A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

Entezami, Alireza; Sarmadi, Hassan; Behkamal, Bahareh

doi:10.1016/j.ymssp.2023.110676

Cited by 39 publications

(8 citation statements)

References 56 publications

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“…In contrast, a data-based approach uses raw measured data and extracts features from them for SHM based on statistical pattern recognition and ML. ML method, which including supervised learning and unsupervised learning [36][37][38], is useful for predicting and assessing structural performance (e.g. displacement monitoring [39]), identifying structural condition (e.g.…”

Section: Shm Techniquesmentioning

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: Shm Techniquesmentioning

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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“…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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“…ML tools are employed to both estimate and evaluate structural performance. Data from numerical studies [28,29], experimental tests [30,31], and health monitoring of actual buildings over time [32,33] are often used in these evaluations. Significant interest has been shown in using ML to study how buildings might react during earthquakes, given ML's proven capability to uncover hidden patterns in complex scenarios in fields like science and engineering [34].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids

Kazemi,

Ghisi,

Entezami

2024

Buildings

Self Cite

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In the realm of architectural computing, this study explores the integration of parametric design with machine learning algorithms to advance the early design phase of tall buildings with outer diagrid systems. The success of such an endeavor relies heavily on a data-driven and artificial intelligence-enhanced workflow aimed at identifying key architectural and structural variables through a feature/response selection process within a supervised machine learning framework. By augmenting an initial dataset, which was notably limited, through four distinct techniques—namely Gaussian copula, conditional generative adversarial networks, Gaussian copula generative adversarial network, and variational autoencoder—this study demonstrates a methodical approach to data enhancement in architectural design. The results indicate a slight preference for the Gaussian copula method, attributed to its less complex hyperparameter tuning process. Evaluation through a random forest regressor revealed stable performance across various cross-validation techniques on synthetic data, although with an acceptable decrease in the coefficient of determination, from an original average score of 0.925 to an augmented score of 0.764. This investigation underscores the potential of artificial intelligence-powered computational tools to guide design decisions by pinpointing the variables with the most significant impact on relevant outputs, quantitatively assessing their influence through the accuracy of the employed machine learning methods.

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A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

Cited by 39 publications

References 56 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

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

Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids

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