Details
| Original language | English |
|---|---|
| Article number | 6439 |
| Pages (from-to) | 1-26 |
| Number of pages | 26 |
| Journal | Sensors (Switzerland) |
| Volume | 20 |
| Issue number | 22 |
| Publication status | Published - 11 Nov 2020 |
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.
Keywords
- Calibration, Deep learning, Finite element analysis, Long short-term memory, Sequence, Terrestrial laser scanning
ASJC Scopus subject areas
- Chemistry(all)
- Analytical Chemistry
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Physics and Astronomy(all)
- Instrumentation
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Sensors (Switzerland), Vol. 20, No. 22, 6439, 11.11.2020, p. 1-26.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference
AU - Xu, Wei
AU - Bao, Xiangyu
AU - Chen, Genglin
AU - Neumann, Ingo
N1 - Funding information: The publication of this article was funded by the Open Access Fund of Leibniz Universität Hannover. The publication of this article was funded by the Open Access Fund of Leibniz Universit?t Hannover. The author Wei Xu would like to thank the China Scholarship Council (CSC) for financially supporting his PhD study at Leibniz Universit?t Hannover, Germany. Acknowledgments: The author Wei Xu would like to thank the China Scholarship Council (CSC) for financially supporting his PhD study at Leibniz Universität Hannover, Germany.
PY - 2020/11/11
Y1 - 2020/11/11
N2 - 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.
AB - 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.
KW - Calibration
KW - Deep learning
KW - Finite element analysis
KW - Long short-term memory
KW - Sequence
KW - Terrestrial laser scanning
UR - http://www.scopus.com/inward/record.url?scp=85095931614&partnerID=8YFLogxK
U2 - 10.3390/s20226439
DO - 10.3390/s20226439
M3 - Article
C2 - 33187250
AN - SCOPUS:85095931614
VL - 20
SP - 1
EP - 26
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
SN - 1424-8220
IS - 22
M1 - 6439
ER -