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

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.

Automated summary

The study explores the use of complex models such as artificial neural networks to study concrete properties, and finds that a bat algorithm-optimized ANN is more accurate in estimating compressive strength of concrete.