Mathematical models for hysteresis
SIAM Review
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Structural inverse analysis by hybrid simplex artificial bee colony algorithms
Computers and Structures
Soil-structure interaction: Parameters identification using particle swarm optimization
Computers and Structures
Identification of Bouc-Wen type models using multi-objective optimization algorithms
Computers and Structures
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The lack of a fundamental theory of hysteresis is a major barrier to successful design of structures against deterioration associated with earthquakes, high winds, and sea waves. Development of a practical model of degrading structures that would match experimental observations is an important task. This paper has a twofold objective. First, a superior system identification algorithm is devised to estimate the unspecified parameters in a differential model of hysteresis from experimental load-displacement traces. This algorithm is based upon the latest theory of genetic evolution and it will be streamlined through global sensitivity analysis. Second, the utility of identification of hysteresis is demonstrated through nonlinear response prediction, which is important in structural design. Suppose a hysteretic model is generated with a given load-displacement trace. It will be shown experimentally that the model will predict the response of the same system driven by other cyclic loads. The requirements for accurate prediction will be addressed. Through identification of hysteresis, it becomes possible to assess, for the first time in analysis, the performance of a real-life structure that has previously been damaged. In the open literature, there is not any other method that can perform the same task.