Spectral acceleration prediction using genetic programming based approaches

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@Article{GANDOMI:2021:ASC,

• author = "Mostafa Gandomi and Ali R. Kashani and Ali Farhadi and Mohsen Akhani and Amir H. Gandomi",
• title = "Spectral acceleration prediction using genetic programming based approaches",
• journal = "Applied Soft Computing",
• volume = "106",
• pages = "107326",
• year = "2021",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2021.107326",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494621002490",
• keywords = "genetic algorithms, genetic programming, Spectral acceleration, Ground-motion models, Multi-gene genetic programming, Gene expression programming, Multi-objective genetic programming",
• abstract = "Evolutionary computation (EC) is a widely used computational intelligence that facilitates the formulation of a range of complex engineering problems. This study tackled two hybrid EC techniques based on genetic programming (GP) for ground motion prediction equations (GMPEs). The first method coupled regression analysis with multi-objective genetic programming. In this way, the strategy was maximizing the accuracy and minimizing the models' complexity simultaneously. The second approach incorporated mesh adaptive direct search (MADS) into gene expression programming to optimize the obtained coefficients. A big data set provided by the Pacific Earthquake Engineering Research Centre (PEER) was used for the model development. Two explicit formulations were developed during this effort. In those formulae, we correlated spectral acceleration to a set of seismological parameters, including the period of vibration, magnitude, the closest distance to the fault ruptured area, shear wave velocity averaged over the top 30 meters, and style of faulting. The GP-based models are verified by a comprehensive comparison with the most well-known methods for GMPEs. The results show that the proposed models are quite simple and straightforward. The high degrees of accuracy of the predictions are competitive with the NGA complex models. Correlations of the predicted data using GEP-MADs and MOGP-R models with the real observations seem to be better than those available in the literature. Three statistical measures for GMPEs, such as E (percent), LLH, and EDR index, confirmed those observations",

}

Genetic Programming entries for Mostafa Gandomi Ali R Kashani Ali Farhadi Mohsen Akhani A H Gandomi

Citations