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 -