Machine Learning for Extracting Features of Approximate Optimal Brace Locations for Steel Frames - Sakaguchi, Kazuma; Ohsaki, Makoto; Kimura, Toshiaki (Frontiers in Built Environment, 2021)
A method is presented for extracting features of approximate optimal brace types and locations of large-scale steel building frames. The frame is subjected to static seismic loads and the maximum stress in the frame members is minimized under constraints on the number of braces in each story and the maximum interstory drift angle. The machine learning results of a small-scale frame are to be utilized to classify the solutions into approximate optimal and non-optimal solutions. A formulation is presented for extracting important features of brace types and locations from the learning results by support vector machine with radial basis function kernel. A nonlinear programming problem is to be solved for relating the features of a large-scale frame to those of a small-scale frame so that the important features of the large-scale frame can be extracted from the machine learning result of the small-scale frame. It is shown in the numerical example that the important features of a 24-story frame are successfully extracted using the learning results of a 12-story frame.