Efficient selection of up to three-component ground excitation sets for earthquake engineering applications

Ground motion record selection methodologies are commonly developed to ensure that the input excitation used in response history analyses embodies essential conditions such as spectral compatibility, hazard and intensity measure consistency, seismological and site-specific criteria, always performing in a computationally efficient manner. A methodology utilizing genetic algorithms is revisited here, expanded to select multi-component ground motions and satisfying the typically required selection objectives of earthquake engineering applications, while ensuring increased efficiency. Multi-objective optimization is performed, claimed to be superior in delivering robust results that account for spectral compatibility in first and second order statistics (mean and standard deviation) in a wide range of spectral values, as well as satisfying seismological and site-specific criteria. A unique contribution is the ability to include probability distribution targets in specific ordinates of the spectrum, on top of the mean and standard deviation, allowing for more refined ground motion sets that can be used to reduce the number of records required in response history analyses. Additionally, a novel benchmarking process to assess the efficiency of ground motion record methodologies is introduced here, in terms of providing sets that are globally-optimal solutions to the optimization problem. Through this benchmarking algorithm, the proposed methodology appears to be impeccable in extracting the best possible ground motion sets.