  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.
