LOAD DISTRIBUTION

This study focused on the analysis of load distribution in reinforced concrete (RC) bridge decks using the Guyon–Massonnet–Bares (GMB) method, enhanced through artificial intelligence (AI) and MATLAB integration. The primary aim was to simplify and automate the lengthy manual calculations typically associated with the GMB method by employing AI-assisted computation and visualization tools. Bridge deck parameters were obtained for a 25 m span bridge, and traditional analytical procedures were performed to determine the composite moment of inertia, centroidal properties, bending moments, and required reinforcement area. To improve efficiency, ChatGPT was utilized to generate MATLAB scripts based on defined parameters, enabling automated computation, graphical validation, and comparison of results with manual calculations. The generated MATLAB program successfully reproduced the analytical outcomes, verified bending moment distributions, and produced visual outputs such as load distribution profiles, bending moment diagrams, and influence lines. The integration of AI in bridge analysis effectively reduced human error, saved computational time by approximately 75%, and served as a dynamic learning platform for engineers and students. Benchmarked against classical GMB results, the AI-enhanced system achieved over 95% predictive accuracy, confirming its reliability and ability to simplify complex structural analysis without compromising computational precision