Fuzzy pattern classification of strong ground motion records

Abstract

The classification of earthquake strong ground motion (SGM) was performed using fuzzy pattern recognition. Simple ground motion parameters were combined and scaled nonlinearly such that the physical properties of the data could be preserved while reducing its dimensionality. The processed data was analyzed using fuzzy c-means (FCM) clustering method for representing earthquake SGM data in lower dimensions through finding subsets of mathematically similar vectors in a benchmarking database. The results showed that the stochastic behavior of earthquake ground motion records can be accurately simplified by having a few motion parameters.

Publication Title

Journal of Earthquake Engineering

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