Metaheuristic-Driven Machine Learning for Best-Observed Feasible Weight Estimation of Braced Steel High-Rise Frames

Preliminary selection of bracing systems in high-rise steel buildings requires comparing many configurations, each demanding computationally intensive minimum-weight design. This study uses machine learning to estimate the best-observed feasible weight directly from building-level configuration parameters. A full factorial dataset of 45 configurations covered three storey numbers (17, 24, and 30), three bracing types (V, X, and K), and five brace-placement layouts. Each configuration was optimized in a coupled SAP2000–MATLAB procedure with Particle Swarm, Grey Wolf, Honey Badger, and Aquila optimization (180 runs in total); no single algorithm was best for every configuration. Mean weight increased from 2537 kN at 17 storeys to 7103 kN at 30 storeys. X-bracing gave the highest mean weight at all heights and K-bracing the lowest at 17 and 24 storeys, whereas V- and K-bracing differed by only 2.4% at 30 storeys. Bracing type changed the mean weight by up to about 30%, and brace placement by about 10–15%. Among six regression models, Ridge achieved the highest mean five-fold cross-validated R2 (0.914; 0.904 over 1000 repeated partitions), while Random Forest gave the lowest RMSE (511.7 kN); in leave-one-out prediction, Ridge ordered 90.3% of configuration pairs correctly. The resulting Ridge equation offers an interpretable screening tool within the investigated design domain.