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Remote structural characterization of thousands of buildings from San Jose, Costa Rica

Author(s):





Medium: journal article
Language(s): English
Published in: Frontiers in Built Environment, , v. 8
DOI: 10.3389/fbuil.2022.947329
Abstract:

The present work shows the implementation of a data collection strategy for characterizing large amounts of buildings efficiently by the conduction of remote surveys on 360° panoramic images and aerial photographs. A set of 7,296 buildings from the Latin American city of San José, Costa Rica were studied and characterized from a structural engineering point of view, obtaining information like occupancy type, height, type of lateral load resisting system, and structural irregularities, among others. Also, an estimation of the error of the remote surveys was performed, by contrasting its results with the ones of field (in situ) surveys applied on a subset of 556 structures denominated “control buildings.” The results show that for San José buildings, the predominant occupancy type, height, type, and material of the lateral load resisting system are, respectively, residential, one or two storey, wall type of confined-reinforced masonry. The overall precision level estimated for the remote surveys was 75 %, which the authors consider acceptable and an improvement when compared to more popular surveys, for example, the field surveys carried out during a population and housing census that typically have an estimated precision level of 50 %. The results proved the adopted strategy to be a promising one, albeit subject to improvements to increase its precision and reduce the implementation time.

Copyright: © Luis C. Esquivel-Salas, Víctor Schmidt-Díaz, Massimiliano Pittore, Diego Hidalgo-Leiva, Michael Haas, Aarón Moya-Fernández
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

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  • About this
    data sheet
  • Reference-ID
    10693785
  • Published on:
    23/09/2022
  • Last updated on:
    10/11/2022
 
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