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Nonlinear structural damage detection using output‐only Volterra series model
Author(s) -
Peng Zhen,
Li Jun,
Hao Hong,
Li Chao
Publication year - 2021
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2802
Subject(s) - nonlinear system , volterra series , nonlinear system identification , series (stratigraphy) , structural system , control theory (sociology) , structural health monitoring , precast concrete , function (biology) , computer science , structural engineering , system identification , engineering , measure (data warehouse) , physics , artificial intelligence , paleontology , control (management) , quantum mechanics , database , evolutionary biology , biology
Summary Volterra series is a promising technique with great potential for nonlinear system identification. The conventional Volterra series model computes the output responses by performing multiple convolutions between the input excitation and Volterra kernels function. However, the difficulty in acquiring the excitation forces of civil engineering structures under operating conditions greatly limits the application of using Volterra series‐based method for system identification. This paper proposes an output‐only‐based approach using Volterra series model for nonlinear structural damage detection, by quantifying the nonlinear behavior of structures without the prior knowledge of external excitations. The proposed approach uses the structural responses measured at two different locations to identify the kernel function parameters and evaluate the contribution of nonlinear components in the measured responses. The ratio between the standard deviation of the nonlinear components and that of the overall structural response is adopted as damage‐sensitive index to quantify the contributions from these two adjacent sensors for performing nonlinear structural damage detection. Numerical studies on a beam structure with a breathing crack under different levels of white noise excitations and experimental studies on a precast segmental concrete column subjected to ground motions with different peak ground acceleration (PGA) values are conducted to validate the capability and accuracy of using the proposed approach for nonlinear structural damage detection. The results demonstrate that the proposed approach is capable of performing nonlinearity quantification effectively and locating structural nonlinear damage. The increasing damage index value can also be used to register the increasing damage severity.