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

Daniel WeiJie Loi; Mavinakere Eshwaraiah Raghunandan; Madhavan Shanmugavel; Varghese Swamy

doi:10.1109/ICCIC.2014.7238557

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

Daniel WeiJie Loi, Mavinakere Eshwaraiah Raghunandan, Madhavan Shanmugavel, Varghese Swamy

Malaysia Sch of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research
Editors	N Krishnan, M Karthikeyan
Place of Publication	New Jersey USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1 - 4
Number of pages	4
ISBN (Print)	9781479939749
DOIs	https://doi.org/10.1109/ICCIC.2014.7238557
Publication status	Published - 2015
Event	IEEE International Conference on Computational Intelligence and Computing Research 2015 - Vickram College of Engineering, Madurai, India Duration: 10 Dec 2015 → 12 Dec 2015 Conference number: 6th https://ieeexplore.ieee.org/xpl/conhome/7430173/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Computational Intelligence and Computing Research 2015
Abbreviated title	ICCIC 2015
Country/Territory	India
City	Madurai
Period	10/12/15 → 12/12/15
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7430173/proceeding (Proceedings)

Access to Document

10.1109/ICCIC.2014.7238557

Cite this

Loi, D. W., Raghunandan, M. E., Shanmugavel, M., & Swamy, V. (2015). Data analytic engineering and its application in earthquake engineering: An overview. In N. Krishnan, & M. Karthikeyan (Eds.), Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research (pp. 1 - 4). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCIC.2014.7238557

Loi, Daniel WeiJie ; Raghunandan, Mavinakere Eshwaraiah ; Shanmugavel, Madhavan et al. / Data analytic engineering and its application in earthquake engineering: An overview. Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research. editor / N Krishnan ; M Karthikeyan. New Jersey USA : IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 1 - 4

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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.",

author = "Loi, {Daniel WeiJie} and Raghunandan, {Mavinakere Eshwaraiah} and Madhavan Shanmugavel and Varghese Swamy",

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Loi, DW, Raghunandan, ME, Shanmugavel, M & Swamy, V 2015, Data analytic engineering and its application in earthquake engineering: An overview. in N Krishnan & M Karthikeyan (eds), Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research. IEEE, Institute of Electrical and Electronics Engineers, New Jersey USA, pp. 1 - 4, IEEE International Conference on Computational Intelligence and Computing Research 2015, Madurai, India, 10/12/15. https://doi.org/10.1109/ICCIC.2014.7238557

Data analytic engineering and its application in earthquake engineering: An overview. / Loi, Daniel WeiJie; Raghunandan, Mavinakere Eshwaraiah; Shanmugavel, Madhavan et al.
Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research. ed. / N Krishnan; M Karthikeyan. New Jersey USA: IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 1 - 4.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - 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.

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Loi DW, Raghunandan ME, Shanmugavel M, Swamy V. Data analytic engineering and its application in earthquake engineering: An overview. In Krishnan N, Karthikeyan M, editors, Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research. New Jersey USA: IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 1 - 4 doi: 10.1109/ICCIC.2014.7238557