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http://dx.doi.org/10.12989/scs.2022.45.2.281

ANN-Incorporated satin bowerbird optimizer for predicting uniaxial compressive strength of concrete  

Wu, Dizi (School of Architecture, Changsha University of Science and Technology)
LI, Shuhua (China Construction Second Engineering Bureau LTD.)
Moayedi, Hossein (Institute of Research and Development, Duy Tan University)
CIFCI, Mehmet Akif (Department of Computer Engineering, Bandirma Onyedi Eylul University)
Le, Binh Nguyen (Institute of Research and Development, Duy Tan University)
Publication Information
Steel and Composite Structures / v.45, no.2, 2022 , pp. 281-291 More about this Journal
Abstract
Surmounting complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) with artificial neural network (ANN) 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, the 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.
Keywords
CFSTC column; Concrete; Compression capacity; Neural computing; Satin bowerbird optimizer;
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