Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jbiomech.2008.12.001
Title: Rapid identification of elastic modulus of the interface tissue on dental implants surfaces using reduced-basis method and a neural network
Authors: Zaw, K. 
Liu, G.R. 
Deng, B. 
Tan, K.B.C. 
Keywords: Elastic modulus
Fast computation
Material characterization
Neural network
Reduced-basis method
Issue Date: 26-Mar-2009
Source: Zaw, K., Liu, G.R., Deng, B., Tan, K.B.C. (2009-03-26). Rapid identification of elastic modulus of the interface tissue on dental implants surfaces using reduced-basis method and a neural network. Journal of Biomechanics 42 (5) : 634-641. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jbiomech.2008.12.001
Abstract: This paper proposes a rapid inverse analysis approach based on the reduced-basis method (RBM) and neural network (NN) to identify the "unknown" elastic modulus (Young's modulus) of the interfacial tissue between a dental implant and the surrounding bones. In the present RBM-NN approach, a RBM model is first built to compute displacement responses of dental implant-bone structures subjected to a harmonic loading for a set of "assumed" Young's moduli. The RBM model is then used to train a NN model that is used for actual inverse analysis in real-time. Actual experimental measurements of displacement responses are fed into the trained NN model to inversely determine the "true" elastic modulus of the interfacial tissue. As an example, a physical model of dental implant-bone structure is built and inverse analysis is conducted to verify the present RBM-NN approach. Based on numerical simulation and actual experiments, it is confirmed that the identified results are very accurate, reliable, and the computational saving is very significant. The present RBM-NN approach is found robust and efficient for inverse material characterizations in noninvasive and/or nondestructive evaluations. © 2008 Elsevier Ltd. All rights reserved.
Source Title: Journal of Biomechanics
URI: http://scholarbank.nus.edu.sg/handle/10635/51510
ISSN: 00219290
DOI: 10.1016/j.jbiomech.2008.12.001
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