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Increasing the Resilience of European Transport Infrastructure

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Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 156))

Abstract

Extreme weather conditions, climate change, damages to the infrastructure (caused by natural and man-made hazards) and traffic impediments negatively impact the reliability of mobility solutions. Risk analysis, adaptation measures and strategies that enable minimizing the impact of both natural and man-made extreme events on seamless transport operation, protect the users of the transport network in case of extreme conditions, as well as provide optimal information to operators and users of the transport infrastructure, need to be developed. Road transport is vulnerable to extreme weather events, while bridges and tunnels are among the most critical land transport structures. A large number of bridges and tunnels have been in operation for more than 50 years and there are widespread signs of deterioration. They need inspection, vulnerability assessment and, when needed, appropriate interventions. Inspection, though, in inaccessible areas, or structures with high volumes of traffic, is expensive, time-consuming, and potentially dangerous. At the same time, structural/vulnerability assessment is also a lengthy process which is especially painful after extreme events. The overall goal of RESIST (RESilient transport InfraSTructure to extreme events) a RIA H2020 project funded by the EU commission with grant number 769,066 is to increase the resilience of seamless transport operation to natural and man-made extreme events, protect the users of the European transport infrastructure and provide optimal information to the operators and users of the transport infrastructure. In the context of RESIST, robotics for visual and contact inspection of structures, structural vulnerability assessment, infrastructure risk management as well as mobility continuity applications considering stress levels of the drivers are being developed towards a high level of resilience of the transport infrastructure.

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References

  1. Pinto R, Medeiros A, Padaratz I, Andrade B (2010) Use of ultrasound to estimate depth of surface opening cracks in concrete structures. http://www.ndt.net/?id=9954

  2. Marini D et al (2019) Acoustic micro-opto-mechanical transducers for crack width measurement on concrete structures from aerial robots. In: 2019 TRANSDUCERS Germany, pp 893–896

    Google Scholar 

  3. Menze M, Geiger A (2015) Object scene flow for autonomous vehicles. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3061–3070

    Google Scholar 

  4. Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1–3):7–42

    Article  Google Scholar 

  5. https://colmap.github.io/

  6. https://www.pix4d.com/

  7. Yang G, Manela J, Happold M, Ramanan D (2019) Hierarchical deep stereo matching on high-resolution images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5515–5524

    Google Scholar 

  8. Yao Y, Luo Z, Li S, Fang T, Quan L (2018) Mvsnet: depth inference for unstructured multi-view stereo. In: Proceedings of the European conference on computer vision (ECCV), pp 767–783

    Google Scholar 

  9. Martin M, Sagnik M, Sreenivas M, Panagiotis P, Ramesh V (2019) Meta-learning convolutional neural architectures for multi-target concrete defect classification with the concrete defect bridge image dataset. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  10. Martin M, Iuliia P, Sagnik M, Ramesh V (2019) Open set recognition through deep neural network uncertainty: does out-of-distribution detection require generative classifiers? In: Proceedings of the IEEE computer society international conference on computer vision (ICCV), 1st workshop on statistical deep learning for computer vision (SDL-CV)

    Google Scholar 

  11. Gal Y, Ghahramani Z (2015) Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Int Conf Mach Learn (ICML) 48

    Google Scholar 

  12. Olaf R, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. MICCAI 9351:234–241

    Google Scholar 

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Correspondence to Kostas Bouklas .

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Bouklas, K. et al. (2021). Increasing the Resilience of European Transport Infrastructure. In: Rainieri, C., Fabbrocino, G., Caterino, N., Ceroni, F., Notarangelo, M.A. (eds) Civil Structural Health Monitoring. CSHM 2021. Lecture Notes in Civil Engineering, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-030-74258-4_48

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  • DOI: https://doi.org/10.1007/978-3-030-74258-4_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-74257-7

  • Online ISBN: 978-3-030-74258-4

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