Sun, Li;
Shawe-Taylor, John;
D'Ayala, Dina;
(2022)
Artificial intelligence-informed planning for the rapid response of hazard-impacted road networks.
Scientific Reports
, 12
(1)
, Article 16286. 10.1038/s41598-022-19637-z.
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Abstract
Post-hazard rapid response has emerged as a promising pathway towards resilient critical infrastructure systems (CISs). Nevertheless, it is challenging to scheme the optimal plan for those rapid responses, given the enormous search space and the hardship of assessment on the spatiotemporal status of CISs. We now present a new approach to post-shock rapid responses of road networks (RNs), based upon lookahead searches supported by machine learning. Following this approach, we examined the resilience-oriented rapid response of a real-world RN across Luchon, France, under destructive earthquake scenarios. Our results show that the introduction of one-step lookahead searches can effectively offset the lack of adaptivity due to the deficient heuristic of rapid responses. Furthermore, the performance of rapid responses following such a strategy is far surpassed, when a series of deep neural networks trained based solely on machine learning, without human interventions, are employed to replace the heuristic and guide the searches.
| Type: | Article |
|---|---|
| Title: | Artificial intelligence-informed planning for the rapid response of hazard-impacted road networks |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41598-022-19637-z |
| Publisher version: | https://doi.org/10.1038/s41598-022-19637-z |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Artificial Intelligence, Earthquakes, Heuristics, Humans, Machine Learning, Neural Networks, Computer |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10157166 |
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