Modal identification and fatigue behavior of Eynel steel arch highway bridge with calibrated models

Sunca, Fezayil; Ergün, Mustafa; Altunışık, Ahmet Can; Günaydın, Murat; Okur, Fatih Yesevi

doi:10.1007/s13349-021-00512-w

Cited by 5 publications

(5 citation statements)

References 46 publications

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“…The results showed that the modal frequency of the arch was mainly affected by the change in elastic modulus. Sunca et al [166] used EFDD and the stochastic subspace identification method to identify the modal parameters of an Eynel steel arch bridge in the frequency domain and time domain, respectively. The results showed that the change in natural frequency was very limited, and there was no change between the vibration modes, which was in line with the actual situation of the bridge.…”

Section: Damage Identification Methods Of Steel Arch Bridgementioning

confidence: 99%

A Review on Damage Monitoring and Identification Methods for Arch Bridges

et al. 2023

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The damage monitoring and identification of arch bridges provide an important means to ensure the safe operation of arch bridges. At present, many methods have been developed, and the applicability and effectiveness of these methods depend on the damage type, structural configuration and available data. To guide the practical application of these methods, a systematic review is implemented in this paper. Specifically, the damage monitoring and identification methods of arch bridges are divided into the damage monitoring of local diseases and damage identification of overall performance. Firstly, the research on the damage monitoring of the local diseases of arch bridges is reviewed. According to the disease type, it is divided into four categories, including suspender inspection, void monitoring, stress detection and corrosion detection. For each disease, this paper analyzes the principles, advantages and shortcomings of various methods. Then, the damage identification methods of the overall performance of arch bridges are reviewed, including masonry arch bridges, steel arch bridges, reinforced concrete arch bridges and concrete-filled steel tubular arch bridges. And the commonly used damage indexes of damage identification methods are summarized. This review aims to help researchers and practitioners in implementing existing damage detection methods effectively and developing more reliable and practical methods for arch bridges in the future.

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Section: Damage Identification Methods Of Steel Arch Bridgementioning

confidence: 99%

A Review on Damage Monitoring and Identification Methods for Arch Bridges

et al. 2023

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“…However, learning from multiple modal information sources provides the possibility of capturing the correspondence between heterogeneous modalities and gaining a deep understanding of natural phenomena. This is supported by research in the field 16 . This text presents a novel approach to addressing complex problems that are challenging to solve using single‐modal learning in real‐world scenarios.…”

Section: Introductionmentioning

confidence: 97%

“…For example, to train a two‐channel neural network for behavior classification, some methods use RGB images and optical flow information 15 . Machine learning theory and practice have confirmed that knowledge can be transferred and shared between related machine‐learning task, and that learning multiple tasks together can lead to better performance than learning each task separately 16‐18 . Video behavior detection mainly focuses on the task of video behavior detection.…”

Section: Introductionmentioning

confidence: 99%

“…15 Machine learning theory and practice have confirmed that knowledge can be transferred and shared between related machine-learning task, and that learning multiple tasks together can lead to better performance than learning each task separately. [16][17][18] Video behavior detection mainly focuses on the task of video behavior detection. When dealing with a video that displays motion behavior, two objectives need to be met: classifying the behavior and locating the motion in either the spatial, temporal, or spatiotemporal domain.…”

mentioning

confidence: 99%

“…This is supported by research in the field. 16 This text presents a novel approach to addressing complex problems that are challenging to solve using single-modal learning in real-world scenarios. Examples of such problems include audio-visual speech recognition, 17 multi-modal emotion recognition, 18 multi-modal machine translation, 19 visual question answering (VQA), 20 multi-modal retrieval, 21 and multi-modal behavior recognition.…”

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confidence: 99%

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A face retrieval technique combining large models and artificial neural networks

2024

Concurrency and Computation

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SummaryMultimodal sentiment analysis is a popular research direction in the field of affective computing. It extends unimodal‐based sentiment analysis to the environment based on multimodal information exchange. Face retrieval is the most critical technology in the current multimodal sentiment analysis field. Traditional methods for face retrieval rely on large amounts of data to train the matching relationship between text and face images. This approach suffers from several issues, including high data acquisition costs, insufficient or poor quality training data, and high computational costs for training models. To address the aforementioned issues, this paper proposes a face retrieval technique that merges a large language model with a visual base model. This technique explores the use of zero samples for training, eliminating the need to collect data and train the model to achieve face retrieval. As a result, it significantly reduces the cost requirements of data and computational power. First, an accurate natural text of face description is generated using a large language model based on cue engineering based on face‐independent features. Second, the text and images are encoded using a visual base model and mapped into the same multidimensional space for matching. The experimental results indicate that this method performs well on the CelebA dataset. Extracting 31 different sets of face feature descriptions from the CelebA dataset for retrieval achieves 72% on top1 accuracy and 93% on top3 accuracy. Compared to traditional methods, the method proposed in this paper achieves considerable face retrieval accuracy without collecting text image pairs of data and without training the model.

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Temperature-based anomaly diagnosis of truss structure using Markov chain-Monte Carlo method

Liu

et al. 2022

J Civil Struct Health Monit

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Modal identification and fatigue behavior of Eynel steel arch highway bridge with calibrated models

Cited by 5 publications

References 46 publications

A Review on Damage Monitoring and Identification Methods for Arch Bridges

A Review on Damage Monitoring and Identification Methods for Arch Bridges

A face retrieval technique combining large models and artificial neural networks

Temperature-based anomaly diagnosis of truss structure using Markov chain-Monte Carlo method

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