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PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING
Edited by: B.H.V. Topping
A Static Response Based Parameter Identification Algorithm using Optimality Criterion Optimization
A.S. Terlaje III and K.Z. Truman
Department of Civil Engineering, Washington University in St. Louis, MO, United States of America
A.S. Terlaje III, K.Z. Truman, "A Static Response Based Parameter Identification Algorithm using Optimality Criterion Optimization", in B.H.V. Topping, (Editor), "Proceedings of the Eleventh International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 88, 2007. doi:10.4203/ccp.86.88
Keywords: structural optimization, parameter identification, optimality criterion.
A parameter identification algorithm is presented that utilizes displacement measurements resulting from applied static loads. The algorithm employs optimality criterion optimization coupled with finite element models to extract the stiffness parameters of two-dimensional truss and moment frame structures. The results presented illustrate the algorithms effectiveness and promise as a parameter identification tool.
Most parameter identification methods utilize dynamic response data to both identify structural stiffness parameters and detect structural damage [1,2,3]. The use of dynamic data contains inherent complications and errors. The use of static data is an attempt to alleviate these complications while providing an alternative method which can be used instead or in conjunction with various dynamic methods. The use of static response data as a means of identification has not been explored as widely as the dynamic based methods.
Optimality Criterion optimization is used due to its effectiveness in structural optimization. Optimality Criterion methods have been used to help designers choose optimal member sizes to create the lowest cost structure [4,5], but its use as a tool for parameter identification has not been explored as widely. However, Optimality Criterion methods exhibit excellent convergence rates and little variability in computing effort compared to problem size.
Three examples are presented that will illustrate the efficiency of the algorithm. The number of iterations required to determine all stiffness parameters (i.e. moments of inertia of moment frame members and cross sectional areas of truss members) remains relatively unchanged regardless of the number of design variables or constraint equations. The results show that the method and algorithm have real promise as a parameter identification tool.
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