Detecting the damage of bridges under changing environmental conditions using the characteristics of the nonlinear narrow dimension of damage features

Yang, Changxi; Liu, Yang

doi:10.1016/j.ymssp.2021.107842

Cited by 10 publications

(6 citation statements)

References 45 publications

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“…There are also methods proposed to eliminate the influence of changing environmental conditions in the case when temperature is not measured [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. The singular value decomposition method and the auto-associative neural network method have been applied to implicitly model the underlying relationship between environmental variables and damage-sensitive features.…”

Section: Introductionmentioning

confidence: 99%

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

Hu,

Liu,

Deng

et al. 2024

Materials

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Temperature is one of the most important factors significantly affecting damage detection performance in civil engineering. A new method called the Adaptive Bandwidth Filter Algorithm (ABFA) is proposed in this paper to separate the temperature effect from quasi-static long-term structural health monitoring data. The Adaptive Bandwidth Filter Algorithm (ABFA) is referred to as an algorithm of automatically adjusting the frequency bandwidth filter via the particle swarm optimization (PSO) algorithm. Considering the obvious multi-scale feature of the collected data of civil structure, the acquired time series are divided into different time scales (for example, day, month, year, etc.), and these scales in the frequency domain correspond to the center frequencies of the adaptive bandwidth filter. The temperature effect on structure responses across different time scales is thereafter explored by adaptively adjusting the frequency bandwidth of the filter based on the known center frequencies of different scales. The relationship between the temperature and the structure responses is established through statistical regression facilitated by sufficient in situ monitoring data. Simulation and experiment results show the very promising performance of the proposed algorithm and decouple the temperature effect accurately from the contaminated data; thus an enhanced capability of damage detection is achieved.

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Section: Introductionmentioning

confidence: 99%

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

Hu,

Liu,

Deng

et al. 2024

Materials

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“…To address this issue, the structure health monitoring (SHM) technique has been proposed to diagnose structural damage and assess the condition of a structure by utilizing a large amount of monitoring data (Amezquita‐Sanchez & Adeil, 2015a; Y. Liu et al., 2021; S. Zhang & Liu, 2020). In the early state of SHM development, vibration‐based damage detection and model update techniques were extensively studied (Y. Liu & Zhang, 2017; C. Yang & Liu, 2021) and have been widely applied to actual structures (Cao et al., 2021; Curry & Cremona, 2012; Ou & Li, 2010). In recent years, with the promotion of visual sensing equipment and the significant progress of computer vision (CV) technology, CV‐based SHM technology has received extensive attention in the field of SHM (Gao & Mosalam, 2018; Xue & Li, 2018; X. Zhang et al., 2019), and CV modules have gradually become an indispensable part of modern SHM frameworks (Dai & Cao, 2017; Y.‐F.…”

Section: Introductionmentioning

confidence: 99%

“…In the early state of SHM development, vibration-based damage detection and model update techniques were extensively studied (Y. Liu & Zhang, 2017;C. Yang & Liu, 2021) and have been widely applied to actual structures (Cao et al, 2021;Curry & Cremona, 2012;Ou & Li, 2010).…”

Section: Introductionmentioning

confidence: 99%

Detecting cracks in concrete structures with the baseline model of the visual characteristics of images

Liu

Gao

2022

Computer aided Civil Eng

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A method based on the baseline model of the visual characteristics of images (BMVCI) is proposed to detect cracks in concrete structures. BMVCI refers to the model, which consists of images of the noncrack areas of a concrete structure with cracks or images of the noncrack state of a concrete structure. Compared with the performance of edge detection (ED) methods for detecting cracks in concrete structures, this baseline model expands the quasi-distance between the edges of cracks and the image background; thus, the crack detection accuracy is effectively improved. Additionally, the discriminative threshold of cracks is quantitatively determined with BMVCI, which avoids the influence of artificial interference when determining the abovementioned threshold used for ED methods. Meanwhile, compared with the methods based on artificial intelligence, such as deep learning (DL), the calculating efficiency of the proposed method is higher because the proposed method converts the high-dimensional image data into low-dimensional digital features for training. With the same small size set of training samples, the accuracy of the crack detection of the proposed method is higher than that of the methods based on the framework of DL.In this study, Gaussian convolution is applied to generate the visual characteristics of images, and then a kernel principal component analysis-based method is implemented to establish the BMVCI. The basic idea of novelty detection is applied to detect cracks in concrete structures. Finally, an experiment on concrete structures is designed and applied to demonstrate the effectiveness of the proposed method.

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“…Therefore, the question of how to effectively eliminate the influence of these complex environmental factors and extract damage diagnosis indicators that are sensitive to structural damage still needs to be further studied. 7,8…”

Section: Introductionmentioning

confidence: 99%

Damage localization for bridges monitored within one cluster based on a spatiotemporal correlation model of strain monitoring data

Cao

Liu

2022

Structural Health Monitoring

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The bridges monitored within one cluster refer to several medium- and small-span beam bridges with similar structural characteristics located in a continuous elevated corridor. The variation in the strain monitoring data of these bridges comprehensively reflects diverse coupling effects. These complex coupling factors present great challenges for the damage diagnosis of bridges. To address this issue, a damage localization method for bridges monitored within one cluster is proposed based on a spatiotemporal correlation model of strain monitoring data between bridges. First, a deep learning architecture combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network is established, which can reveal the complex time-varying mapping relationship between the strain monitoring data for similar bridges within one cluster to obtain an accurate spatiotemporal correlation model. Second, a strain prediction framework is presented that uses the proposed spatiotemporal correlation model after training. On this basis, the predicted and measured strains can be utilized to calculate a damage localization index that is not affected by complex coupling factors. Then, combined with abnormal diagnosis theory, the proposed index is implemented to accurately localize damage in all bridges within one cluster. Finally, the application of the proposed method to three actual bridges monitored within one cluster demonstrates the accuracy of the spatiotemporal correlation model and the effectiveness of structural damage localization.

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Detecting the damage of bridges under changing environmental conditions using the characteristics of the nonlinear narrow dimension of damage features

Cited by 10 publications

References 45 publications

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

Detecting cracks in concrete structures with the baseline model of the visual characteristics of images

Damage localization for bridges monitored within one cluster based on a spatiotemporal correlation model of strain monitoring data

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