An Application of Neural Networks to Hysteretic Modeling of Steel Structuresby Kohsuke Yamamoto, Central Research Institute of, Electric Power Industry, Komae, Japan,
Michiya Sakai, Central Research Institute of, Electric Power Industry, Komae, Japan,
Abstract: This paper describes an application of the neural networks to realistic hysteretic modeling. The neural networks learn from sample data (training cases) and make predictions for new cases. With this new modeling strategy, the load-displacement relations obtained from experiments were recognized.
Subject Headings: Steel structures | Neural networks | Structural models | Data processing | Displacement (mechanics) | Training | Load factors
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