Modeling and Prediction of Nonstationary Ground Motions as Time–Frequency Images

Jale Tezcan, Jie Cheng, Qiang Cheng

Research output: Contribution to journalArticlepeer-review

7 Citations (SciVal)

Abstract

Proper modeling of nonstationary characteristics of ground motions is critical in estimating the seismic performance of structures. The importance of ground motion nonstationarity is being increasingly recognized in seismic design codes and provisions. This paper develops a new method for predicting seismic ground motions in the joint time–frequency domain based on recorded ground motions. We treat wavelet energy maps of acceleration records as images and extract essential patterns from them using principal component analysis. These patterns are then linked to the seismic source, path, and site variables using general regression neural network. The resulting model predicts an image representing the expected evolutionary power spectrum conditioned on the input variables. An example application is presented using records from the Next Generation Attenuation of Ground Motions Database of the Pacific Earthquake Engineering Center. As opposed to conventional ground motion prediction models, the proposed approach retains the time domain characteristics of ground motions. The results show that the proposed model can predict significant patterns in the seismic energy distribution, acceleration time series, and 5% damped elastic spectral accelerations.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalIEEE Transactions on Geoscience and Remote Sensing
DOIs
StateAccepted/In press - 2023

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Artificial neural network (ANN)
  • continuous wavelet transform (CWT)
  • ground motion prediction
  • principal component analysis (PCA)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences

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