A method is presented for optimal placement of braces of plane frames using machine learning. The frame is subjected to static horizontal loads representing seismic loads. We consider the process of seismic retrofit by attaching braces. Therefore, the maximum value of additional stresses in the existing beams and columns and the maximum interstory drift angle are incorporated in the optimization problem. Characteristics of approximate optimal solutions and nonoptimal solutions are extracted using machine learning based on support vector machine and binary decision tree. Convolution and pooling are used for defining the features characterizing the solutions while reducing the number of variables. Optimization is carried out using a heuristic algorithm called simulated annealing based on local search. It is shown in the numerical examples that the computational cost is successfully reduced by avoiding costly structural analysis for a solution judged by machine learning as nonoptimal, and the important features in approximate optimal and nonoptimal solutions are identified.
There are two major types of lateral frame systems in steel buildings, which are space frame system (SFS) and perimeter frame system (PFS). Moment connections are used in most of beam-to-column connections in SFS, while they are limitedly used in the perimeter frames in PFS. In this study, structural characteristics of 7-story standard steel office buildings designed with SFS and PFS are investigated. Steel volume is minimized by using Multiple Start Local Search (MSLS) under constraints on allowable stress design and ultimate lateral strength requirements. The design variables are the discrete section sizes. The steel volume of the obtained solutions is lower for PFS than SFS.
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