Predicting nominal shear capacity of reinforced concrete wall in building by metaheuristics-optimized machine learning

Abstract

Reinforced concrete shear walls are used in many structural systems to resist earthquake loading. In recent earthquakes, shear wall buildings have tended to perform well. Modern building codes include provisions concerning shear capacity, which are recognized for their effectiveness. Studies have demonstrated that the American Concrete Institute (ACI) 318-19 provision uses a low safety factor and does not cover high-strength concrete shear walls whereas the Eurocode 8 provision is overly conservative. A rational method for predicting shear wall capacity could be used as an alternative to the simplified provision in the codes. Nevertheless, the use of rational methods may present some difficulties for structural engineers because they require an iterative calculation to determine the peak strengths of shear walls. Accordingly, an appropriate data-driven machine learning scheme that accurately determines shear capacity is needed. Three experimental cases that involve various input variables are adopted herein to train single models and ensemble models. Numerical analytics show that the best result is achieved by using extreme gradient boosting (XGBoost), which involves conventional parameters and synthetic parameters that are inspired by the ACI shear wall strength equation. Subsequently, two metaheuristic optimization algorithms are used to fine-tune the hyperparameters of the generally recognized XGBoost. Two proposed metaheuristically hybrid models, jellyfish search (JS)-XGBoost and symbiotic organisms search (SOS)-XGBoost, outperform the ACI provision equation and grid search optimization (GSO)-XGBoost in the literature in predicting the nominal capacity of reinforced concrete shear walls in buildings. Metaheuristics-optimized machine learning models can be used to improve building safety, simplify a cumbersome shear capacity calculation process, and reduce material costs. The systematic approach that is utilized herein also serves as a general framework for quantifying the performance of various mechanical models and empirical formulas that are used in design standards.