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

authored by

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.

Organisation(s)

Geodetic Institute

External Organisation(s)

China University of Mining And Technology

Type

Article

Journal

Sensors (Switzerland)

Volume

20

Pages

1-26

No. of pages

26

ISSN

1424-8220

Publication date

11.11.2020

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Analytical Chemistry, Biochemistry, Atomic and Molecular Physics, and Optics, Instrumentation, Electrical and Electronic Engineering

Electronic version(s)

https://doi.org/10.3390/s20226439 (Access: Open)

 

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