Settlement-induced building damage assessment using MT-INSAR data for the crossrail case study in London

Conference Paper (2019)
Author(s)

V. Macchiarulo (University of Bath)

Giorgia Giardina (University of Bath)

P. Milillo (California Institute of Technology)

Javier González Martí (Sixense Group)

J. Sánchez (TRE-Altamira)

M. J. Dejong (University of California)

Affiliation
External organisation
DOI related publication
https://doi.org/10.1680/icsic.64669.721
More Info
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Publication Year
2019
Language
English
Affiliation
External organisation
Pages (from-to)
721-727
ISBN (electronic)
9780727764669

Abstract

The expansion of modern cities causes a growing demand for efficient transport facilities, motivating the realization of large urban tunnelling projects. A major concern during tunnelling operations is the evaluation of the response of existing buildings to induced ground movements, with a considerable economic impact on the total project cost. Since traditional monitoring is based on costly in-situ installations, only a limited amount of observations is typically provided for each structure. Without a systematic increase in the availability of monitored building deformations, lesson learned from previous projects cannot be effectively used to improve existing damage assessment procedures. Satellite Interferometric Synthetic Aperture Radar (InSAR) techniques, providing high quality measurements of building deformations, have been recently validated to be used in combination with damage assessment procedures. In particular, Multi-Temporal InSAR can provide cumulative displacement maps for a high density of monitoring points with millimetric accuracy, enabling to assess buildings nearby the excavation. In this paper, results from post-tunnelling damage assessment of hundreds of buildings along the Crossrail route in London are presented. By exploiting InSAR-based displacements, a semi-automated assessment tool is used to investigate the structural response to tunnelling-induced settlements. Results are compared to the ones obtained through the greenfield-based assessment, enabling to evaluate their accuracy. The application of the proposed method to real case studies highlights its potential for the quasi-real time identification of building damage levels over extensive area. This semi-automatic procedure can positively impact the construction industry, enabling to improve traditional damage assessment strategies and to complement ground-based monitoring systems.

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