Using InSAR stacking techniques to predict bridge collapse due to scour

Failure of bridges due to scour is of great concern to bridge asset owners, and is currently very difficult to predict and monitor regularly using conventional assessment methods. This paper presents evidence of how InSAR techniques can be used to monitor bridges at risk of scour, using Tadcaster Bridge, England, as a case study. Tadcaster Bridge suffered a partial collapse due to river scour on the evening of December 29th, 2015 following a period of severe rainfall and flooding. SAR scenes over the bridge from the two-year period prior to the collapse are analysed using SBAS interferometry methods, highlighting a distinct movement in the region of the bridge where the collapse occurred prior to the actual event. This precursor to failure observed in the data suggests the possible use of InSAR in structural health monitoring of bridges at risk of scour, as a means of an early warning system

Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques

Scour is the removal of ground material in water bodies due to environmental changes in water flow. It particularly occurs at bridge piers and the holes formed can make bridges susceptible to collapse. The most common cause of bridge collapse is due to scour occurring during flooding, some failures causing loss of life and most resulting in significant transport disruption and economic loss. Consequently, failure of bridges due to scour is of great concern to bridge asset owners, and is currently very difficult to predict since conventional assessment methods foresee very resource-demanding monitoring efforts in situ. This paper presents evidence of how InSAR techniques can be used to monitor bridges at risk of scour, using Tadcaster Bridge, England, as a case study. Tadcaster Bridge suffered a partial collapse due to river scour on the evening of December 29th, 2015 following a period of severe rainfall and flooding. 48 TerraSAR-X scenes over the bridge from the two-year period prior to the collapse are analysed using the small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) approach. The study highlights a distinct movement in the region of the bridge where the collapse occurred prior to the actual event. This precursor to failure observed in the data over a month before actual collapse suggests the possible use of InSAR as a means of an early warning system in structural health monitoring of bridges at risk of scour.This work was made possible by EPSRC (UK) Award 1636878, with iCase sponsorship from the National Physical Laboratory and additional funding from Laing O’Rourke

Online hybrid learning methods for real-time structural health monitoring using remote sensing and small displacement data

Structural health monitoring (SHM) by using remote sensing and synthetic aperture radar (SAR) images is a promising approach to assessing the safety and the integrity of civil structures. Apart from this issue, artificial intelligence and machine learning have brought great opportunities to SHM by learning an automated computational model for damage detection. Accordingly, this article proposes online hybrid learning methods to firstly deal with some major challenges in data-driven SHM and secondly detect damage via small displacement data from SAR images in a real-time manner. The proposed methods contain three main parts: (i) data augmentation by Hamiltonian Monte Carlo and slice sampling for addressing the problem of small displacement data, (ii) data normalization by an online deep transfer learning algorithm for removing the effects of environmental and/or operational variability from augmented data, and (iii) feature classification via a scalar novelty score. The major contributions of this research include proposing two online hybrid unsupervised learning methods and providing effective frameworks for online damage detection. A small set of displacement samples extracted from SAR images of TerraSar-X regarding a long-term monitoring scheme of the Tadcaster Bridge in United Kingdom is applied to validate the proposed methods

Interferometric Synthetic Aperture Radar for remote satellite monitoring of bridges

The structural health of critical infrastructure is difficult to assess and monitor with existing methods of evaluation which rely predominantly on visual inspection and/or the installation of sensors to measure the in-situ performance of structures. There are vast numbers of critical structures that need to be monitored and these are often located in diverse geographical locations which are difficult and costly to access. Recent advances in satellite technologies provide the opportunity for global coverage of assets and the measurement of displacement to sub-centimetre accuracy. Such measurements could supplement existing monitoring techniques and provide asset owners with additional insights which could inform operational and maintenance decisions. Most past research within the field of Interferometric Synthetic Aperture Radar (InSAR) monitoring using satellite radar imagery focusses on widespread measurement of land areas, although there have been some case studies using InSAR to assess movements of individual structures such as dams. However, there is limited published research into the use of these techniques for accurately monitoring the displacements of individual civil engineering structures over time and relating these measurements to structural performance. This research focusses on bridges as a specific example of critical infrastructure to establish whether remote satellite monitoring can be used to measure displacements at a resolution that is sufficiently accurate for use in monitoring of performance, and examines the relevance and limitations of satellite monitoring to civil engineering applications in general. In order to assess the millimetre-scale performance of InSAR, an initial evaluation was undertaken in controlled conditions on a purpose-built test bed fitted with satellite reflectors at the National Physical Laboratory in Teddington to validate InSAR displacement measurements against traditional terrestrial in-situ displacement measurements. Subsequently, traditional sensor and surveying measurements of displacements were compared with InSAR displacement measurements at key points of interest on Waterloo Bridge and the Hammersmith Flyover. A further case study on Tadcaster Bridge was undertaken to demonstrate the potential applicability of InSAR displacement measuring techniques for monitoring bridges at risk of scour failure. Scour is the most common form of bridge collapse around the world and to date no cost-effective and widely applicable method for providing advanced warning of impending failure due to scour has been developed. Methodologies for integrating digital, structural and signal processing models for the identification and mapping of InSAR measurement points on bridge structures from SAR imagery were developed, as well as methodologies for combining satellite data with traditional surveying methods. An important outcome of this research was that through comparison of independent measurements, InSAR measurements are of a scale that is applicable to bridge monitoring. Remote sensing can therefore reach global coverage, with unsupervised readings over an interval of days, and as such supplement traditional inspection regimes. However, this outcome must be presented with several limitations. Practical implications of applying InSAR to real bridges are discussed, including imaging effects and the suitability of monitoring different forms of bridge deformation. The key to successful implementation of InSAR monitoring of bridges lies in understanding the limitations and opportunities of InSAR, and making a clear case to satellite data providers on what specifications (resolution, frequency, processing assumptions) would unlock using such datasets for wider use in monitoring of infrastructure. InSAR can provide measurements and useful insights for bridge monitoring but it is limited to specific cases and, at this stage of technological development, it should be considered as a tool for specific bridges and failure mechanisms rather than a full bridge monitoring solution.This PhD was funded by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Award 1636878 with iCASE sponsorship by the National Physical Laboratory. Further funding contributions were provided by Laing O’Rourke. Projects within the PhD received funding from Innovate UK and some of the data was provided by the German Aerospace Centre (DLR) under proposal MTH3513

Evaluating the benefit of structural health monitoring for improving bridge resilience against scour. Deliverable D1 – Report on critical review of alternative techniques for bridge scour monitoring

No abstract available

Monitoring deformations of Istanbul metro line stations through Sentinel-1 and levelling observations

Turkey, as a developing country, is designing and performing massive construction projects around Istanbul. Beginning from the 1960s, rapid urbanization has been taking place due to industrialization, which brings an increase in the population. Yet, construction projects have been accelerated especially during the last decade, and many new projects are scheduled to be completed in a short time. Ground-based observations are generally carried out to monitor the deformations within construction sites, especially through geometric levelling, and GNSS techniques. However, in most cases, these monitoring measurements are only scheduled within the period of the construction process, and ensuing deformations are usually not considered. In addition to these techniques, the space-based interferometric technique can also be used to define the line of sight surface displacements with high accuracy, using the phase difference between image result for synthetic aperture radar images. In particular, Persistent Scatter Interferometry is one of the interferometric methods that are capable of defining the two-dimensional (vertical and horizontal) deformation for the desired epoch with a high temporal resolution. Thus it can be used as a complementary method for monitoring ground deformations, where the measurement is made by ground-based observations. In this study, the deforming areas related to underground metro construction are investigated through significant displacements between 2015 and 2018 of Sentinel-1 space-borne SAR data using the PSI technique. These results are validated by comparison with available levelling data corresponding to the new metro line

Detection of Partially Structural Collapse Using Long‐Term Small Displacement Data from Satellite Images

The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long‐term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR‐based SHM. Conversely, the major challenge of the long‐term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis‐squared distance. The first method presented in this work develops an artificial neural network‐based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher– student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long‐term displacement samples extracted from a few SAR images of TerraSAR‐X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR‐based SHM applications

Electromagnetic sensors for underwater scour monitoring

Scour jeopardises the safety of many civil engineering structures with foundations in riverbeds and it is the leading cause for the collapse of bridges worldwide. Current approaches for bridge scour risk management rely mainly on visual inspections, which provide unreliable estimates of scour and of its effects, also considering the difficulties in visually monitoring the riverbed erosion around submerged foundations. Thus, there is a need to introduce systems capable of continuously monitoring the evolution of scour at bridge foundations, even during extreme flood events. This paper illustrates the development and deployment of a scour monitoring system consisting of smart probes equipped with electromagnetic sensors. This is the first application of this type of sensing probes to a real case-study for continuous scour monitoring. Designed to observe changes in the permittivity of the medium around bridge foundations, the sensors allow for detection of scour depths and the assessment of whether the scour hole has been refilled. The monitoring system was installed on the A76 200 Bridge in New Cumnock (S-W Scotland) and has provided a continuous recording of the scour for nearly two years. The scour data registered after a peak flood event (validated against actual measurements of scour during a bridge inspection) show the potential of the technology in providing continuous scour measures, even during extreme flood events, thus avoiding the deployment of divers for underwater examination

Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation

We present a neural network-based method to detect anomalies in time-dependent surface deformation fields given a set of geodetic images of displacements collected from multiple viewing geometries. The presented methodology is based on a supervised classification approach using combinations of line of sight multitemporal, multi-geometry interferometric synthetic aperture radar (InSAR) time series of displacements. We demonstrate this method with a set of 170 million time series of surface deformation generated for the entire Italian territory and derived from ERS, ENVISAT, and COSMO-SkyMed Synthetic Aperture Radar satellite constellations. We create a training dataset that has been compared with independently validated data and current state-of-the-art classification techniques. Compared to state-of-the-art algorithms, the presented framework provides increased detection accuracy, precision, recall, and reduced processing times for critical infrastructure and landslide monitoring. This study highlights how the proposed approach can accelerate the anomalous points identification step by up to 147 times compared to analytical and other artificial intelligence methods and can be theoretically extended to other geodetic measurements such as GPS, leveling data, or extensometers. Our results indicate that the proposed approach would make the anomaly identification post-processing times negligible when compared to the InSAR time-series processing