Resilience Quantification Based on Monitoring & Prediction Data Using Artificial Intelligence (AI)
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Bibliographic Details
| Author(s): |
Dimitra V. Achillopoulou
(Civil Engineering Department, Democritus University of Thrace University Campus, Xanthi Department of Civil & Environmental Engineering, University of Surrey, Guildford UK)
Nikoleta K. Stamataki (Civil Engineering Department, Democritus University of Thrace University Campus, Xanthi) Anastasios-Panagiotis Psathas (Civil Engineering Department, Democritus University of Thrace University Campus, Xanthi) Lazaros Iliadis (Civil Engineering Department, Democritus University of Thrace University Campus, Xanthi) Athanasios Karabinis (Civil Engineering Department, Democritus University of Thrace University Campus, Xanthi) |
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| Medium: | conference paper | ||||
| Language(s): | English | ||||
| Conference: | IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022 | ||||
| Published in: | IABSE Congress Nanjing 2022 | ||||
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| Page(s): | 1715-1722 | ||||
| Total no. of pages: | 8 | ||||
| DOI: | 10.2749/nanjing.2022.1715 | ||||
| Abstract: |
Lately, there is an increasing demand for resilient infrastructure assets. To support the documentation of resilience, Structural Health Monitoring (SHM) data is a necessity, as well as traffic loads. Those diagnosis and function data can be the basis for the prognosis of future prediction for the performance of the assets. Towards this direction, this paper develops a new methodology that uses real monitoring data and Artificial Intelligence (AI) algorithms to quantify the resilience based on future traffic load predictions of functionality. It includes the case study of the “Hollandse Brug” bridge in the Netherlands considering strains and traffic load predictions and other external. Resilience is derived as a function of both functional and structural parameters throughout the lifecycle. The quantification is supported by sustainability indices and key performance indicators representing the traffic flow, the structural integrity and the sustainability level of the asset. |
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| Keywords: |
bridges structural health monitoring traffic resilience machine learning artificial intelligence
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| Copyright: | © 2022 International Association for Bridge and Structural Engineering (IABSE) | ||||
| License: | This creative work is copyrighted material and may not be used without explicit approval by the author and/or copyright owner. |
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