An Efficient Algorithm to Identify Strong-Velocity Pulses in Multicomponent Ground Motions

Abstract

Ground motions with strong velocity pulses are of special concern for structural engineers. We describe an efficient and quantitative method to identify such ground motions. Previous algorithms to classify these pulse-like ground motions considered the ground motion in a single orientation, which made classifying pulses in arbitrary orientations difficult. We propose an algorithm that can identify pulses at arbitrary orientations in multicomponent ground motions, with little extra computational cost relative to a single-orientation calculation. We use continuous wavelet transforms of two orthogonal components of the ground motion to identify the orientations most likely to contain a pulse. The wavelet transform results are then used to extract pulses from the selected orientations, and a new classification criterion based on support vector machines is proposed. Because we are mostly interested in forward directivity pulses, which are found early in the time history, a criterion to reject pulses arriving late in the time history is also proposed. The procedure was used to classify ground motions in the Next Generation Attenuation-West2 database (Ancheta et al., 2013). The list of pulse-like ground motions was then manually filtered using sourceto-site geometry and site conditions to find the pulses most likely caused by directivity effects. Lists of both pulse-like ground motions and directivity ground motions are provided, along with the periods of the pulses and the orientations in which the pulses were strongest. Using the classification results, new models to predict the probability of a pulse and pulse period for a given future earthquake scenario are developed.

Analytical modelling of near‐source pulse‐like seismic demand for multi‐linear backbone oscillators

“…The present study employs a dataset of one‐hundred and thirty pulse‐like NS ground motions, whose impulsive nature is believed to be related to rupture directivity. The methodologies for pulse identification adopted while assembling this dataset were those suggested in and . The NS‐FD ground motion dataset employed in served as a starting point and was subsequently enriched by records from more recent seismic events, such as the Parkfield 2004 (California) event, the Darfield 2010 and Christchurch 2011 (New Zealand) events, and the South Napa 2014 (California) event.…”

Near -Source Pulse-Like Seismic Demand for Multi-Linear Backbone Oscillators

“…Having as a starting point the dataset used in [9], the pulse identification approaches suggested in [10] and [11] were used to seek out additional directivity ground motions. This search mainly focused on more recent seismic events which provided a multitude of NS recordings, such as the Parkfield 2004 (California) event, the Darfield 2010 and Christchurch 2011 (New Zealand) events and the South Napa 2014 (California) event.…”

An improved energy‐based approach for selecting pulse‐like ground motions

“…Mukhopadhyay and Gupta [8] (hereafter abbreviated as the E M&G method) suggested identifying velocity pulses based on the maximum fractional energy contributed by any half-cycle pulses. Based on the Baker's algorithm, Shahi, and Baker [11] advanced a modified way of classifying pulses in arbitrary orientations in multicomponent ground motions. Hayden et al [10] developed a new pulse-classification scheme, which involves several somewhat complex steps, like filtering the record, identifying the largest PPV pulse, and scoring the motion.…”