Enhanced Prediction of the Flexural Capacity of Prestressed Reinforced Concrete Beams Using an Improved PSO-ANN Model - Tran, Minh Thu; Le, Linh (Engineering, Technology & Applied Science Research, 2026)
Accurate prediction of the flexural capacity of Prestressed Reinforced Concrete (PRC) beams remains a complex task due to nonlinear interactions among mechanical properties, environmental influences, and deterioration mechanisms such as corrosion and sustained loading. This study proposes a novel hybrid prediction framework that integrates an Improved Particle Swarm Optimization (IPSO) algorithm with an Artificial Neural Network (ANN) to enhance predictive performance in both accuracy and computational efficiency. The key innovation lies in the IPSO algorithm, which employs adaptive inertia weights and dynamic acceleration coefficients to effectively balance global exploration and local exploitation during training, thereby accelerating convergence and preventing premature convergence to local optima. To ensure model robustness, a unique dataset was synthetically generated using Monte Carlo simulations to reflect realistic variability in critical factors, including load levels, corrosion ratios, concrete strength, temperature, and humidity, based on actual experimental configurations. The proposed IPSO-ANN model significantly outperformed baseline models (standard PSO-ANN and Adam-ANN), as demonstrated by its superior results in Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Moreover, it achieved a notable reduction in computation time compared to the standard PSO, highlighting the algorithm's efficiency.