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Decision Support Systems for Civil Infrastructure Management Based on Satellite Technology

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 9496

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Dr. Daniel Tonelli

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Guest Editor

Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano, 77, 38123 Trento, Italy
Interests: infrastructure management; structural health monitoring; remote sensing; monitoring system design; bayesian data analysis; decision-making

Dr. Cristian Rossi

E-Mail Website
Guest Editor

1. Satellite Applications Catapult, Electron Building, Fermi Avenue, Harwell Campus, Didcot, Oxfordshire OX11 0QR, UK
2. School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
Interests: earth observation; optical and radar remote sensing; InSAR data processing; spatial finance; climate change adaptation; early warning
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Dear Colleagues,

Several bridges and other civil infrastructures worldwide have been in operation for over half a century and exhibit widespread signs of deterioration. Monitoring and maintenance are needed to sustain their safe operation, and satellite technology can revolutionize the current contact-type structural health monitoring (SHM) practice. Indeed, hundreds of satellites are already monitoring our planet every day, and remote sensing techniques, such as InSAR, can provide measurements of ground motions and infrastructure displacements at reduced costs and with sub-millimetric accuracy, without any sensors installed on site.

The remarkable advances in this research field during the last decade make it timely to announce a Special Issue devoted to civil infrastructure monitoring and management based on satellite technology and decision support systems.

This Special Issue encourages high-quality contributions addressing the current state of the art, recent advances, applications, case studies, and future trends in infrastructure management based on satellite technology. Topics of interest include, but are not limited to: methods, frameworks, tools, and platforms for satellite data processing, visualization, and data fusion with monitoring data from terrestrial technologies; interpretation of infrastructure response and compensation of environmental effects; risk assessment, early warning, and decision support for cost-effective maintenance prioritization based on satellite data; relevant applications to real-life case studies.

Dr. Daniel Tonelli
Dr. Cristian Rossi
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

structural health monitoring
remote sensing
satellite technology
decision support system
early warning
risk assessment
data fusion
civil infrastructure
InSAR (interferometric synthetic aperture radar)
GNSS (global navigation satellite system)
finite element models
building information models

Published Papers (5 papers)

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Research

22 pages, 16324 KiB

Open AccessArticle

Satellite Synthetic Aperture Radar, Multispectral, and Infrared Imagery for Assessing Bridge Deformation and Structural Health—A Case Study at the Samuel de Champlain Bridge

by Daniel Cusson and Helen Stewart

Remote Sens. 2024, 16(4), 614; https://doi.org/10.3390/rs16040614 - 07 Feb 2024

Viewed by 996

Abstract

A space-borne remote sensing method was applied, validated, and demonstrated in a case study on the Samuel de Champlain Bridge in Montreal, Canada. High-resolution C-band radar satellite imagery was analyzed using the Persistent Scatterer Interferometric Synthetic Aperture Radar technique to derive bridge displacements and compare them against theoretical estimates. Multispectral and long-wave thermal infrared satellite imagery acquired during the InSAR observation period and historical environmental data were analyzed to provide context for the interpretation and understanding of InSAR results. Thermal deformation measurements compared well with their theoretical estimates based on known bridge geometry and ambient temperature data. Non-thermal deformation measurements gave no evidence of settlement during the 2-year monitoring period, as would normally be expected for a newly constructed bridge with its foundation on bedrock. The availability of environmental data obtained from multispectral and thermal infrared satellite imagery was found to be useful in providing context for the bridge stability assessment. Ambient temperature measurements from thermal infrared satellite imagery were found to be a suitable alternative in cases where data from in situ temperature sensors or nearby weather stations are not available or not fit for purpose. No strong correlation was found between the river conditions and bridge deformation results from the InSAR analysis; this is partly due to the fact that most of these effects act along the river flow in the north–south direction, to which the satellite sensor is not sensitive. Full article

(This article belongs to the Special Issue Decision Support Systems for Civil Infrastructure Management Based on Satellite Technology)

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24 pages, 11251 KiB

Open AccessArticle

Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology

by Daniel Tonelli, Valeria F. Caspani, Andrea Valentini, Alfredo Rocca, Riccardo Torboli, Alfonso Vitti, Daniele Perissin and Daniele Zonta

Remote Sens. 2023, 15(21), 5242; https://doi.org/10.3390/rs15215242 - 04 Nov 2023

Cited by 1 | Viewed by 1947

Abstract

This paper presents a study on applying satellite Interferometric Synthetic Aperture Radar (InSAR) technology for the remote monitoring of road bridges and interpreting the results from a structural standpoint. The motivation behind this study arises from the widespread deterioration observed in many road bridges worldwide, leading to the need for large-scale, economic, and effective structural health monitoring (SHM) techniques. While traditional contact-type sensors have cost sustainability limitations, remote sensing techniques, including satellite-based InSAR, offer interesting alternative solutions. The objective of this study is three-fold: (i) to process InSAR data specifically for road bridges in operational conditions through the Multi-Temporal InSAR technique and extract displacement time series of reflective targets on their decks; (ii) to interpret the observed millimetric bridge displacements to verify the consistency with expected response to environmental loads and the possibility to detect unexpected behaviours; and (iii) to investigate the correlation between bridge displacements and environmental loads as temperature and river water flow variations. The study focuses on the multi-span prestressed concrete A22 Po River Bridge in Italy, utilising a dataset of X-Band HIMAGE mode Stripmap images acquired over eight years by the satellite constellation COSMO-SkyMed. The study demonstrates the effectiveness of InSAR-based SHM in detecting temperature-induced displacements and identifying different bridge spans simply by studying the sign of the correlation between displacements and temperature variation. It also reveals an unexpected behaviour in a portion of the bridge retrofitted to prevent scour issues a few years before the dataset start date. Furthermore, the correlation between pier displacements and river level variations underscores the importance of considering environmental factors and the geotechnical characteristics of the foundation soils in bridge monitoring. The results obtained from this study are significant with a view to using this satellite InSAR-based monitoring for early detection of anomalous bridge behaviour on a large scale. Full article

(This article belongs to the Special Issue Decision Support Systems for Civil Infrastructure Management Based on Satellite Technology)

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18 pages, 12797 KiB

Open AccessArticle

Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure

by Maral Bayaraa, Cristian Rossi, Freddie Kalaitzis and Brian Sheil

Remote Sens. 2023, 15(20), 4910; https://doi.org/10.3390/rs15204910 - 11 Oct 2023

Viewed by 2166

Abstract

There is a critical need for a global monitoring capability for Tailings Storage Facilities (TSFs), to help protect the surrounding communities and the environment. Satellite Synthetic Aperture Radar Interferometry (InSAR) shows much promise towards this ambition. However, extracting meaningful information and interpreting the deformation patterns from InSAR data can be a challenging task. One approach to address this challenge is through the use of data science techniques. In this study, the representation of InSAR metadata as Entity Embeddings within a Deep Learning framework (EE-DL) is investigated for modelling the spatio-temporal deformation response. Entity embeddings are commonly used in natural-language-processing tasks. They represent discrete objects, such as words, as continuous, low-dimensional vectors that can be manipulated mathematically. We demonstrate that EE-DL can be used to predict anomalous patterns in the InSAR time series. To evaluate the performance of the EE-DL approach in SAR interferometry, we conducted experiments over a mining test site (Cadia, Australia), which has been subject to a TSF failure. This study demonstrated that EE-DL can detect and predict the fine spatial movement patterns that eventually resulted in the failure. We also compared the results with deformation predictions from common baseline models, the Random Forest model and Gaussian Process Regression (GPR). Both EE-DL and GPR greatly outperform Random Forest. While GPR is also able to predict displacement patterns with millimetric accuracy, it detects a significantly lower number of anomalies compared to EE-DL. Overall, our study showed that EE-DL is a promising approach for building early-warning systems for critical infrastructures that use InSAR to predict ground deformations. Full article

(This article belongs to the Special Issue Decision Support Systems for Civil Infrastructure Management Based on Satellite Technology)

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18 pages, 8072 KiB

Open AccessCommunication

Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)

by Gianluigi Della Ragione, Alfredo Rocca, Daniele Perissin and Emilio Bilotta

Remote Sens. 2023, 15(10), 2555; https://doi.org/10.3390/rs15102555 - 13 May 2023

Cited by 1 | Viewed by 1492

Abstract

The construction of tunnels in urban areas can affect the nearby existing infrastructures and buildings, as shallow excavations induce movements up to the ground surface. An important parameter to be monitored during the excavation is the volume loss, which plays a crucial role in determining the ground movements at the surface. InSAR satellite monitoring has the potential to detect ground movements at the millimetric scale on a vast area for tunneling applications. In the present study, the Multi-Temporal InSAR (MT-InSAR) technique, based on the persistent scattering method, is used to retrieve vertical displacements induced by the excavation of twin tunnels of a metro line in the City of Naples (Italy). Here, the volume loss is obtained by fitting a Gaussian curve on the monitored settlement data induced by the excavation of the first tunnel. The latter is then used to predict the settlement of the second excavation about one year later and compared to the MT-InSAR data. These monitored data show the typical shape of the settlement profile, confirming the empirical Gaussian distribution and MT-InSAR capability to detect millimetric displacements. Therefore, MT-InSAR can be used to feed algorithms to improve the prediction of tunneling-induced displacements. Full article

(This article belongs to the Special Issue Decision Support Systems for Civil Infrastructure Management Based on Satellite Technology)

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19 pages, 7660 KiB

Open AccessArticle

Outlier Detection Based on Nelder-Mead Simplex Robust Kalman Filtering for Trustworthy Bridge Structural Health Monitoring

by Liangliang Hu, Yan Bao, Zhe Sun, Xiaolin Meng, Chao Tang and Dongliang Zhang

Remote Sens. 2023, 15(9), 2385; https://doi.org/10.3390/rs15092385 - 02 May 2023

Cited by 4 | Viewed by 1551

Abstract

Structural health monitoring (SHM) is vital for ensuring the service safety of aging bridges. As one of the most advanced sensing techniques, Global Navigation Satellite Systems (GNSS) could capture massive spatiotemporal information for effective bridge structural health monitoring (BSHM). Unfortunately, GNSS measurements often contain outliers due to various factors (e.g., severe weather conditions, multipath effects, etc.). All such outliers could jeopardize the accuracy and reliability of BSHM significantly. Previous studies have examined the feasibility of integrating the conventional multi-rate Kalman filter (MKF) with an adaptive algorithm in the data processing processes to ensure BSHM accuracy. However, frequent parameter adjustments are still needed in tedious data processing processes. This study proposed an outlier detection method using a Nelder-Mead simplex robust multi-rate Kalman filter (RMKF) for supporting trustworthy BSHM using GNSS and accelerometer. In the end, the authors have validated the proposed method using the monitoring data collected at the Wilford Bridge in the UK. Results showed that the accuracy of the total dynamic vibration displacement time series has been improved by 21% compared with the results using the conventional MKF approach. The authors envision that the proposed method will shed light on reliable and explainable data processing policy and trustworthy BSHM. Full article

(This article belongs to the Special Issue Decision Support Systems for Civil Infrastructure Management Based on Satellite Technology)

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