Optimization method based on Generalized Pattern Search Algorithm to identify bridge parameters indirectly by a passing vehicle

Generalized Pattern Search Algorithm (GPSA) has rarely been investigated for structural health monitoring, but may have potential application in civil engineering, because it does not require any gradient information of the objective function. Meanwhile, indirect identification is an attractive concept that recognizes the bridge parameters by the vehicle responses. This paper proposes a theoretical indirect identification method based on optimization method, and the implementation is performed by the GPSA. Firstly, the GPSA theory is investigated, and a simple example is employed to describe the process of the algorithm. Secondly, a theoretical indirect identification method is proposed, based on the optimization method rather than the conventional transforms from time domain to frequency domain. The proposed method can identify the parameters of the vehicle bridge system, including the bridge stiffness and the 1st frequency. Based on the optimization method, the feasibility and accuracy of GPSA are demonstrated with 0.06% of errors. The GPSA shows good robustness in the identifications with various noise levels, and the maximum error is about 3.30% and can be accepted for the engineering application even with a SNR 5 noise level. The computation time relies only on the function evaluation times, and is not positively related to the noise level. Thirdly, the performance of GPSA is compared with that of Genetic Algorithm (GA). The accuracy of GPSA and GA are approximately equivalent with various noise levels. Compared with GA, GPSA needs fewer iterations and much fewer evaluations, therefore is more efficient in the identification with an almost consistent accuracy with various noise levels. (C) 2013 Elsevier Ltd. All rights reserved.