Abstract
This work tackles strain simulation and modelling, as well as channel prediction with strain and temperature measurements as output and input, respectively. We mathematically characterize and quantify the most severe influencer of errors in the light of interpreting strain measurements in relation to structural health monitoring, evaluation and integrity. Subsequently, we automatically pinpoint and trace readings to the sensors and channels they issued forth from, for accurate channel identification, calibration and strain compensation and process automation. Classifiers and simulation models are implemented and validated where applicable.
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References
A-iyeh, E., Peters, F.J.: Gini index-based digital image complementing in the study of medical images. J. Intell. Decis. Technol. 9(2), 209–218 (2015)
A-iyeh, E.:Thermal output and thermal compensation models for apparent strain in a structural health monitoring-based environment. M.Sc. Thesis, University of Manitoba. Advisor: Dean K. McNeill (2012)
McNeill, D.K., A-iyeh, E.: Characterizing variability in strain measurements resulting from temperature changes affecting data acquisition equipment. In: Fu-Kuo, C. (ed.) Proceedings of the Ninth International Workshop on Structural Health Monitoring, pp. 1294–1301. DEStech Publications Inc. (2013)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. In: Annals of Statistics, pp. 1189–1232. JSTOR (2001)
Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: ACM SIGMOID Record, pp. 71–79. ACM (1995)
Vishay Micro-Measurements: Strain gage thermal output and gage factor variation with temperature. In: Strain Gauges and Instruments, Vishay Micro-Measurements (2010)
A-iyeh, E: Comparative feature-ranking performance of machine learning: model classifiers in skin segmentation and classification. In: Science and Information Conference, pp. 592–607. Springer (2019)
Wallach, D., Goffinet, B.: Mean squared error of prediction as a criterion for evaluating and comparing system models. Ecol. Model. J. 44, 299–306 (1989)
Meehan, C.L., Talebi, M.: A method for correcting field strain measurements to account for temperature effects. Geotext. Geomembr. J. 45(4), 250–260 (2017)
Dedication and Acknowledgment
This paper is dedicated to Amy Dario, graduate student advisor of ECE, University of Manitoba for her service, dedication and stewardship. Sincere thanks to the anonymous reviewers for their work. This work was financially supported by Government of the Province of Manitoba, Department of Innovation, Energy and Mines, Natural Sciences and Engineering Research Council (NSERC) of Canada and the Structural Innovation and Monitoring Technologies Resource Centre (SIMTReC). I am also grateful to Prof. Dean K. McNeill for the dataset used in this work.
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A-iyeh, E. (2020). Quantifying Apparent Strain for Automatic Modelling, Simulation, Compensation and Classification in Structural Health Monitoring. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1228. Springer, Cham. https://doi.org/10.1007/978-3-030-52249-0_28
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DOI: https://doi.org/10.1007/978-3-030-52249-0_28
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