Early damage detection under massive data via innovative hybrid methods: application to a large-scale cable-stayed bridge

Daneshvar, Mohammad Hassan; Gharighoran, Alireza; Zareei, Seyed Alireza; Karamodin, Abbas

doi:10.1080/15732479.2020.1777572

Cited by 30 publications

(10 citation statements)

References 50 publications

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“…Most recent data-based studies were conducted on cable-stayed bridges with extensive SHM systems collecting data for many years. Examples include the Sutong Bridge, the longest cable-stayed bridge in China with an SHM system in place since 2008 [33,55], the Seohae Grand Bridge, the longest cablestayed bridge in South Korea with monitoring date since 2000 [59], the Tianjin Yonghe Bridge, a cable-stayed bridge used for SHM benchmarking studies since 2007 [54,60], the Shanghai Yangtze River Bridge with SHM data dating from 2012 [57], and the Corgo River Viaduct in Portugal being monitored since its opening in 2013 [61].…”

Section: Model-based Shm Of Cable-stayed Bridgesmentioning

confidence: 99%

Latest Advances in Finite Element Modelling and Model Updating of Cable-Stayed Bridges

et al. 2022

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As important links in the transport infrastructure system, cable-stayed bridges are among the most popular candidates for implementing structural health monitoring (SHM) technology. The primary aim of SHM for these bridges is to ensure their structural integrity and satisfactory performance by monitoring their behaviour over time. Finite element (FE) model updating is a well-recognised approach for SHM purposes, as an accurate model serves as a baseline reference for damage detection and long-term monitoring efforts. One of the many challenges is the development of the initial FE model that can accurately reflect the dynamic characteristics and the overall behaviour of a bridge. Given the size, slenderness, use of long cables, and high levels of structural redundancy, precise initial models of long-span cable-stayed bridges are desirable to better facilitate the model updating process and to improve the accuracy of the final updated model. To date, very few studies offer in-depth discussions on the modelling approaches for cable-stayed bridges and the methods used for model updating. As such, this article presents the latest advances in finite element modelling and model updating methods that have been widely adopted for cable-stayed bridges, through a critical literature review of existing research work. An overview of current SHM research is presented first, followed by a comprehensive review of finite element modelling of cable-stayed bridges, including modelling approaches of the deck girder and cables. A general overview of model updating methods is then given before reviewing the model updating applications to cable-stayed bridges. Finally, an evaluation of all available methods and assessment for future research outlook are presented to summarise the research achievements and current limitations in this field.

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Section: Model-based Shm Of Cable-stayed Bridgesmentioning

confidence: 99%

Latest Advances in Finite Element Modelling and Model Updating of Cable-Stayed Bridges

et al. 2022

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“…In contrast, if the model or novelty detector depends on some hyperparameters, the anomaly detection method is parametric. Data clustering methods based on the k -means, 24 k -medoids, 25 fuzzy c -means, 15 and Gaussian mixture model, 26 and artificial neural networks based on an auto-associative neural network 27 and auto-encoder 28 are some well-known examples of this kind of unsupervised learning algorithm. Finally, a semi-parametric novelty detector is based on a combination of the non-parametric and parametric algorithms in order to enhance the performance of non-parametric detectors.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Finally, a semi-parametric novelty detector is based on a combination of the non-parametric and parametric algorithms in order to enhance the performance of non-parametric detectors. 15,27,29 Despite effective unsupervised learning methods for health monitoring of full-scale structures, some challenges are still open problems that need to be dealt with appropriately. In the context of data-driven SHM under the concept of machine learning, a high rate of decision-making errors (i.e., false positive and false negative) is a major challenging issue.…”

Section: Introductionmentioning

confidence: 99%

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Non-parametric empirical machine learning for short-term and long-term structural health monitoring

Entezami

Shariatmadar

Michele

2022

Structural Health Monitoring

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Early damage detection is an initial step of structural health monitoring. Thanks to recent advances in sensing technology, the application of data-driven methods based on the concept of machine learning has significantly increased among civil engineers and researchers. On this basis, this article proposes a novel non-parametric anomaly detection method in an unsupervised learning manner via the theory of empirical machine learning. The main objective of this method is to define a new damage index by using some empirical measure and the concept of minimum distance value. For this reason, an empirical local density is initially computed for each feature and then multiplied by the minimum distance of that feature to derive a new damage index for decision-making. The minimum distance is obtained by calculating the distances between each feature and training samples and finding the minimum quantity. The major contributions of this research contain developing a novel non-parametric algorithm for decision-making under high-dimensional and low-dimensional features and proposing a new damage index. To detect early damage, a threshold boundary is computed by using the extreme value theory, generalized Pareto distribution, and peak-over-threshold approach. Dynamic and statistical features of two full-scale bridges are used to verify the effectiveness and reliability of the proposed non-parametric anomaly detection. In order to further demonstrate its accuracy and proper performance, it is compared with some classical and recently published anomaly detection techniques. Results show that the proposed non-parametric method can effectively discriminate a damaged state from its undamaged condition with high damage detectability and inconsiderable false positive and false negative errors. This method also outperforms the anomaly detection techniques considered in the comparative studies.

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“…Despite the simplicity and effectiveness of this method, the complex operating conditions of bridges, the demand for a large amount of environmental data, and the limitation of sensor numbers may impede its application for establishment of accurate regression models. Conversely, the implicit methods like feature extraction, cointegration, Mahalanobis squared distance (MSD), and clustering analysis potentially considers environmental impacts without environmental measurements and has become a research hotspot (Yan et al 2005;Kostic and Gul 2017;Zhou and Sun 2019;Daneshvar et al 2021). The performance can be assessed according to false-positive (Type I) and false-negative (Type II) errors.…”

Section: Introductionmentioning

confidence: 99%

Early Anomaly Warning of Environment-Induced Bridge Modal Variability through Localized Principal Component Differences

Wang

Yang

et al. 2022

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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Accurate elimination of environmental variability on bridge modal frequency is a prerequisite for high-quality structural performance evaluation. However, the non-Gaussian and nonlinear characteristics of data distribution associated with variable environments restrict the application of anomaly warning methods with inaccurate or unreliable detection results. Consequently, an early warning method in abnormal modal frequency based on the localized principal component differences model through integrating the slow feature analysis (SFA) and k-nearest neighbor rule is proposed in this paper. SFA is first used to extract the measured slowly features of modal frequency for dimensionality reduction and redundant information elimination. Second, the localized modal set of each sample can be automatically searched from the training database based on the Euclidean distance metrics. Third, the estimated slowly features of modal frequency can be calculated using the mean vector of this set. Finally, the environmental variability can be suppressed through the principal component differences between measured and estimated slowly features. After this analysis, an early warning index of modal abnormality (i.e., Mahalanobis distance) is defined for enlarging slight changes in abnormal frequency. The warning results of Z24 bridge indicate that the proposed method discards the environment-induced modal variability without environmental measurements by fully considering both the nonlinearity between modal variables and the non-Gaussianity of data distribution, and the detectability of frequency anomalies outperforms conventional methods under various modal order combinations.

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Early damage detection under massive data via innovative hybrid methods: application to a large-scale cable-stayed bridge

Cited by 30 publications

References 50 publications

Latest Advances in Finite Element Modelling and Model Updating of Cable-Stayed Bridges

Latest Advances in Finite Element Modelling and Model Updating of Cable-Stayed Bridges

Non-parametric empirical machine learning for short-term and long-term structural health monitoring

Early Anomaly Warning of Environment-Induced Bridge Modal Variability through Localized Principal Component Differences

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