Structural Damage Identification Based on the Transmissibility Function and Support Vector Machine

Diao, Yansong; Men, Xue; Sun, Zhanliang; Guo, Kongzheng; Wang, Yumei

doi:10.1155/2018/4892428

Cited by 15 publications

(8 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The correctness of the model has been verified. To simulate structural damage, this model employs a stiffness reduction approach [24] . Specifically, the overall damage of the model is achieved by reducing the elastic modulus of concrete throughout the entire model by a certain percentage.…”

Section: Finite Element Modelmentioning

confidence: 99%

Structural damage identification method based on Swin Transformer and continuous wavelet transform

Xin,

Tao,

Tang

et al. 2024

Intell Robot

View full text Add to dashboard Cite

The accuracy improvement of deep learning-based damage identification methods has always been pursued. To this end, this study proposes a novel damage identification method using Swin Transformer and continuous wavelet transform (CWT). Specifically, the original structural vibration data is first transferred to a time-frequency diagram by CWT, thereby capturing the characteristic information of structural damage. Secondly, the Swin Transformer is applied to learn the two-dimensional time-frequency diagram layer by layer and extract the damage information, by which the damage identification is achieved. Then, the identification accuracy of the proposed method is analyzed under various sample lengths and different levels of environmental noise to validate the robustness of this approach. Finally, the practicality of this method is verified through laboratory test. The results show the proposed method can effectively recognize the damage and achieve excellent accuracy even under noise interference. Its accuracy reaches 99.6% and 99.0% under single damage and multiple damage scenarios, respectively.

show abstract

Section: Finite Element Modelmentioning

confidence: 99%

Structural damage identification method based on Swin Transformer and continuous wavelet transform

Xin,

Tao,

Tang

et al. 2024

Intell Robot

View full text Add to dashboard Cite

show abstract

“…Regarding the application of machine learning methods, Xu et al [37] used a residual strain energy method and employed principal component analysis to remove the influence of the environmental temperature variation. Concerning the non-parametric vibration-based methods, it is worth mentioning the work of Diao et al [38] and Cheng and Wang [39] as applied to offshore platform structures.…”

Section: Vibration-based Damage Detection Of Wind Turbines and Offsho...mentioning

confidence: 99%

A Damage Detection and Location Scheme for Offshore Wind Turbine Jacket Structures Based on Global Modal Properties

Cevasco

Tautz-Weinert

Richmond

et al. 2022

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

View full text Add to dashboard Cite

Structural failures of offshore wind turbine substructures might be less likely than failures of other equipment of the wind turbine generator but pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations like inspections and maintenance, thus remote monitoring shows promise for cost-efficient structural integrity management. This work is aimed to investigate the feasibility of the two-level detection, in terms of anomaly identification and localisation, in the jacket structure of an offshore wind turbine. A monitoring scheme is developed based on a database of modal properties of the structure for different scenarios. The method identifies the correct anomaly scenario based on three types of modal indicators, namely natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher's linear discriminant analysis is applied to transform the modal indicators to maximise the separability of anomaly scenarios. A Fuzzy clustering algorithm is trained to predict the membership of new data to the scenarios in the database. In a case study, extreme scour phenomena and jacket member integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and localise the simulated scenarios via the global monitoring of an offshore wind jacket structure.

show abstract

“…23 Applying principal component analysis and wavelet packet decomposition, two novel damage features named low order principal component and wavelet packet energy vector were constructed for detecting the decrease of elastic modulus of an offshore platform. 24 Combining transmissibility function and modal assurance criterion (MAC) with the particle swarm optimization, reduction in stiffness of a plane truss structure with bars was identified. 25 Applying power spectra to define transmissibility function-based damage detection indicator, a set of composite beams subjected to damages and varying operational conditions were monitored.…”

Section: Introductionmentioning

confidence: 99%

A novel fault evaluation method based on nonlinear vibration features and Euclidean distance measurement for grid-like structures

Liao

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

Complex grid-like structures such as steel truss bridges and steel truss roofs commonly exist in civil engineering applications. Since these structures are usually subject to dynamic operational forces, faults like chemical corrosion, fatigue crack, and bolt loosening arise, and seriously affect structural health. To consider some issues in existing frequency domain fault evaluation methods like applicability for complex structures, requirement of baseline data, neglect of nonlinear boundaries, and localization of local faults, a novel method using nonlinear vibration features and Euclidean distance measurement is presented in this paper. Firstly, complex grid-like structures are decomposed into a series of simple T-type substructures according to structural characteristics and load paths. Secondly, related T-type multi-degree-of-freedom model is built by simulating potential faults and nonlinear boundaries as related nonlinear damper-spring connections. Thirdly, exciting the model and extracting output responses only, novel and local fault features, which are functions of structural properties, nonlinear fault-induced loads and transmissibility functions, are defined. Then, utilizing defined features from the local substructure to be evaluated only, a novel fault index is defined with Euclidean distance measurement. Finally, corresponding evaluation method is developed, and its effectiveness and applicability are demonstrated by comparative studies on a lab bolted grid-like structure.

show abstract

Structural Damage Identification Based on the Transmissibility Function and Support Vector Machine

Cited by 15 publications

References 43 publications

Structural damage identification method based on Swin Transformer and continuous wavelet transform

Structural damage identification method based on Swin Transformer and continuous wavelet transform

A Damage Detection and Location Scheme for Offshore Wind Turbine Jacket Structures Based on Global Modal Properties

A novel fault evaluation method based on nonlinear vibration features and Euclidean distance measurement for grid-like structures

Contact Info

Product

Resources

About