Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

Damage characterisation using Sentinel-1 images:Case study of bridges in Ukraine

Bridges are vital infrastructure assets, ensuring the economic activity during the adverse times of conflict. Notwithstanding, there is insignificant research regarding their damage characterization with the use of remote approaches for post-conflict recovery. Monitoring and remote sensing is a promising technology for identification of damages caused by war-induced hazards, including artillery fire, explosions and shelling, and hence facilitate accurate and rapid evaluations of capacity and functionality loss, providing valuable information for reliable risk assessments at emergency and normal circumstances. The geospatial analysis, based on Interferometric SAR (InSAR) products of coherence, calculated between SAR images recorded at different dates could serve as a mean to characterize the level of damage, as demonstrated in this research. The main findings of study include the use fully open-access and remote data for assessment of critical infrastructure damages

Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies -- a tiered approach

Critical infrastructure, such as transport networks and bridges, are systematically targeted during wars and suffer damage during extensive natural disasters because it is vital for enabling connectivity and transportation of people and goods, and hence, underpins national and international economic growth. Mass destruction of transport assets, in conjunction with minimal or no accessibility in the wake of natural and anthropogenic disasters, prevents us from delivering rapid recovery and adaptation. As a result, systemic operability is drastically reduced, leading to low levels of resilience. Thus, there is a need for rapid assessment of its condition to allow for informed decision-making for restoration prioritisation. A solution to this challenge is to use technology that enables stand-off observations. Nevertheless, no methods exist for automated characterisation of damage at multiple scales, i.e. regional (e.g., network), asset (e.g., bridges), and structural (e.g., road pavement) scales. We propose a methodology based on an integrated, multi-scale tiered approach to fill this capability gap. In doing so, we demonstrate how automated damage characterisation can be enabled by fit-for-purpose digital technologies. Next, the methodology is applied and validated to a case study in Ukraine that includes 17 bridges, damaged by human targeted interventions. From regional to component scale, we deploy technology to integrate assessments using Sentinel-1 SAR images, crowdsourced information, and high-resolution images for deep learning to facilitate automatic damage detection and characterisation. For the first time, the interferometric coherence difference and semantic segmentation of images were deployed in a tiered multi-scale approach to improve the reliability of damage characterisations at different scales

Development of inventory datasets through remote sensing and direct observation data for earthquake loss estimation

This report summarizes the lessons learnt in extracting exposure information for the three study sites, Thessaloniki, Vienna and Messina that were addressed in SYNER-G. Fine scale information on exposed elements that for SYNER-G include buildings, civil engineering works and population, is one of the variables used to quantify risk. Collecting data and creating exposure inventories is a very time-demanding job and all possible data-gathering techniques should be used to address the data shortcoming problem. This report focuses on combining direct observation and remote sensing data for the development of exposure models for seismic risk assessment. In this report a summary of the methods for collecting, processing and archiving inventory datasets is provided in Chapter 2. Chapter 3 deals with the integration of different data sources for optimum inventory datasets, whilst Chapters 4, 5 and 6 provide some case studies where combinations between direct observation and remote sensing have been used. The cities of Vienna (Austria), Thessaloniki (Greece) and Messina (Italy) have been chosen to test the proposed approaches.JRC.G.5-European laboratory for structural assessmen

Hydrogeomorphological analysis and modelling for a comprehensive understanding of flash-flood damage processes: the 9 October 2018 event in northeastern Mallorca

[EN] A flash-flood event hit the northeastern part of Mallorca on 9 October 2018, causing 13 casualties. Mal- lorca is prone to catastrophic flash floods acting on a sce- nario of deep landscape transformation caused by Mediter- ranean tourist resorts. As global change may exacerbate dev- astating flash floods, analyses of catastrophic events are cru- cial to support effective prevention and mitigation measures. Field-based remote-sensing and modelling techniques were used in this study to evaluate rainfall¿runoff processes at the catchment scale linked to hydrological modelling. Continu- ous streamflow monitoring data revealed a peak discharge of 442 m³ s¿¹ with an unprecedented runoff response. This ex- ceptional behaviour triggered the natural disaster as a com- bination of heavy rainfall (249 mm in 10 h), karstic features and land cover disturbances in the Begura de Salma River catchment (23 km²). Topography-based connectivity indices and geomorphic change detection were used as rapid post- catastrophe decision-making tools, playing a key role dur- ing the rescue search. These hydrogeomorphological preci- sion techniques were combined with the Copernicus Emer- gency Management Service and ¿ground-based¿ damage as- sessment, which showed very accurately the damage-driving factors in the village of Sant Llorenç des Cardassar. The main challenges in the future are to readapt hydrological modelling to global change scenarios, implement an early flash-flood warning system and take adaptive and resilient measures on the catchment scale.This research was supported by the Spanish Ministry of Science, Innovation and Universities, the Spanish Agency of Research (AEI) and the European Regional Development Fund (ERDF) through the project CGL2017-88200-R "Functional hydrological and sediment connectivity at Mediterranean catchments: global change scenarios -MEDhyCON2".Estrany, J.; Ruiz-Perez, M.; Mutzner, R.; Fortesa, J.; Nacher Rodriguez, B.; Tomas-Burguera, M.; Garcia-Comendador, J.... (2020). Hydrogeomorphological analysis and modelling for a comprehensive understanding of flash-flood damage processes: the 9 October 2018 event in northeastern Mallorca. 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D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Development of an Operational Satellite-Based Flood Monitoring Model for Tanzania

Timely information during water related disasters is of utmost importance for flood preparedness and risk reduction. Real time observation and monitoring of flooded areas is an expensive and time-consuming exercise. Satellite remote sensing is a quick and affordable approach that can be used for concurrent floods detection at different scales. This is important as it facilitates timely information for emergency response to disaster management departments, even in scarcely instrumented catchments. This study presents a novel approach for flood tracking using satellite technology to map flood affected areas. An open-source water detection algorithm is developed that employs readily available satellite images and the Google Earth Engine (GEE) platform. Dar es Salaam and Singida regions in Tanzania were used as the case study for validation of the proposed approach. Use is made of Sentinel-1 satellite images and GEE coding. The after-flood tracking GEE code was validated with the physical flood extent markers and after-event flood extent survey points of the regions provided by the Ministry of Water (MoW). The findings reveal that the approach supports mapping flood extent areas by giving promising results after the satisfaction from validated data. Relevant parameters were then coded in order to develop the flood map of Tanzania. The findings of this study demonstrate the usefulness of open-source GEE in rapid flood inundation mapping.&nbsp