Top > JDR > Development of Building Inventory Data and Earthquake Damage ...

single-dr.php

JDR Vol.9 No.6 pp. 1032-1041
(2014)
doi: 10.20965/jdr.2014.p1032

Paper:

Views over last 60 days: 846

Development of Building Inventory Data and Earthquake Damage Estimation in Lima, Peru for Future Earthquakes

Masashi Matsuoka*1, Shun Mito*2, Saburoh Midorikawa*1,
Hiroyuki Miura*3, Luis G. Quiroz*4, Yoshihisa Maruyama*4,
and Miguel Estrada*5

*1Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Nagatsuta 4259-G3, Midori-ku, Yokohama 226-8502, Japan

*2Mitsubishi Jisho Property Management Co., Ltd., Tokyo, Japan

*3Graduate School of Engineering, Hiroshima University, Hiroshima, Japan

*4Graduate School of Engineering, Chiba University, Chiba, Japan

*5Japan-Peru Center for Earthquake Engineering and Disaster Mitigation (CISMID), National University of Engineering, Lima, Peru

Received:
August 6, 2014
Accepted:
September 1, 2014
Published:
December 1, 2014
Creative Commons License
Keywords:
building inventory data, earthquake damage estimation, census data, fragility curve, socio-economic class, satellite imagery
Abstract
Even though detailed building inventory data are necessary for estimating earthquake damage reliably, most developing countries do not have sufficient data for such estimations. This necessitates a way for finding building distribution and feature easily. In this study for estimating the number of households in all building categories of different structures or floor numbers in Lima, Peru, where a great earthquake is expected, we propose an estimation method based on existing GIS data from a census, satellite imagery, and building data from field surveys, and apply it to estimate the entire area of Lima for create building inventory data. Building fragility functions were used to calculate a severe damage ratio of buildings due to the expected earthquake. The rate was multiplied by created building inventory data to estimate the number of households in damaged buildings. Furthermore we clarified damage reduction by retrofitting for low earthquake-resistant buildings.
Cite this article as:
M. Matsuoka, S. Mito, S. Midorikawa, H. Miura, L. Quiroz, Y. Maruyama, and M. Estrada, “Development of Building Inventory Data and Earthquake Damage Estimation in Lima, Peru for Future Earthquakes,” J. Disaster Res., Vol.9 No.6, pp. 1032-1041, 2014.
Data files:
References
  1. [1] K. Hasegawa and S. Midorikawa, “Seismic Risk Mapping of Wooden House in Large Area Using the Grid-square Statistics: Part 1 Estimation of population of wooden houses with different construction age,” Journal of Structural and Construction Engineering, Architectural Institute of Japan, No.497, pp. 75-80, 1997 (in Japanese with English abstract).
  2. [2] K. Hasegawa and S. Midorikawa, “Seismic Risk Mapping of Nonwooden Building in Large Area Using the Grid-square Statistics,” Journal of Structural and Construction Engineering, Architectural Institute of Japan, No.521, pp. 41-47, 1999 (in Japanese with English abstract).
  3. [3] H. Miura and S. Midorikawa, “Updating GIS Building Inventory Data Using High-resolution Satellite Images for Earthquake Damage Assessment: Application to Metro Manila, Philippines,” Earthquake Spectra, Vol.22, No.1, pp. 151-168, 2006.
  4. [4] I. F. Shaker, A. Abd-Elrahman, A. K. Abdel-Gawad, and M. A. Sherief, “Building Extraction from High Resolution Space Images in High Density Residential Areas in the Great Cairo Region,” Remote Sensing, Vol.3, No.4, pp. 781-791, 2011.
  5. [5] M. Matsuoka, H. Miura, S. Midorikawa, and M. Estrada, “Extraction of Urban Information for Seismic Hazard and Risk Assessment in Lima, Peru Using Satellite Imagery,” Journal of Disaster Research, Vol.8, No.2, pp. 328-345, 2013.
  6. [6] Instituto Nacional de Estadistica e Informatica (INEI): National Census 2007: Population XI and VI Housing,
    http://www.inei.gob.pe/estadisticas/censos/ [access available on Jul.1, 2014]
  7. [7] J. Takaku and T. Tadano, “PRISM On-Orbit Geometric Calibration and DSM Performance,” IEEE Transaction on Geoscience and Remote Sensing, Vol.47, No.12, pp. 4060-4073, 2009.
  8. [8] Federal Emergency Management Agency (FEMA): HAZUS®MH MR4 Earthquake Model Technical Manual, Washington D.C., 2009.
  9. [9] C. Zavala, M. Estrada, F Lazares, S. Alarcon, J. Taira, and J. Morales, “Quick Risk Evaluation of Earthquake Losses on Housing,” Proc. Of 15th World Conference on Earthquake Engineering, ID: 3779, p. 9, 2012.
  10. [10] H. Miura, S. Midorikawa, K. Fujimoto, B. M. Pacheco, and H. Yamanaka, “Earthquake Damage Estimation in Metro Manila, Philippines based on Seismic Performance of Buildings Evaluated by Local Experts’ Judgments,” Soil Dynamics and Earthquake Engineering, Vol.28, pp. 764-777, 2008.
  11. [11] B. Grunthal, “European Macroseismic Scale 1998,” European Seismological Commission, 1998.
  12. [12] S. Matsuzaki, N. Pulido, H. Yamanaka, K. Chimoto, Y. Maruyama, and F. Yamazaki, “Vulnerability Estimation of Building Using Damage Survey Data Following the 2007 Pisco, Peru Earthquake,” Journal of Social Safety Science, No.21, pp. 27-36, 2013 (in Japanese with English abstract).
  13. [13] L. G. Quiroz and Y. Maruyama, “Assessment of performance of Peruvian high-rise thin RC wall buildings using numerical fragility functions,” Proceedings of the 2014 World Congress on Advances in Civil, Environmental, & Material Research (ACEM14), Paper No. M4F.3.CC406 728F, 2014.
  14. [14] N. Pulido, Z. Aguilar, H. Tavera, M. Chlieh, D. Calderón, T. Sekiguchi, S. Nakai, and F. Yamazaki, “Source Models Scenarios and Strong Ground Motion for Future Mega-earthquakes: Application to Lima, Central Peru,” Bulletin of Seismological Society of America, 2014 (in press).

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 18, 2024