A SURROGATE APPROACH FOR ACCURATE ESTIMATION OF STRUCTURAL RESPONSE IN STEEL FRAME OPTIMIZATION

Structural design seeks to achieve optimal performance with minimum cost while meeting code requirements. Evaluating optimized designs usually depends on finite element analysis, which is computationally expensive. Recently, surrogate models have been developed to predict structural behavior more efficiently. Among these, Support Vector Machine (SVM) has become a reliable tool in civil engineering. However, the predictive power of SVM is highly dependent on proper parameter tuning. This study introduces the Improved Electric Eel Foraging Optimization Algorithm (I-EEFO) for training SVM to estimate the response of steel frames. Two benchmark structures, a ۲‑story and a ۷‑story steel frame, were analyzed, and the results were compared with other metaheuristic algorithms. The proposed method achieved very high accuracy: mean squared errors of ۱.۱۱E‑۱۳ for the ۲‑story frame and ۲.۹۹E‑۰۷ meters for the ۷‑story frame over ۱۰ runs. The root mean square errors for displacement prediction on test data were ۲.۶۷E‑۰۷ and ۷.۲۳E‑۰۴ meters, respectively, confirming reliable estimates. Convergence curves demonstrated that I‑EEFO converges faster and more effectively than competing methods. These findings highlight the potential of the proposed approach as a robust and computationally efficient alternative to traditional simulations, offering engineers a practical tool to reduce costs in structural design without compromising accuracy.

کلیدواژه ها:

Electric Eel Foraging Optimization ، Structural Design ، Metaheuristic algorithm ، Machine Learning ، Tall Building