Biomechanical Study Using Fuzzy Systems to Quantify Collagen Fiber Recruitment and Predict Creep of the Rabbit Medial Collateral Ligament

[+] Author and Article Information
A. F. Ali, N. G. Shrive, C. B. Frank

McCaig Centre for Joint Injury and Arthritis Research,  University of Calgary, Canada

M. M. Reda Taha1

Department of Civil Engineering,  University of New Mexico, Albuquerque, NM, USA

G. M. Thornton

Division of Orthopaedic Engineering Research,  University of British Columbia and MacInnis Engineering Associates Ltd., Vancouver, BC, Canada


email: mrtaha@unm.edu

J Biomech Eng 127(3), 484-493 (Dec 07, 2004) (10 pages) doi:10.1115/1.1894372 History: Received October 30, 2003; Revised December 07, 2004

In normal daily activities, ligaments are subjected to repeated loads, and respond to this environment with creep and fatigue. While progressive recruitment of the collagen fibers is responsible for the toe region of the ligament stress-strain curve, recruitment also represents an elegant feature to help ligaments resist creep. The use of artificial intelligence techniques in computational modeling allows a large number of parameters and their interactions to be incorporated beyond the capacity of classical mathematical models. The objective of the work described here is to demonstrate a tool for modeling creep of the rabbit medial collateral ligament that can incorporate the different parameters while quantifying the effect of collagen fiber recruitment during creep. An intelligent algorithm was developed to predict ligament creep. The modeling is performed in two steps: first, the ill-defined fiber recruitment is quantified using the fuzzy logic. Second, this fiber recruitment is incorporated along with creep stress and creep time to model creep using an adaptive neurofuzzy inference system. The model was trained and tested using an experimental database including creep tests and crimp image analysis. The model confirms that quantification of fiber recruitment is important for accurate prediction of ligament creep behavior at physiological loads.

Copyright © 2005 by American Society of Mechanical Engineers
Topics: Creep , Fibers , Stress , Fuzzy logic
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Figure 1

Fuzzy inference system general structure

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Figure 2

General ANFIS architecture with two input parameter (X and Y) and an output parameter (Z)

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Figure 3

Experimental MCL creep data and standard deviations for the normalized creep function JNORM(t,t0) at different stress levels (4, 7, 14, 28, and 38 MPa). (Some of the experimental data shown here have been published by Thornton (11) and Thornton (25).)

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Figure 4

Block diagram of the model used to predict creep of the MCL. Circles represent parameters and rectangles represent systems (modules).

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Figure 5

Internal Structure of the ANFIS model

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Figure 6

Change In ANFIS training and checking errors during training

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Figure 7

TSK fuzzy model to quantify collagen fibers recruitment in the rabbit MCL during creep relating creep time* and stress* to a FRI.

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Figure 8

TSK fuzzy model prediction of FRI versus experimentally determined FRI from crimp image analysis for pre- and post-creep of the rabbit MCL.

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Figure 9

FRI values as quantified by the TSK fuzzy model for the creep testing groups

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Figure 10

Time versus predicted and measured creep compliance of the MCL using the ANFIS network. The input data included the stress level (14 MPa), creep time, and the simulated FRI of the collagen fibers in the MCL during creep.

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Figure 11

Time versus predicted and measured creep compliance of the MCL using the ANFIS network. The input data included the stress level (28 MPa), creep time, and the simulated FRI of the collagen fibers in the MCL during creep.



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