PSO-Optimized Levenberg–Marquardt Neural Network for Predicting bond strength between concrete and corroded rebar
نویسندگان
1 Assistant Professor, Department of Civil Engineering, University of Torbat-e Jam, Torbat-e Jam, Iran
2 Assistant Professor, Department of Civil Engineering, University of Torbat-e Jam, Torbat-e Jam, Iran
3 Civil Engineering Department, University of Sistan and Baluchestan, Zahedan, Iran
4 Msc, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
doi
10.61882/NMCE.2511.1105چکیده
Rebar corrosion critically affects the durability of concrete structures, necessitating accurate prediction of bond strength between the concrete and corroded reinforcement. This study presents a novel hybrid approach, combining Monte Carlo simulations for systematic selection of the optimal Levenberg–Marquardt-based Multi-Layer Perceptron (LM-MLP) architecture with Particle Swarm Optimization (PSO) for refining network weights and biases. Using 132 experimental data points, the optimized model achieved a maximum correlation coefficient (R) of 0.959, representing an improvement of up to 3.75%, and reduced the root-mean-square error (RMSE) by up to 21.42% compared to the conventional LM-MLP model. An empirical regression model is also developed for comparison, reaffirming the superior accuracy of the proposed approach. These results demonstrate the model’s robustness and effectiveness for rapid and reliable prediction of bond strength under varying corrosion conditions. This hybrid approach not only enhances the accuracy and stability of the model but also provides rapid and reliable predictions under varying corrosion conditions, outperforming classical methods.
