The Yellow Frame: Experimental Studies and Remote Monitoring of the Structural Health Monitoring Benchmark Structure

Mendler, Alexander; Ventura, Carlos E.; Allahdadian, Saeid

doi:10.1007/978-3-319-75390-4_21

Cited by 5 publications

(8 citation statements)

References 7 publications

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“…Yellow Frame is considered a benchmark for testing the state-of-the-art SHM approaches. Further details on Yellow Frame can be found in Mendler et al 25 The frame response is measured via fifteen accelerometers with a sampling frequency of 1000 Hz over four different days. Further instrumentation details are explained in Allahdadian.…”

Section: Yellow Framementioning

confidence: 99%

“…Yellow Frame 25 is a four-story, one-third scaled modular steel frame established at the University of British Columbia, as shown in (Figure 1(a)). The frame’s elements, such as beams, masses, and braces, can be manipulated to simulate different damage scenarios.…”

Section: Case Studiesmentioning

confidence: 99%

“…Yellow Frame is considered a benchmark for testing the state-of-the-art SHM approaches. Further details on Yellow Frame can be found in Mendler et al 25

Figure 1.Yellow Frame: (a) structure 26 (b) brace labeling, and sensor locations. Used with author’s permission.…”

Section: Case Studiesmentioning

confidence: 99%

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A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features

Soleimani‐Babakamali

Soleimani-Babakamali

Sarlo

2021

Structural Health Monitoring

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This study proposes a novelty-classification framework that applies to structural health monitoring (SHM) and sensor output validation (SOV) problems. The proposed framework has simple high-dimensional features with several advantages. First, the feature extraction method is extensively applicable to instrumented structures. Second, the high-dimensional features’ utilization alleviates one of the main issues of supervised novelty classifications, namely, imbalanced datasets and low-sampled data classes. Recurrent Neural Networks are employed for the classification of high-dimensional features. Furthermore, generative adversarial networks (GAN) are trained with low-sampled data classes’ high-dimensional features for generating new data objects. The generated data objects are combined with the initial training set for improving classification results. The proposed framework is studied on two SHM and SOV datasets. The SHM dataset has twenty-one data classes, with a total test accuracy of 99.60% compared to another study with 88.13% accuracy. The SOV classification shows improved results with a mean accuracy of 96.5% compared to three other studies with mean accuracy values of 93.5%, 92.97%, and 71.1%. Furthermore, the integration of GAN’s generated data objects with low-sampled classes improved those classes’ mean F1 score from 44.77% to 64.58% and from 73.39% to 90.84% on SOV and SHM case studies, respectively. The integration of GAN-generated data objects with the initial low-sampled data classes for accuracy improvement shows more potential in the SHM dataset than the SOV case, which can be due to the signal pattern-based labeling logic of SOV datasets.

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Section: Yellow Framementioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

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A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features

Soleimani‐Babakamali

Soleimani-Babakamali

Sarlo

2021

Structural Health Monitoring

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“…Yellow Frame [26] is a one-third modular four-story steel frame established at the University of British Columbia, as shown in (Fig. 1a).…”

Section: Yellow Framementioning

confidence: 99%

“…There are fifteen accelerometers on the structure, capturing the ambient vibration response with a sampling frequency of 1000 Hz. Readers are referred to Mendler et al [26] and Allahdadian [27] for details of the structure and instrumentation.…”

Section: Yellow Framementioning

confidence: 99%

System-reliability based multi-ensemble of GAN and one-class joint Gaussian distributions for unsupervised real-time structural health monitoring

Soleimani-Babakamali,

Sepasdar,

Nasrollahzadeh

et al. 2021

Preprint

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Unsupervised health monitoring has gained much attention in the last decade as the most practical real-time structural health monitoring (SHM) approach. Among the proposed unsupervised techniques in the literature, there are still obstacles to robust and real-time health monitoring. These barriers include loss of information from dimensionality reduction in feature extraction steps, case-dependency of those steps, lack of a dynamic clustering, and detection results' sensitivity to user-defined parameters. This study introduces an unsupervised real-time SHM method with a mixture of low-and high-dimensional features without a case-dependent extraction scheme. Both features are used to train multi-ensembles of Generative Adversarial Networks (GAN) and one-class joint Gaussian distribution models (1-CG). A novelty detection system of limit-state functions based on GAN and 1-CG models' detection scores is constructed. The Resistance of those limit-state functions (detection thresholds) is tuned to user-defined parameters with the GAN-generated data objects by employing the Monte Carlo histogram sampling through a reliability-based analysis. The tuning makes the method robust to user-defined parameters, which is crucial as there is no rule for selecting those parameters in a real-time SHM. The proposed novelty detection framework is applied to two standard SHM datasets to illustrate its generalizability: Yellow Frame (twenty damage classes) and Z24 Bridge (fifteen damage classes). All different damage categories are identified with low sensitivity to the initial choice of user-defined parameters with both introduced dynamic and static baseline approaches with few or no false alarms.

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Launching Semi-automated Modal Identification of the Port Mann Bridge

Mendler

Ventura

Nandimandalam

et al. 2019

Conference Proceedings of the Society for Experimental Mechanics Series

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The Yellow Frame: Experimental Studies and Remote Monitoring of the Structural Health Monitoring Benchmark Structure

Cited by 5 publications

References 7 publications

A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features

A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features

System-reliability based multi-ensemble of GAN and one-class joint Gaussian distributions for unsupervised real-time structural health monitoring

Launching Semi-automated Modal Identification of the Port Mann Bridge

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