive.

Methods

Results

Conclusion