IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v78y2015i2p779-802.html
   My bibliography  Save this article

Assessment of seismic building vulnerability based on rapid visual screening technique aided by aerial photographs on a GIS platform

Author

Listed:
  • S. Rajarathnam
  • A. Santhakumar

Abstract

The stability of buildings in seismic prone areas depends upon the structural behavior of buildings subjected to ground motion. Buildings of plan and vertical irregularities with respect to either geometry or stiffness have exhibited poor performance during past earthquakes . Identification of seismic building vulnerability is required for metropolitan cities which have moderate to high level of seismic proneness and have a high building density. Inspection of a large number of buildings individually requires enormous time and manpower. Using aerial photograph approach in GIS platform is an ideal choice to overcome these constraints. Chennai city is the thirty-fourth largest city in the world and prone to moderate earthquake hazards. This paper presents a methodology to analyze and predict the seismic vulnerability of buildings with five storeys and above in Chennai. Aerial photographic data of all buildings, land features and roads, within the city and its direct surroundings, were digitized and incorporated on a GIS platform, resulting in a digital vector map. A number of structural parameters determined on the basis of engineering knowledge and observations through rapid visual screening (RVS) have been used to identify the plan and vertical irregularities of the buildings. A new vulnerability scoring system to identify the level of damageability has been introduced based on RVS technique and compared with traditional grading system. Subsequent to RVS, a database of buildings having different range of vulnerability scores has been prepared. The percentage of desirable and undesirable features of buildings with respect to their contribution to damage was assessed based on past experiences and vulnerability scores of buildings. The survey shows that 30 % of the buildings are in the A category of being vulnerable to collapse. There are no buildings that can be classified as defect-free. It is also to note that 55–70 % of buildings lie in the category of D (low damage vulnerability). Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • S. Rajarathnam & A. Santhakumar, 2015. "Assessment of seismic building vulnerability based on rapid visual screening technique aided by aerial photographs on a GIS platform," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(2), pages 779-802, September.
    • Handle: RePEc:spr:nathaz:v:78:y:2015:i:2:p:779-802
    DOI: 10.1007/s11069-014-1382-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-014-1382-2
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11069-014-1382-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Onur Coskun & Alper Aldemir, 2023. "Machine learning network suitable for accurate rapid seismic risk estimation of masonry building stocks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 261-287, January.
    2. Yu Zhao & Bo Huang & Zhizhong Zhu & Jiachen Guo & Jianqun Jiang, 2024. "Building information extraction and earthquake damage prediction in an old urban area based on UAV oblique photogrammetry," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11665-11692, October.
    3. Nurullah Bektaş & Orsolya Kegyes-Brassai, 2022. "Conventional RVS Methods for Seismic Risk Assessment for Estimating the Current Situation of Existing Buildings: A State-of-the-Art Review," Sustainability, MDPI, vol. 14(5), pages 1-40, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:78:y:2015:i:2:p:779-802. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.