A decoupled algorithm for cable parameter identification by frequency‐ratio‐offset

Abstract

This paper proposes a frequency‐ratio‐offset (FRO)‐based parameter identification algorithm for real bridge cables. By introducing specific time and space scaling factors, the dynamic equation is fully nondimensionalized and reduced to a most concise form, independent of all distributed cable parameters. As a result, the frequency equation has only two variables, which can be easily solved to obtain the universal dimensionless frequency–length curve under specified boundary conditions. Based on these curves, a decoupled identification algorithm is constructed, using the newly defined FRO as a bridge to directly connect the dimensionless beam and its real‐world counterpart. The algorithm enables a simultaneous determination of any two of the three cable parameters using a pair of measured frequencies. Examples involving both laboratory tests and real‐life applications demonstrate the effectiveness and accuracy of the algorithm. Based on a statistical analysis of the fitness of dozens of predicted and measured frequencies, a guideline for frequency selection is suggested, and the influence of measurement error on the identification results is discussed. The proposed algorithm could become a convenient tool for engineers to efficiently identify multiple parameters (e.g., tension and bending stiffness) for a broad class of bridge cables.

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“…Gai et al (2023) used the neural network model and the finite element model as the basis to establish the correlation between cable structure characteristics such as cable length and input frequency and cable force. Sun et al (2023) proposed a real bridge cable parameter identification algorithm based on frequency ratio offset. In addition, nonlinear feature extraction methods (Amezquita-Sanchez & Adeli, 2019) may also provide help in solving this problem.…”

“…Sun et al. (2023) proposed a real bridge cable parameter identification algorithm based on frequency ratio offset. In addition, nonlinear feature extraction methods (Amezquita‐Sanchez & Adeli, 2019) may also provide help in solving this problem.…”