A hybrid PSO-FFNN approach for optimized seismic design and accurate structural response prediction in steel moment-resisting frames.

The first steel is the most prevalent material used in building. Steel's intrinsic hardness and durability make it appropriate for different uses, but its greater adaptability makes it ideal for seismic design. The brittle fracture occurred in welded moment connections of steel structures, which were originally thought to be ductile for resistance to earthquakes. The research aims to optimize structural parameters in steel structure seismic design. This paper presents an effective technique for the best seismic design of steel structures, which consists of two computational methodologies. First, particle swarm optimization (PSO) was presented to accurately define the structural characteristics in the seismic design of steel constructions, then a feed-forward neural network (FFNN) to determine unconventional seismic design methodologies for steel frameworks, precisely forecast the structural responses, and improve seismic resistance and dependability under dynamic conditions by using high-tech components and technological advancements. This study presents designing a realistic storey steel moment-resisting frame (MRF) structure and maximum weight under full seismic loading. The outcome demonstrates the reduction in generations that was accomplished during the optimization procedure. Although the PSO method in the paper converges in lower generations, the process indeed requires a significant amount of computing power. The FFNN approach involves the suggestion of a neural network model that works well to predict the necessary structural reactions during optimization. The proposed model considerably minimizes the total computation time. Study aims to improve the seismic analysis of steel using PSO along with forecasting structural responses using a network of feed-forward neural networks (FFNN) to enhance accuracy and reduce the computation time (2.4 min). The proposed FFNN model is more accurate than earlier methods, with the lowest MAPE values in S_IO (3.0661), S_LS (3.562), and S_CP (3.9252). Moreover, it reveals the highest predictive precision with the lowest RRMSE values of 0.0231 (S_IO), 0.0281 (S_LS), and 0.0314 (S_CP). Moreover, the FFNN model has a competitive run time of 2.4 minutes while possessing good goodness-of-fit, with 1.0096, 1.0995, and 0.9925 of R2 for S_IO, S_LS, and S_CP, respectively. As compared to WCFBP-RB, the proposed PSO+FFNN model has better prediction for S_IO, where the predicted value of 0.7879 is almost identical to the actual value of 0.8000. As compared to WCFBP-RB, the model predicts 1.4085 for S_LS, while the actual value is 1.4388. For S_CP, PSO+FFNN predicts 1.8621, which is more precise than WCFBP-RB and almost equals the actual figure of 1.9000.