@article { author = {Rezaeemanesh, Mansoureh and Mashayekhi, Mohammadreza}, title = {Investigating the Correlation between the Parameters of Ground Motion Intensity Measures for Iran's Data}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {6}, number = {4}, pages = {59-82}, year = {2022}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2022.344135.1450}, abstract = {This paper presents a statistical correlation analysis of peak ground acceleration to peak ground velocity ratio (A/V) and other ground motion intensity measures (IMs) for Iran’s data. A/V is an important parameter that can significantly affect nonlinear structural responses. Findings from this study provide beneficial insights into selecting suitable parameters for characterizing earthquake ground motions. The studied database included 2053 strong ground motion records with the moment magnitude from 4.5 to 7.8 MW, rupture distance from 1 to 600 km, and average shear wave velocity from 155 to 1594 m/s. Correlation coefficients between A/V and several IMs were obtained for near-field and far-field records at three A/V levels, low A/V, middle A/V, and high A/V. Regression analyses for predicting A/V from the IMs were also conducted for near-field and far-field records. The results showed that the mean period (Tm) has the highest correlation with A/V at all A/V levels and for both far-field and near-field earthquakes compared to the other IMs. Therefore, this parameter can be employed for record selection as a frequency content-based parameter. Finally, current results showed that the accuracy of the Artificial Neural Network (ANN) models are more than the regression models for predicting A/V.}, keywords = {Ground motion intensity measures,Near-field earthquakes,Far-field earthquakes,A/V}, url = {https://www.jsoftcivil.com/article_158234.html}, eprint = {https://www.jsoftcivil.com/article_158234_bc6e339bfc8ce8a25183e25bd77b8323.pdf} }