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Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

verfasst von
Wei Xu, Xiangyu Bao, Genglin Chen, Ingo Neumann
Abstract

The demand for efficient and accurate finite element analysis (FEA) is becoming more prevalent with the increase in advanced calibration technologies and sensor-based monitoring methods. The current research explores a deep learning-based methodology to calibrate FEA results. The utilization of monitoring reference results from measurements, e.g., terrestrial laser scanning, can help to capture the actual features in the static loading process. We learn the deviation sequence results between the standard FEA computations with the simplified geometry and refined reference values by the long short-term memory method. The complex changing principles in different deviations are trained and captured effectively in the training process of deep learning. Hence, we generate the FEA sequence results corresponding to next adjacent loading steps. The final FEA computations are calibrated by the threshold control. The calibration reduces the mean square errors of the FEA future sequence results significantly. This strengthens the calibration depth. Consequently, the calibration of FEA computations with deep learning can play a helpful role in the prediction and monitoring problems regarding the future structural behaviors.

Organisationseinheit(en)
Geodätisches Institut
Externe Organisation(en)
China University of Mining And Technology
Typ
Artikel
Journal
Sensors (Switzerland)
Band
20
Seiten
1-26
Anzahl der Seiten
26
ISSN
1424-8220
Publikationsdatum
11.11.2020
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Analytische Chemie, Biochemie, Atom- und Molekularphysik sowie Optik, Instrumentierung, Elektrotechnik und Elektronik
Elektronische Version(en)
https://doi.org/10.3390/s20226439 (Zugang: Offen)
 

Details im Forschungsportal „Research@Leibniz University“