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<p>Physics-based simulations (PBS) have found vast application in the generation of non-ergodic ground motion. However, due to their computational requirement, they are combined with other methodologies in a hybrid framework to expand the effect to a broadband frequency range. The present work proposes a new Artificial Neural Network (ANN) model developed using strong motion records from the Engineering Strong Motion (ESM) database, which predicts Fourier Amplitude Spectra (FAS) values in the high-frequency range. The developed model performed well in statistical comparisons and could capture various earthquake scaling effects. Further, the work includes the 2023 Elbistan, Türkiye earthquake as the case study for model validation. It was found that the model-predicted values correspond well with the recorded data for both spectra and ground motion Ground Motion Intensity Measures (GMIMs). The developed model is useful in performing non-ergodic seismic hazard analysis and obtaining broadband time histories necessary for non-linear time history analysis of structures.</p>

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ANN-based broadband modelling of Fourier amplitude spectra through physics-based simulations: application to 2023 Elbistan earthquake

  • 期刊论文

  • 发表日期：

    2026年2月10日

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  • Varun Sharma,
  • J. Dhanya,
  • Maheshreddy Gade,
  • Harsh Kumar Arya

摘要

Physics-based simulations (PBS) have found vast application in the generation of non-ergodic ground motion. However, due to their computational requirement, they are combined with other methodologies in a hybrid framework to expand the effect to a broadband frequency range. The present work proposes a new Artificial Neural Network (ANN) model developed using strong motion records from the Engineering Strong Motion (ESM) database, which predicts Fourier Amplitude Spectra (FAS) values in the high-frequency range. The developed model performed well in statistical comparisons and could capture various earthquake scaling effects. Further, the work includes the 2023 Elbistan, Türkiye earthquake as the case study for model validation. It was found that the model-predicted values correspond well with the recorded data for both spectra and ground motion Ground Motion Intensity Measures (GMIMs). The developed model is useful in performing non-ergodic seismic hazard analysis and obtaining broadband time histories necessary for non-linear time history analysis of structures.