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.
Robust model reconstruction for intelligent health monitoring of tunnel structures
2020-01-01
International Journal of Advanced Robotic Systems 17 (2020), Nr. 2
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
AI-based , B-spline approximation , health monitoring , robust modeling , TLS , Agricultural robots , Efficiency , Intelligent robots , Interpolation , Mean square error , Robotics , Thallium , Artificial intelligence technologies , Automatic health monitoring , Comparative advantage , Intelligent processing , Root mean square errors , Structural health monitoring , ddc:620
Predictive Fault Diagnosis System for Intelligent and Robust Health Monitoring
British Library Conference Proceedings | 2010
|A robust structural health monitoring technique for airframe structures
Tema Archiv | 2009
|Subway tunnel intelligent monitoring method and system
Europäisches Patentamt | 2023
|