2006
DOI: 10.1016/j.soildyn.2005.12.011
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A simple approach to integration of acceleration data for dynamic soil–structure interaction analysis

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Cited by 131 publications
(68 citation statements)
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“…This method is frequently used in strong-motion earthquake research to acquire the structure displacement from ground motion acceleration. It is widely recognised that velocity results cannot be obtained by simple integration without pre-processing of the acceleration data (Boore, 2005, Yang et al, 2006, Stiros, 2008, Chen et al, 2010. Direct integration of acceleration records often causes unrealistic drifts in displacements and velocities (Yang et al, 2006).…”
Section: Resultsmentioning
confidence: 99%
“…This method is frequently used in strong-motion earthquake research to acquire the structure displacement from ground motion acceleration. It is widely recognised that velocity results cannot be obtained by simple integration without pre-processing of the acceleration data (Boore, 2005, Yang et al, 2006, Stiros, 2008, Chen et al, 2010. Direct integration of acceleration records often causes unrealistic drifts in displacements and velocities (Yang et al, 2006).…”
Section: Resultsmentioning
confidence: 99%
“…Chiu (1997) discusses different sources of errors that can be identified to explain the drifts in velocities and displacements computed by directly integrating raw acceleration records, including digitisation errors, low-frequency instrument noise, low-frequency background noise and mechanical or electrical hysteresis in a sensor. Baseline errors in particular have been shown to cause severe drifts if appropriate pre-processing schemes are not adopted (Yang et al, 2006). In fact, although the shifts in the baseline (zero level) are negligible in the acceleration signal, they may have a dramatic impact in computing the corresponding velocity and displacement waveforms.…”
Section: Methodsmentioning
confidence: 99%
“…An established process of quantitatively estimating the trend of outcomes, also known as curve-fitting, therefore becomes necessary [2]. The best-fitting curve can be obtained by the method of least squares [48]. The method of least squares assumes that the best-fitting curve of a given type is the curve that has the minimal sum of the deviations squared (least square error) from a given set of data.…”
Section: Parameter Extractionmentioning
confidence: 99%