Integration of satellite radar interferometry and civil engineering procedures to assess building and infrastructure conditions
: (Alternative Format Thesis)

  • Valentina Macchiarulo

Student thesis: Doctoral ThesisPhD


The deterioration of ageing infrastructure assets and the risk of damaging surrounding structures during new construction are major concerns for the transport industry worldwide. Whilst, Structural Health Monitoring (SHM) could assist the lifecycle management of existing assets and the assessment of structures adjacent to large construction projects, the cost of sensors limits the number of structures that can be evaluated. Space-borne Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote-sensing technology which can provide deformation measurements over time for numerous points located on buildings and infrastructure assets. Such measurements are available with an accuracy comparable to traditional in-situ monitoring devices, but at a much lower cost. Furthermore, due to the combination of wide area coverage and high spatial resolution, InSAR can easily shift from local to larger scale, with the potential to provide a cost-effective tool for SHM on a regional level. However, to assess the condition of a given structure, InSAR deformation measurements need to be translated into structural performance indicators. This requires an integration of InSAR displacement measurements and structural information and/or models. Such integration can be particularly demanding for regional analysis, where a vast number of structures needs to be evaluated.

The objective of this thesis is to develop a new methodology using InSAR data to
monitor and assess the conditions of buildings and infrastructure assets on a regional
scale. Such a methodology will allow the understanding of the long-term behaviour
of existing infrastructure, and the assessment of damage to existing structures caused
by new infrastructure construction. This involved: (i) the definition of performance
indicators for the identification of anomalous structural deformations on a large scale;
(ii) the integration of InSAR deformation measurements and structural models to
interpret the measured deformations in relation to a specific source of movement;
(iii) a clear definition of the InSAR challenges that still need to be overcome in order
to use the developed methodology as an operational SHM tool.

First, a new method using InSAR displacement measurements to evaluate the
condition of large transport networks was developed. The proposed method is based
on an automated workflow which enables the integration of InSAR deformation measurements and digital databases of roadway and bridge infrastructure to warn of potentially anomalous deformations within a given network. For each asset and roadway segment, the method allows the assessment of monitoring point density, the retrieval of local displacements and velocities, and the identification of anomalous relative movements within the same structure. The developed method provides output maps showing the distribution of PS density, local displacements, velocities and the locations of anomalies. The developed method was tested on the Los Angeles highway and freeway network and on the Italian motorway system, validating its performance from city to national scale. Furthermore, to show the capability of the proposed method of identifying potentially damaging movements, the case of an Italian motorway viaduct that was damaged in 2015 is presented.

Second, a new method using InSAR displacement measurements for the structural
assessment of buildings adjacent to tunnel excavations was developed. The proposed method is based on an automated workflow which enables the integration of InSAR deformation measurements, digital building databases and structural models
of the building response to tunnelling-induced settlements. InSAR displacement
measurements were used to estimate the settlement profile for each building. To
calculate critical strains for each building, building settlement profiles were analysed
through a semi-empirical model. On the basis of the estimated critical strain, a level
of damage was assigned to each building. The proposed method provides as outputs
damage maps showing the distribution of damage levels for the buildings along the
excavation. The developed method was tested on the buildings along the Crossrail
tunnel alignment in London, and enabled the identification of structural damage to
more than 800 buildings, highlighting its capability as a city-scale assessment tool.
The developed integration also provided the first large dataset of field information on
building response to tunnelling, enabling the identification of relationships between
structural characteristics and building responses.

Finally, based on the technical issues encountered during this research and the
open problems identified by other researchers, a discussion about the advances needed in InSAR technology to be used as a SHM tool was developed. In this discussion, each InSAR technological limitation was evaluated from a SHM perspective. The challenges that still need to be overcome to use InSAR as an operational tool were
defined, with examples showing the practical limitations of InSAR technology. Possible
solutions and promising research directions were also identified.

The proposed methodology has the potential to inform timely maintenance and
prioritisation decisions, and can complement in-situ monitoring instrumentation. Findings can improve current practice for condition monitoring and assessment on large scale, with the possibility to advance the understanding of structural deformation mechanisms.
Date of Award17 Jan 2022
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorChris Blenkinsopp (Supervisor), Cormac Reale (Supervisor), Giorgia Giardina (Supervisor), Pietro Milillo (Supervisor) & Matthew J. DeJong (Supervisor)


  • MT-InSAR
  • remote sensing
  • deformation monitoring
  • bridges
  • roadways
  • Infrastructure vulnerability
  • buildings
  • early warning
  • settlement
  • tunnelling

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