Data analytic engineering and its application in earthquake engineering: An overview

D. Loi, M. Raghunandan, M. Shanmugavel, V. Swamy
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Abstract

This paper deliberates the challenges of using regression models for earthquake data analysis and compares them with the field measurements. Regression analyses to model the peak ground acceleration (PGA) data are discussed with magnitude and distance as variables. Suitability of the models are further compared with the ground motion (PGA) field records obtained from the seismic stations within the peninsular Malaysia. Far field (distance above 300km from the epicenter) and local earthquakes within 50-300km with a wide range of moment magnitude (1.0-9.1) are considered in this study. Result from the regression models showed significant error between the predicted and field data. Further discussion highlights that the ground motion prediction equation (GMPE) is a function of multiple variables developed from the specific site properties. The paper concludes with a note showing the significance of statistical input and analysis in the GMPE's to achieve a more realistic earthquake data prediction model.
数据分析工程及其在地震工程中的应用综述
本文讨论了回归模型在地震资料分析中的挑战,并与实测数据进行了比较。讨论了以震级和距离为变量的峰值地加速度(PGA)数据模型的回归分析。模型的适用性进一步与马来西亚半岛地震台站的地面运动(PGA)现场记录进行了比较。本研究考虑远场地震(距震中300km以上)和50-300km范围内矩震级范围较宽(1.0-9.1)的局地地震。回归模型结果表明,预测结果与实测数据存在较大误差。进一步的讨论强调了地震动预测方程(GMPE)是由特定场地属性发展而来的多个变量的函数。文章最后还说明了GMPE中统计输入和分析对于实现更真实的地震数据预测模型的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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