Identifying Environmental- and Operational-Insensitive Damage Features

Gibbs, David; Jankowski, K.; Rees, Ben; Farrar, Charles R.; Flynn, Garrison

doi:10.1007/978-3-030-76004-5_13

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Selecting and estimating a reliable DSF is essential for the implementation of a successful statistical pattern recognition framework. The selected feature ought to be strongly correlated with the structural properties of the system, to detect structural changes due to the effect of damage, while being insensitive to changes due to noise or variations in operational and environmental conditions [13][14][15][16]. Modal parameters (e.g., natural frequencies or mode shapes [17]) and Auto Regressive model coefficients [18,19] are examples of popular and common DSFs often employed for structural health monitoring applications.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Damage Detection and Classification of Unknown Classes with a Hybrid Supervised–Unsupervised Approach

Stagi,

Sclafani,

Tronci

et al. 2024

Infrastructures

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Most damage-assessment strategies for dynamic systems only distinguish between undamaged and damaged conditions without recognizing the level or type of damage or considering unseen conditions. This paper proposes a novel framework for structural health monitoring (SHM) that combines supervised and unsupervised learning techniques to assess damage using a system’s structural response (e.g., the acceleration response of big infrastructures). The objective is to enhance the benefits of a supervised learning framework while addressing the challenges of working in an SHM context. The proposed framework uses a Linear Discriminant Analysis (LDA)/Probabilistic Linear Discriminant Analysis (PLDA) strategy that enables learning the distributions of known classes and the performance of probabilistic estimations on new incoming data. The methodology is developed and proposed in two versions. The first version is used in the context of controlled, conditioned monitoring or for post-damage assessment, while the second analyzes the single observational data. Both strategies are built in an automatic framework able to classify known conditions and recognize unseen damage classes, which are then used to update the classification algorithm. The proposed framework’s effectiveness is first tested considering the acceleration response of a numerically simulated 12-degree-of-freedom system. Then, the methodology’s practicality is validated further by adopting the experimental monitoring data of the benchmark study case of the Z24 bridge.

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Section: Introductionmentioning

confidence: 99%

Enhancing the Damage Detection and Classification of Unknown Classes with a Hybrid Supervised–Unsupervised Approach

Stagi,

Sclafani,

Tronci

et al. 2024

Infrastructures

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“…[3][4][5][6] Nonetheless, these features may not establish a robust relationship with damage, and environmental factors such as temperature can influence features like mode shapes and natural frequencies. [7][8][9] Given these limitations, researchers have begun adopting deep learning (DL) as a viable alternative to relying solely on hand-crafted sensitive features. DL-based approaches offer end-to-end feature extraction and classification, wherein feature extraction is performed automatically, and the algorithm autonomously learns significant features specific to the given problem from raw data.…”

Section: Introductionmentioning

confidence: 99%

A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection

Kord,

Taghikhany,

Akbari

2023

Structural Health Monitoring

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In recent years, convolutional neural networks (CNNs) have demonstrated promising results in detecting structural damage. However, their architectures often overlook spatial and temporal effects simultaneously. This limitation can result in the loss of valuable information and an incapability to fully capture the complexity of the data, ultimately leading to reduced accuracy and suboptimal performance. This study proposes an intuitive three-dimensional CNN architecture that takes into account vibration history along with sensor spatial relations based on their relative positions. Furthermore, a multi-task learning (MTL) approach is suggested, which is a powerful approach for performing multiple tasks with a single network. The proposed 3D CNN method has been employed to detect single and double damage cases in an experimental steel frame through conventional classification alongside the transfer learning (TL). Moreover, MTL is used to detect single and double damage scenarios with a single unified network, which evaluates damage presence in separate tasks. The 3D CNN fulfilled state-of-the-art performance and 100% accuracy in detecting structural damage in almost all experiments. Additionally, the MTL model achieved promising results even in the presence of severe imbalanced classes of data. Furthermore, it was observed that the utilization of TL resulted in a notable reduction of computation time by 68% and the number of trainable parameters by 90% with the same level of accuracy in double-damage cases.

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Identifying Environmental- and Operational-Insensitive Damage Features

Cited by 2 publications

References 9 publications

Enhancing the Damage Detection and Classification of Unknown Classes with a Hybrid Supervised–Unsupervised Approach

Enhancing the Damage Detection and Classification of Unknown Classes with a Hybrid Supervised–Unsupervised Approach

A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection

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