On the vulnerability of data-driven structural health monitoring models to adversarial attack

Champneys, Max D.; Green, André; Morales, John; Silva, Moisés; Mascareñas, David

doi:10.1177/1475921720920233

Cited by 7 publications

(2 citation statements)

References 29 publications

(30 reference statements)

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“…The variety of different scenarios applied to the structure is the main reason that induced several researchers to investigate its dynamical behavior. [61][62][63][64] Figure 2(a) shows the structure's layout, where aluminum plates and four aluminum columns were assembled using bolted joints to compose each floor. Figure 2(b) shows the damage mechanism located at the center of the structure's top floor.…”

Section: Damage Detection Methodologymentioning

confidence: 99%

Damage quantification using transfer component analysis combined with Gaussian process regression

Yano

Silva

Figueiredo

et al. 2022

Structural Health Monitoring

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Machine learning methods used in Structural Health Monitoring applications still have generalization difficulties among structures, even when structures are nominally and topologically similar. The data sets present divergences between their probability distributions that do not allow the model’s generalization for damage detection. This issue is even more complex in situations where one wants to quantify damage levels through data sets collected from different structures. Transfer learning methods offer a solution to overcome those limitations, using relevant information from a labeled structure (source domain) to assist the analysis of another structure (target domain) under unknown conditions. Therefore, this paper proposes the use of transfer component analysis to mitigate divergences between the model/structure’s features, and the label consistency requirement is applied in combination with a Gaussian process regression model for damage quantification. The effectiveness of the estimated model improves when the labels consistency between domains is achieved, indicating the current damage level in the structure when the regression model achieves its best performance (lowest error). The proposed methodology is applied on the benchmark data of a three-story building structure from the Los Alamos National Laboratory using the knowledge from its numerical model under several conditions, where the complete information of its behavior is available. The results compare the analysis in the original space and after applying the proposed methodology, demonstrating an improvement of the performance in the damage detection and quantification steps.

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Section: Damage Detection Methodologymentioning

confidence: 99%

Damage quantification using transfer component analysis combined with Gaussian process regression

Yano

Silva

Figueiredo

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…5G integration with SHM is becoming widespread, although security aspects such as integration need to be further evaluated. 357 Mobile edge computing and fog networks as part of the capabilities brought by 5G and 6G enable on-site device deployment of models and algorithms used for rapid assessment of civil infrastructures. 358 • Versatile SHM systems require a robust data management scheme.…”

Section: Open Research Issuesmentioning

confidence: 99%

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

266

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Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

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A Multi Stage Data Attack Traceability Method Based on Convolutional Neural Network for Industrial Internet

Xu,

Liu

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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On the vulnerability of data-driven structural health monitoring models to adversarial attack

Cited by 7 publications

References 29 publications

Damage quantification using transfer component analysis combined with Gaussian process regression

Damage quantification using transfer component analysis combined with Gaussian process regression

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

A Multi Stage Data Attack Traceability Method Based on Convolutional Neural Network for Industrial Internet

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