Shear strength of light weight concrete elements model based on deep neural network and COVID-19 optimization

Abstract

Abstract Predicting the shear strength of concrete elements is a complex challenge influenced by numerous factors, with the type of concrete playing a decisive role in structural performance. Lightweight concrete, offering a superior strength-to-weight ratio and improved thermal properties compared to conventional weight concrete, has gained increasing adoption in structural applications. This study proposes a deep neural network (DNN) model optimized using the COVID-19 optimization algorithm to predict the shear strength of lightweight concrete elements. The optimization process determines the optimal initial weights and biases of the DNN, enhancing convergence and accuracy. The proposed model was evaluated against three international design codes—ACI, EC2, and JSCE—using experimental datasets. Results show that the COVID-19–optimized DNN closely simulates and tracks actual shear strength values, even in highly nonlinear data regions (e.g., samples 90–120), where traditional models produce larger deviations. Quantitatively, the proposed DNN achieved the lowest average error (0.692) compared to the higher errors from ACI, EC2, and JSCE models. These findings demonstrate the model’s superior predictive capability and its potential to enhance design accuracy for lightweight concrete structures.