Long-term health monitoring of concrete and steel bridges under large and missing data by unsupervised meta learning

Entezami, Alireza; Sarmadi, Hassan; Behkamal, Bahareh

doi:10.1016/j.engstruct.2023.115616

Cited by 40 publications

(14 citation statements)

References 39 publications

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“…Several studies have been carried out around the application of SHM on different structural systems, including steel and concrete [94,95], composite [96][97][98], masonry [99,100], timber [63,101], etc. However, due to its recent application, the age of SHM to assess the structural elements composed of glass is not profound.…”

Section: Applicationsmentioning

confidence: 99%

An overview of technological advancements in the application of structural health monitoring on glass elements

Ahani,

Zhang,

Wang

et al. 2024

Meas. Sci. Technol.

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The concepts behind employing structural health monitoring (SHM) to the glass structures are in their early stages. While limited information by the application of SHM on glass-made elements could obtain through the literature, recent technological advances enrich the state of the art around the glass elements. As yet because of the novelty in both using SHM and the application of LGs for structural purposes, the historical background around the application of SHM on glass elements doesn't exceed 20 years, and the novelty of the concepts around the LGs may be even more. With this in mind, the demanded assessments have to be incept from the very basis of both the glass and SHM sciences to place the stepping stones of the fundamental stage, which would be a platform for the extending evaluations of glass elements at higher levels. This study is an endeavor to bring forward the available approaches for elaborating SHM on structural elements composed of glass with an emphasis on glass façades. According to the outcomes, the requirement for early detection due to its brittle content proffers structural health monitoring as the limited available alternatives for damage detection of the systems composed of it.

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Section: Applicationsmentioning

confidence: 99%

An overview of technological advancements in the application of structural health monitoring on glass elements

Ahani,

Zhang,

Wang

et al. 2024

Meas. Sci. Technol.

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show abstract

“…Prior to starting the SHM procedure, some missing points are eliminated from the set of modal frequencies. 16 Accordingly, the total number of measurements corresponds to 3932. Figure 4 illustrates the final modal frequencies, where the feature points 1–3475 are concerned with the undamaged structural state and the other 457 samples of the modal frequencies belong to the damaged condition.…”

Section: Applicationsmentioning

confidence: 99%

“…4–6 Machine learning is the main area of artificial intelligence that intends to develop an automated learner (i.e., computational model) via training data and then conduct some tasks in terms of classification, regression, prediction, clustering, anomaly detection, etc. This methodology contains some underlying frameworks based on supervised, 7 semi-supervised, 8 and unsupervised learning 9 within some advanced algorithms under deep learning, 10 transfer learning, 11 active learning, 12 kernel learning, 13 multi-task learning, 14 dictionary learning, 15 meta-learning, 16 etc.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised data normalization for continuous dynamic monitoring by an innovative hybrid feature weighting-selection algorithm and natural nearest neighbor searching

Sarmadi

Entezami

Magalhães

2023

Structural Health Monitoring

Self Cite

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Continuous dynamic monitoring brings an important opportunity to evaluate the health and integrity of civil structures in a long-term manner. However, high dimensionality and sparsity of data caused by long-term monitoring and negative influences of environmental and/or operational variability are major challenges in this process. To address these important issues, this article proposes an innovative unsupervised data normalization method based on a novel hybrid feature weighting-selection algorithm and the idea of natural nearest neighbor (NN) searching emanated from the theory of mutual friendships in human societies. The proposed hybrid algorithm is a combination of global feature weighting with a new weighting measure and local feature selection. For this algorithm, this article leverages the natural NN searching that seeks to find adequate NNs automatically. The main objective of the proposed method is to remove the environmental and/or operational effects and provide normalized weighted features for reliable continuous dynamic monitoring. Using such features, an anomaly detector based on the Mahalanobis-squared distance is developed to assess and detect structural damage. The key innovations of this paper contain proposing a fully nonparametric unsupervised learning technique in two parts of data normalization and anomaly detection and developing a novel hybrid algorithm for removing the environmental and/or operational variations. Long-term dynamic features (modal frequencies) of a three-span box-girder concrete bridge (Z24 Bridge) and a long-span concrete arch bridge (Infante Dom Henrique Bridge) are considered to verify the proposed technique with several comparisons. Results indicate that this technique is successful and reliable in mitigating the environmental and/or operational effects and notifying accurate structural states.

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“…Feature extraction plays a crucial role in image processing and pattern recognition [19,20], enhancing model efficiency and performance by reducing data dimensions and preserving key information. In Table 3, we conducted a comparative analysis of various feature extraction methods.…”

Section: Feature Fusion Based On Discrete Cosine Transformmentioning

confidence: 99%

An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition

Zhang,

He,

et al. 2023

Applied Sciences

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In human and other organisms’ perception, olfaction plays a vital role, and biomimetic olfaction models offer a pathway for studying olfaction. The most optimal existing biomimetic olfaction model is the KIII model proposed by Professor Freeman; however, it still exhibits certain limitations. This study aims to address these limitations: In the feature extraction stage, it introduces adaptive histogram equalization, Gaussian filtering, and discrete cosine transform methods, effectively enhancing and extracting high-quality image features, thereby bolstering the model’s recognition capabilities. To tackle the computational cost issue associated with solving the numerical solutions of neuronal dynamics equations in the KIII model, it replaces the original method with the faster Euler method, reducing time expenses while maintaining good recognition results. In the decision-making stage, several different dissimilarity metrics are compared, and the results indicate that the Spearman correlation coefficient performs best in this context. The improved KIII model is applied to a new domain of traffic sign recognition, demonstrating that it outperforms the baseline KIII model and exhibits certain advantages compared to other models.

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Long-term health monitoring of concrete and steel bridges under large and missing data by unsupervised meta learning

Cited by 40 publications

References 39 publications

An overview of technological advancements in the application of structural health monitoring on glass elements

An overview of technological advancements in the application of structural health monitoring on glass elements

Unsupervised data normalization for continuous dynamic monitoring by an innovative hybrid feature weighting-selection algorithm and natural nearest neighbor searching

An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition

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