Robust model reconstruction for intelligent health monitoring of tunnel structures

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dc.identifier.uri http://dx.doi.org/10.15488/9907
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/9965
dc.contributor.author Xu, Xiangyang
dc.contributor.author Yang, Hao
dc.date.accessioned 2020-06-29T15:21:49Z
dc.date.available 2020-06-29T15:21:49Z
dc.date.issued 2020
dc.identifier.citation Xu, X.; Yang, H.: Robust model reconstruction for intelligent health monitoring of tunnel structures. In: International Journal of Advanced Robotic Systems 17 (2020), Nr. 2. DOI: https://doi.org/10.1177/1729881420910836
dc.description.abstract Advanced robotic systems will encounter a rapid breakthrough opportunity and become increasingly important, especially with the aid of the accelerated development of artificial intelligence technology. Nowadays, advanced robotic systems are widely used in various fields. However, the development of artificial intelligence-based robot systems for structural health monitoring of tunnels needs to be further investigated, especially for data modeling and intelligent processing for noises. This research focuses on integrated B-spline approximation with a nonparametric rank method and reveals its advantages of high efficiency and noise resistance for the automatic health monitoring of tunnel structures. Furthermore, the root-mean-square error and time consumption of the rank-based and Huber’s M-estimator methods are compared based on various profiles. The results imply that the rank-based method to model point cloud data has a comparative advantage in the monitoring of tunnel, as well as the large-area structures, which requires high degrees of efficiency and robustness.
dc.language.iso eng
dc.publisher Thousand Oaks : SAGE Publications Inc.
dc.relation.ispartofseries International Journal of Advanced Robotic Systems 17 (2020), Nr. 2
dc.relation.uri https://doi.org/10.1177/1729881420910836
dc.rights CC BY 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject AI-based eng
dc.subject B-spline approximation eng
dc.subject health monitoring eng
dc.subject robust modeling eng
dc.subject TLS eng
dc.subject Agricultural robots eng
dc.subject Efficiency eng
dc.subject Intelligent robots eng
dc.subject Interpolation eng
dc.subject Mean square error eng
dc.subject Robotics eng
dc.subject Thallium eng
dc.subject Artificial intelligence technologies eng
dc.subject Automatic health monitoring eng
dc.subject B-spline approximation eng
dc.subject Comparative advantage eng
dc.subject Health monitoring eng
dc.subject Intelligent processing eng
dc.subject Robust modeling eng
dc.subject Root mean square errors eng
dc.subject Structural health monitoring eng
dc.subject.ddc 620 | Ingenieurwissenschaften und Maschinenbau ger
dc.title Robust model reconstruction for intelligent health monitoring of tunnel structures
dc.type article
dc.type Text
dc.relation.issn 1729-8806
dc.bibliographicCitation.issue 2
dc.bibliographicCitation.volume 17
tib.accessRights frei zug�nglich


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