Integrated method for intelligent structural design of steel frames based on optimization and machine learning algorithm

Abstract

Optimization methods using metaheuristic algorithms have been widely used in steel frame design to improve the inefficient traditional design method due to repeated model tuning and massive mechanical analysis. However, the random search feature of them may easily result in poor performances. In this paper, combining metaheuristic algorithms and machine learning methods, a highly integrated method based on an on-line model training, updating and parameter tuning process is proposed to improve the performance of the optimization algorithm with general forms and parameters. It reduces the impact of the iterative mechanism and parameter setting of metaheuristic algorithms on their performance. Such method is introduced to intelligent structural design of steel frames including three steps. The standard optimization process is conducted to search optimal design and simultaneously collect the mechanical analysis data of the structure. Then the data is adopted to generate and update surrogate models of structural responses dynamically while an analysis-based feature engineering and an automatic model tuning technique are employed to improve the model accuracy. Finally, a much more efficient procedure is presented to obtain potential solutions which are used to improve the convergence rate and performance of standard optimization. Four cases are used to study the effectiveness of the integrated method and the influence of different settings is discussed, as well as its generality. As a conclusion, the proposed method can achieve structural safety and economic benefit of steel frames, which exhibits superiorly in terms of robustness, optimal results and computational cost even in large-scale optimization problems of complicated frames.