การพัฒนาประสิทธิภาพการประมาณค่าพารามิเตอร์ของโครงสร้างจากผลตอบสนองเชิงโหมดโดยวิธีปริภูมิย่อยไครโลฟ - ณัฐพล จารุศิริสมบัติ (จุฬาลงกรณ์มหาวิทยาลัย, 2004)
Structural parameter estimation schemes are generally used for the identification of certain constitutive properties, e.g. stiffness parameters, of the structures that minimize the discrepancies between the responses obtained from mathematical modeling and the actual measurements. The structural parameter estimation problem can be cast as the least-squares minimization of the deviation between the computed and the measured modal response, using the recursive quadratic programming to obtain the optimal solution. It was found from a preliminary study that the performance of the method is limited by the choice of the algorithm for solving the corresponding systems of linear equations. When the structural model is more complex or composed of more degrees of freedom, the estimation time can significantly increase. The current study investigates the use of the Krylov subspace methods, e.g. conjugate gradient (CG), symmetric LQ (SYMMLQ), minimum residual (MINRES), and symmetric quasi-minimum residual (SQMR) in comparison with the LU decomposition method in solving these systems of linear equations. The performance of these methods will be compared through case studies in order to assess the efficiency of the Krylov subspace methods in structural parameter estimation. The comparison of the computational efficiency in structural parameter estimation is based upon a three-dimensional truss model as the case study. It is found from the simulation results that the use of the Krylov subspace methods is able to improve the computation time in solving the system of linear equations involved in the parameter estimation of the structure. Nonetheless, for the case in which the measurements are incomplete, the Krylov subspace methods are computationally efficient when using only the low-frequency modes. Furthermore, the performance of the Krylov subspace methods in structural parameter estimation tends to decrease with the reducing number of the measured degree of freedom for the vibration modes.