Satellite remote sensing and non-destructive testing methods for transport infrastructure monitoring: advances, challenges and perspectives

High temporal frequency monitoring of transport infrastructure is crucial to prioritise mainte-nance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are re-quired for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Mul-ti-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from i) satellite remote sensing and ii) ground-based NDT methods is still a subject to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications in these two areas of endeavour for transport infrastructure monitoring. Recent advances, main challenges and future perspectives arising from their mutual integration are also discussed

Development of sensors and non-destructive techniques to determine the performance of coatings in construction

The primary objective of this work was to examine and develop techniques for monitoring the degradation of Organically Coated Steel (OCS) in-situ. This included the detection of changes associated with the weathering to both the organic coating and metallic substrate. Initially, a review of current promising techniques was carried out however many were found to be unsuitable for this application and the adaptation of current techniques and the development of new techniques was considered. A brief concept investigation, based on initial testing and considerations, was used to determine a number of sensing techniques to examine. These included embedded, Resonant Frequency Identification (RFID), Magnetic Flux Leakage (MFL) and dielectric sensing. Each of these techniques were assessed for the application, prototyped, and tested against a range of samples to determine the accuracy and sensitivity of degradation detection provided. A range of poorly and highly durable coated samples were used in conjunction with accelerated weathering testing for this aim. Track based electronic printed sensors were presented as both a cut edge corrosion tracking and coating capacitance measurement method. While suffering somewhat from electrical paint compatibility issues both concepts showed merit in initial trials however the capacitive sensor ultimately proved insufficiently responsive to coating changes. The embedded, progressive failure-based, cut edge corrosion sensor was produced and tested in modern coating systems with moderate success. Novel applications of RFID and MLF techniques were considered and proved capable of detecting large changes in substrate condition due to significant corrosion. However, there was a lack of sufficient sensitivity when considering early-stage corrosion of durable modern OCS products. Finally, it was shown that a chipless antenna could be designed and optimised for novelly monitoring the changes to the dielectric properties of a paint layer due to degradation. However, ultimately this test, due to equipment requirements, lent itself more to lab testing than in-situ. Due to some of these limitations a different approach was considered in which the environmental factors influencing degradation were examined with the aim of relating these to performance across a building. It was observed that a combination of high humidity and the build-up of aggressive natural deposits contributed to high degradation rates in sheltered regions, such as building eaves, where microclimates were created. The build-up of deposits and their effect was presented as a key degradation accelerant during in-use service. A unique numerical simulation approach was developed to predict the natural washing, via rain impact and characteristics of the building analysed. This approach showed promise for determining areas unlikely to be naturally washed, and therefore subjected to a degradation accelerating, build-up of deposits. Given these understandings coated wetness sensors were considered as a realistic live-monitoring device capable of determining deposit build up and ultimately OCS lifetime

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis

Systematic network monitoring can be the cornerstone for the dependable operation of safety-critical distributed systems. In this paper, we present our vision for informed anomaly detection through network monitoring and resilience measurements to increase the operators' visibility of ATM communication networks. We raise the question of how to determine the optimal level of automation in this safety-critical context, and we present a novel passive network monitoring system that can reveal network utilisation trends and traffic patterns in diverse timescales. Using network measurements, we derive resilience metrics and visualisations to enhance the operators' knowledge of the network and traffic behaviour, and allow for network planning and provisioning based on informed what-if analysis

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Service Level Agreement-based GDPR Compliance and Security assurance in (multi)Cloud-based systems

Compliance with the new European General Data Protection Regulation (Regulation (EU) 2016/679) and security assurance are currently two major challenges of Cloud-based systems. GDPR compliance implies both privacy and security mechanisms definition, enforcement and control, including evidence collection. This paper presents a novel DevOps framework aimed at supporting Cloud consumers in designing, deploying and operating (multi)Cloud systems that include the necessary privacy and security controls for ensuring transparency to end-users, third parties in service provision (if any) and law enforcement authorities. The framework relies on the risk-driven specification at design time of privacy and security level objectives in the system Service Level Agreement (SLA) and in their continuous monitoring and enforcement at runtime.The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644429 and No 780351, MUSA project and ENACT project, respectively. We would also like to acknowledge all the members of the MUSA Consortium and ENACT Consortium for their valuable help