Analyzing Uniaxial Compressive Strength of Concrete Using a Novel Satin Bowerbird Optimizer

TL;DR

This paper introduces a hybrid artificial neural network combined with a novel satin bowerbird optimizer to accurately predict the uniaxial compressive strength of concrete, outperforming other recent algorithms.

Contribution

The study presents a new hybrid ANN-SBO model that enhances prediction accuracy of concrete strength compared to existing algorithms.

Findings

ANN-SBO outperforms benchmark algorithms in accuracy.

Correlation index of 0.95663 achieved by ANN-SBO.

ANN-SBO demonstrates high reliability for practical engineering use.

Abstract

Surmounting the complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates an artificial neural network (ANN) with a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.