Literature DB >> 33546231

A Step-by-Step Damage Identification Method Based on Frequency Response Function and Cross Signature Assurance Criterion.

Jiawang Zhan1, Fei Zhang1, Mohammad Siahkouhi1.   

Abstract

This paper aims to present a method for quantitative damage identification of a simply supported beam, which integrates the frequency response function (FRF) and model updating. The objective function is established using the cross-signature assurance criterion (CSAC) indices of the FRFs between the measurement points and the natural frequency. The CSAC index in the frequency range between the first two frequencies is found to be sensitive to damage. The proposed identification procedure is tried to identify the single and multiple damages. To verify the effectiveness of the method, numerical simulation and laboratory testing were conducted on some model steel beams with simulated damage by cross-cut sections, and the identification results were compared with the real ones. The analysis results show that the proposed damage evaluation method is insensitive to the systematic test errors and is able to locate and quantify the damage within the beam structures step by step.

Entities:  

Keywords:  cross-signature assurance criterion (CSAC); damage identification; frequency response function (FRF); model updating; simply supported beam; structural health monitoring

Year:  2021        PMID: 33546231      PMCID: PMC7913376          DOI: 10.3390/s21041029

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  3 in total

Review 1.  Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation.

Authors:  Xiang Zhu; Maosen Cao; Wieslaw Ostachowicz; Wei Xu
Journal:  Sensors (Basel)       Date:  2019-01-23       Impact factor: 3.576

2.  Probabilistic Damage Detection of a Steel Truss Bridge Model by Optimally Designed Bayesian Neural Network.

Authors:  Tao Yin; Hong-Ping Zhu
Journal:  Sensors (Basel)       Date:  2018-10-09       Impact factor: 3.576

3.  Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study.

Authors:  Asma Alsadat Mousavi; Chunwei Zhang; Sami F Masri; Gholamreza Gholipour
Journal:  Sensors (Basel)       Date:  2020-02-26       Impact factor: 3.576

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.