Optical Fibre Sensing and Deep Learning-Based Disaster and Climate Change Risk Assessments of Civil Infrastructure: Current Status and Future Perspective
Paper
Anjana, R. W. K., Herath, H. M. C., Jayawickrema, U. M. N. and Epaarachchi, J. A.. 2023. "Optical Fibre Sensing and Deep Learning-Based Disaster and Climate Change Risk Assessments of Civil Infrastructure: Current Status and Future Perspective." 13th International Conference on Sustainable Built Environment. Kandy, Sri Lanka 16 - 18 Dec 2022 Singapore . Springer. https://doi.org/10.1007/978-981-99-3471-3_33
| Paper/Presentation Title | Optical Fibre Sensing and Deep Learning-Based Disaster and Climate Change Risk Assessments of Civil Infrastructure: Current Status and Future Perspective |
|---|---|
| Presentation Type | Paper |
| Authors | Anjana, R. W. K., Herath, H. M. C., Jayawickrema, U. M. N. and Epaarachchi, J. A. |
| Journal or Proceedings Title | Lecture Notes in Civil Engineering |
| Proceedings of the 13th International Conference on Sustainable Built Environment | |
| Journal Citation | 362, pp. 463-476 |
| Number of Pages | 14 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | Singapore |
| ISBN | 9789819934713 |
| 9789819934737 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-99-3471-3_33 |
| Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-99-3471-3_33 |
| Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-99-3471-3 |
| Conference/Event | 13th International Conference on Sustainable Built Environment |
| Event Details | 13th International Conference on Sustainable Built Environment Parent International Conference on Sustainable Built Environment Delivery In person Event Date 16 to end of 18 Dec 2022 Event Location Kandy, Sri Lanka |
| Abstract | Civil infrastructures are affected by climate change including sea level rise, changes in snowfall, ice, and permafrost, and effects on hydrological systems, including precipitation floods and droughts, tropical cyclones, storms, and sea-wave heights. Also, disasters such as earthquakes, landslides, and tsunamis worldwide disrupt people’s lives and cause significant property damage. This study investigates the recent advancements toward damage mitigation of civil infrastructures through early detection of natural disasters and climatic changes using the measurements of optical fibre sensing (OFS) followed by deep learning (DL) models. It was shown that pointed and distributed OFS was effective for measuring strain, temperature, and pressure to trace the changes in seismological, hydrological, and geological data at surface level, subsurface level, and submerged. OFS is a rapidly growing research area for improving inspection accuracy and performance due to its advantageous properties of being lightweight, reliable, small in size, immunity to external electromagnetic perturbations, low power, high sensitivity, multiplexing capability, and wide bandwidth. Distributed optical fibre sensing (DOFS) has gained immense interest in engineering as they offer unique advantages for spatially distributed measurements for hundreds of kilometres. DOFS could detect the damage's precise location, magnitude, and propagation over time. Climate changes and disasters triggered propagating damage to structures including buildings, bridges, highways, and dams can be identified in terms of cracks, fatigue, creep, and slip. Big data-driven methods for disaster management are developed with DL, most frequently through convolutional neural networks. DL-based disaster-predicting systems that can be identified with seismological data have been developed with precision and a recall of over 90%. This review reveals that disasters due to earth movements and pore water pressure can be precisely detected through OFS followed by DL models. There is ample room for further development of combinational studies between OFS and DL for disaster and climate change risk assessment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. |
| Keywords | Climate resilient infrastructure; Disaster prediction; Fibre optic sensing; Deep learning; Structural health monitoring |
| ANZSRC Field of Research 2020 | 401602. Composite and hybrid materials |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Series | Lecture Notes in Civil Engineering |
| Byline Affiliations | Uva Wellassa University of Sri Lanka, Sri Lanka |
| Centre for Future Materials | |
| School of Engineering |
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https://research.usq.edu.au/item/z2759/optical-fibre-sensing-and-deep-learning-based-disaster-and-climate-change-risk-assessments-of-civil-infrastructure-current-status-and-future-perspective
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