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Volume 8, Number 1, January 2023 , pages 37-59 DOI: https://doi.org/10.12989/acd.2023.8.1.039 |
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Ensemble techniques and hybrid intelligence algorithms for shear strength prediction of squat reinforced concrete walls |
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Mohammad Sadegh Barkhordari and Leonardo M. Massone
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| Abstract | ||
| Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R2 = 0.99) that are superior to other models. | ||
| Key Words | ||
| ensemble learning methods; optimization algorithm; shear strength; squat reinforced concrete wall | ||
| Address | ||
| Mohammad Sadegh Barkhordari: Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran Leonardo M. Massone: Department of Civil Engineering, University of Chile, Blanco Encalada 2002, Santiago, Chile | ||
