Monitoring deformations of infrastructure networks: A fully automated GIS integration and analysis of InSAR time-series

Ageing stock and extreme weather events pose a threat to the safety of infrastructure networks. In most countries, funding allocated to infrastructure management is insufficient to perform systematic inspections over large transport networks. As a result, early signs of distress can develop unnoticed, potentially leading to catastrophic structural failures. Over the past 20 years, a wealth of literature has demonstrated the capability of satellite-based Synthetic Aperture Radar Interferometry (InSAR) to accurately detect surface deformations of different types of assets. Thanks to the high accuracy and spatial density of measurements, and a short revisit time, space-borne remote-sensing techniques have the potential to provide a cost-effective and near real-time monitoring tool. Whilst InSAR techniques offer an effective approach for structural health monitoring, they also provide a large amount of data. For civil engineering procedures, these need to be analysed in combination with large infrastructure inventories. Over a regional scale, the manual extraction of InSAR-derived displacements from individual assets is extremely time-consuming and an automated integration of the two datasets is essential to effectively assess infrastructure systems. This paper presents a new methodology based on the fully automated integration of InSAR-based measurements and Geographic Information System-infrastructure inventories to detect potential warnings over extensive transport networks. A Sentinel dataset from 2016 to 2019 is used to analyse the Los Angeles highway and freeway network, while the Italian motorway network is evaluated by using open access ERS/Envisat datasets between 1992 and 2010, COSMO-SkyMed datasets between 2008 and 2014 and Sentinel datasets between 2014 and 2020. To demonstrate the flexibility of the proposed methodology to different SAR sensors and infrastructure classes, the analysis of bridges and viaducts in the two test areas is also performed. The outcomes highlight the potential of the proposed methodology to be integrated into structural health monitoring systems and improve current procedures for transport network management.

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Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

Sensors ◽

10.3390/s20236894 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6894

Author(s):

Nicola-Ann Stevens ◽

Myra Lydon ◽

Adele H. Marshall ◽

Su Taylor

Keyword(s):

Survival Analysis ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Performance Indicators ◽

Key Performance Indicators ◽

Predictive Maintenance ◽

Sensor Data ◽

Transport Networks ◽

Data Set ◽

Structural Health

Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.

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Reference-free damage localization in time-space domain for structural health monitoring of X-COR sandwich composites

Journal of Intelligent Material Systems and Structures ◽

10.1177/1045389x18810803 ◽

2018 ◽

Vol 30 (3) ◽

pp. 371-385 ◽

Cited By ~ 4

Author(s):

Guoyi Li ◽

Aditi Chattopadhyay

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Damage Localization ◽

Sandwich Composites ◽

Space Domain ◽

Space Analysis ◽

Structural Health ◽

Time Space ◽

Wide Range ◽

Excitation Frequencies

This article presents a guided wave based damage localization framework using a time-space analysis for structural health monitoring of X-COR sandwich composites with a reference-free perspective to overcome the difficulty in detecting reflected guided waves in a highly attenuated media. Transducers, including macro-fiber composites and piezoelectric wafers, are used to design the sensing paths. The time-space domain is constructed using de-noised signals that are processed by signal processing techniques including matching pursuit decomposition and Hilbert transform. The localization framework is then validated across a wide range of excitation frequencies in X-COR sandwich composites with seeded facesheet delamination. The results indicate that time-space analysis offers a high accuracy for detection and localization of internal damages and serves as a promising framework for structural health monitoring of complex sandwich composites with reinforcements. This work also provides a comprehensive study of the changes in group velocities, attenuation tendencies, and time-space resolution of actuated and converted modes under different excitation frequencies across a range of ultrasonic transducer sizes, thereby helping to improve reliability and accuracy of damage localization in time-space domain.

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