Pattern-Search-Based Algorithm for Ground-Motion Selection with Targeted Mean and Standard Deviation

摘要

Ground-motion selection is essential as an input for dynamic structural analyses. Although recent studies have highlighted the importance of target variance as auxiliary criteria and introduced selection methods, they typically require a trade-off between computational expense and accuracy. This paper presents an efficient and accurate method for ground-motion selection and scaling that matches both the target mean and standard deviation, while using either a single-objective- or multi-objective-based pattern-search optimization technique. The selection algorithm was tested using the code-based and conditional-spectrum-based target spectrum for various design scenarios. The results indicated that the pattern-search-based selection algorithm is an optimal method as it provides the best-matching records suite with a more precise and quicker selection than other notable optimization techniques.