A New Damage Index Based on Statistical Features, PCA, and Mahalanobis Distance for Detecting and Locating Cables Loss in a Cable-Stayed Bridge

Yanez-Borjas, Jesus J.; Machorro-Lopez, Jose M.; Camarena-Martinez, David; Valtierra-Rodríguez, Martín; Amezquita-Sanchez, Juan P.; Carrion-Viramontes, Francisco J.; Quintana-Rodriguez, Juan A.

doi:10.1142/s0219455421501273

Cited by 18 publications

(10 citation statements)

References 54 publications

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“…Dynamic damage identification methods also include damage identifications that are based on signal processing [11][12][13] and damage identification based on statistical characteristics of the dynamic response. The latter utilizes statistical theory for damage detection directly in the time domain, and compared with other dynamic-based damage identification methods, this method is operationally simple and does not involve a complex modal processing procedure, which does not result in the loss of valid information.…”

Section: Of 23mentioning

confidence: 99%

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A Feature of Mechanics-Driven Statistical Moments of Wavelet Transform-Processed Dynamic Responses for Damage Detection in Beam-Type Structures

et al. 2022

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Multiple damage detection using structural responses only is a problem unresolved that is in the field of structural health monitoring. To address this problem, a novel feature of mechanics-driven statistical moments of wavelet transform-processed dynamic responses is proposed for multi-damage identification in beam-type structures. This feature is referred to as a continuous wavelet transform (CWT)-second-order strain statistical moment (SSSM), with CWT-SSSM in the abbreviation. The mechanical connotation of CWT-SSSM lies in that the SSSM of each order principal vibration contains strain mode shapes, inducing greater sensitivity to local damage. With this method, the CWT is used to extract and amplify the singularities caused by damage in each order SSSM curve, following which the data fusion technology and three-sigma rule in statistics are adopted to construct the damage index. The presence of damage is characterized by the abrupt change in the damage index. The soundness and characteristics of the CWT-SSSM feature are verified by identifying multiple damages in a cantilever beam bearing two breathing cracks. The results show that the proposed feature can accurately designate multiple cracks free of baseline information on the intact counterpart; moreover, it has robustness against noise and applicability under excitations of approximately uniform spectra.

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Section: Of 23mentioning

confidence: 99%

“…In addition, it is noted that the H ε sr (ω) in Formula (11) takes into account the contribution of all the strain mode shapes. Under the premise that only the contribution of the ith-order strain mode shape is considered, Formulas (11) and (12) can be rewritten as:…”

Section: Second-order Strain Statistical Moments (Sssm)mentioning

confidence: 99%

A Feature of Mechanics-Driven Statistical Moments of Wavelet Transform-Processed Dynamic Responses for Damage Detection in Beam-Type Structures

et al. 2022

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“…Based on the vibration signal, a Shannon entropy-based methodology for detecting and locating a lost cable in a cable-stayed bridge exposed to ambient vibrations was presented by Jose et al [ 23 ]. A methodology based on statistical features, principal component analysis (PCA), and Mahalanobis distance (MD) for detecting and locating cable loss using vibration signals was proposed by Jesus et al [ 24 ]. These two methods were validated in the Rio Papaloapan Bridge (Veracruz, Mexico) with a 100% effectiveness to detect the lost cable location.…”

Section: Introductionmentioning

confidence: 99%

Multiple Damaged Cables Identification in Cable-Stayed Bridges Using Basis Vector Matrix Method

Ren

Zhu

2023

Sensors

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A new damaged cable identification method using the basis vector matrix (BVM) is proposed to identify multiple damaged cables in cable-stayed bridges. The relationships between the cable tension stiffness and the girder bending strain of the cable-stayed bridge are established using a force method. The difference between the maximum bending strains of the bridges with intact and damaged cables is used to obtain the damage index vectors (DIXVs). Then, BVM is obtained by the normalized DIXV. Finally, the damage indicator vector (DIV) is obtained by DIXV and BVM to identify the damaged cables. The damage indicator is substituted into the damage severity function to identify the corresponding damage severity. A field cable-stayed bridge is used to verify the proposed method. The three-dimensional finite element model is established using ANSYS, and the model is validated using the field measurements. The validated model is used to simulate the strain response of the bridge with different damage scenarios subject to a moving vehicle load, including one, two, three, and four damaged cables with damage severity of 10%, 20%, and 30%, respectively. The noise effect is also discussed. The results show that the BVM method has good anti-noise capability and robustness.

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“…When working with rolling element bearing, Mathew and Alfredson [2] found that among more than twenty investigated statistical features, kurtosis is a damage-sensitive one that can provide a good indication of failure states. Yanez-Borjas et al [3] proposed to combine a set of statistical features, including high-order moments (up to 6 th ) and shape-related features, with the Principal Component Analysis algorithm and the Mahalanobis distance metric to perform SHM of load-bearing cables in a cable-stayed bridge. For SHM of wind turbines, root mean square is widely used as an effective parameter in identifying damage existence.…”

Section: Introductionmentioning

confidence: 99%

Robust Vibration Output-only Structural Health Monitoring Framework Based on Multi-modal Feature Fusion and Self-learning

Dang

Nguyen

2023

Period. Polytech. Civil Eng.

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Output-only structural health monitoring is a highly active research direction because it is a promising methodology for building digital twin applications providing near-real-time monitoring results of the structure. However, one of the technical bottlenecks is how to work effectively with multiple high-dimensional vibration signals. To address this question, this study develops a two-stage data-driven framework based on various advanced techniques, such as time-series feature extractions, self-learning, graph neural network, and machine learning algorithms. At first, multiple features in statistical, time, and spectral domains, are extracted from raw vibration data; then, they subsequently enter a graph convolution network to account for the spatial correlation of sensor locations. After that, the high-performance adaptive boosting machine learning algorithm is leveraged to assess structures' health states. This method allows for learning a lower-dimensional yet informative representation of vibration data; thus, the subsequent monitoring tasks could be performed with reduced time complexity and economical computational resources. The performance of the proposed method is qualitatively and quantitatively demonstrated through two examples involving both numerical and experimental structural data. Furthermore, comparison and robustness studies are carried out, showing that the proposed approach outperforms various machine learning/deep learning-based methods in terms of accuracy and noise/missing-robustness.

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A New Damage Index Based on Statistical Features, PCA, and Mahalanobis Distance for Detecting and Locating Cables Loss in a Cable-Stayed Bridge

Cited by 18 publications

References 54 publications

A Feature of Mechanics-Driven Statistical Moments of Wavelet Transform-Processed Dynamic Responses for Damage Detection in Beam-Type Structures

A Feature of Mechanics-Driven Statistical Moments of Wavelet Transform-Processed Dynamic Responses for Damage Detection in Beam-Type Structures

Multiple Damaged Cables Identification in Cable-Stayed Bridges Using Basis Vector Matrix Method

Robust Vibration Output-only Structural Health Monitoring Framework Based on Multi-modal Feature Fusion and Self-learning

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