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Surmounting the complexities in analyzing the mechanical parameters ofconcrete entails selecting an appropriate methodology. This study integrates anartificial neural network (ANN) with a novel metaheuristic technique, namelysatin bowerbird optimizer (SBO) for predicting uniaxial compressive strength(UCS) of concrete. For this purpose, the created hybrid is trained and testedusing a relatively large dataset collected from the published literature. Threeother new algorithms, namely Henry gas solubility optimization (HGSO),sunflower optimization (SFO), and vortex search algorithm (VSA) are also usedas benchmarks. After attaining a proper population size for all algorithms,Utilizing various accuracy indicators, it was shown that the proposed ANN-SBOnot only can excellently analyze the UCS behavior, but also outperforms allthree benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In theprediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and0.95663, 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, andANN-SBO, respectively, manifested the best prediction performance for theproposed 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 forpredicting the UCS of concrete.

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