Fuzzy clustering of time-series model to damage identification of structures

Zeng, Yongping; Yan, Yongyi; Weng, Shun; Sun, Yanjing; Tian, Weisheng; Yu, Hong Liang

doi:10.1177/1369433218789191

Cited by 10 publications

(4 citation statements)

References 46 publications

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“…Clustering algorithms are unsupervised machine learning methods that group unlabeled data based on their similarity into clusters. Fuzzy c-means, proposed by Dunn [51] and Bezdek [52], is a low-cost and straightforward clustering algorithm that is widely used in structural health monitoring problems [33,[53][54][55][56][57][58]. Fuzzy c-means minimizes Euclidean distance as a similarity measurement between clusters' centroids and data features to cluster data points [51,52].…”

Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

Event identification in acoustic emission from wire breaks in pre-stressing/post-tensioning cables

Mahmoudkhani,

Zhao,

Cochingco

et al. 2024

Eng. Res. Express

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Steel tendons commonly used in pre-stressed/post-tensioned concrete structural systems can lose cross-section due to corrosion, eventually leading to acoustic emission (AE) events when the stress exceeds the breaking strength of the wires that make up the tendons. Reliable differentiation of wire break AE events from traffic or grout crack events is critical for monitoring large structures, even where the distance between sensors may produce highly attenuated signals. In this paper, the Fuzzy c-means clustering algorithm was employed to differentiate AEs released from breaking wires of steel tendons from a database of 13464 AEs, including wire breaks, environmental and grout crack AEs. Wire breaks and grout crack AEs were collected from axial loading tests of grouted tendons in which the load increased until a wire broke. Environmental acoustic signals were collected from a bridge. Then all the collected AEs were gathered in a database and post-processed to simulate attenuation of up to 20 m from source to sensor. To optimize the speed and reliability of the Fuzzy c-means clustering algorithm, a non-dominated sorting genetic algorithm-II (NSGA-II) was used to find the minimum number of acoustic features needed. The NSGA-II algorithm started with 201 possible acoustic features and found 12 combinations of features that resulted in more than 80% wire break detection accuracy. In contrast, less than 3% of grout cracks and 0% of environmental signals were detected as wire breaks. The proposed method is suitable for deployment in a large sensor network and has sufficiently low-computational requirements for at-the-sensor processing, eliminating the need to send high-frequency sampled data outside the sensor node.

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Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

Event identification in acoustic emission from wire breaks in pre-stressing/post-tensioning cables

Mahmoudkhani,

Zhao,

Cochingco

et al. 2024

Eng. Res. Express

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“…Chen et al [ 10 ] proposed a method for identifying structural nonlinear damage based on ARMA model and vector space cosine similarity (VSCS), and verified the feasibility of the method through experimental studies, solving the nonlinear problems that traditional methods cannot effectively handle caused by structural damage. Zeng et al [ 11 ] advanced a time series model based on fuzzy c-means clustering algorithm, characterizing the degree of structural damage by the change of model coefficients, and verifying the feasibility and accuracy of the method through experimental and numerical studies. The method has low computational cost and is suitable for real-time monitoring of civil engineering.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Damage Identification Method for a Large-Span Timber Lattice Shell Structure in Taiyuan Botanical Garden based on Structural Health Monitoring

Wang

Zhang

et al. 2023

Sensors

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Large-span spatial lattice structures generally have characteristics such as incomplete modal information, high modal density, and high degrees of freedom. To address the problem of misjudgment in the damage detection of large-span spatial structures caused by these characteristics, this paper proposed a damage identification method based on time series models. Firstly, the order of the autoregressive moving average (ARMA) model was selected based on the Akaike information criterion (AIC). Then, the long autoregressive method was used to estimate the parameters of the ARMA model and extract the residual sequence of the autocorrelation part of the model. Furthermore, principal component analysis (PCA) was introduced to reduce the dimensionality of the model while retaining the characteristic values. Finally, the Mahalanobis distance (MD) was used to construct the damage sensitive feature (DSF). The dome of Taiyuan Botanical Garden in China is one of the largest non-triangular timber lattice shells worldwide. Relying on the structural health monitoring (SHM) project of this structure, this paper verified the effectiveness of the damage identification model through numerical simulation and determined the damage degree of the dome structure through SHM measurement data. The results demonstrated that the proposed damage identification method can effectively identify the damage of large-span timber lattice structures, locate the damage position, and estimate the degree of damage. The constructed DSF had relatively strong robustness to small damage and environmental noise and has practical application value for SHM in engineering.

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“…Once some key members are damaged severely, great losses would have been caused to the whole society. Therefore, it has always been a hot issue to get the damage conditions of existing bridges from the monitoring data timely and correctly (Entezami and Shariatmadar, 2018; Zeng et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Study of damage identification for bridges based on deep belief network

Guo

Feng

Zhang

et al. 2020

Advances in Structural Engineering

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To solve the problem of poor anti-noise ability faced by traditional pattern recognition methods in damage identification field, a bridge damage identification method based on deep belief network was proposed. Taken the modal curvature difference as the damage index, three restricted Boltzmann machines were constructed for pre-training. Then, the Softmax classifier and neural network were used to identify the damage location and degree under the environmental cases of no noise, weak noise, and strong noise, respectively. Subsequently, the influence of incomplete measurement modal data on the method was studied. Finally, damage identification based on deep belief network was implemented to a continuous beam bridge and compared with that of the back propagation neural network. The results showed that the proposed method could be highly effective not only on damage location but also on degree identification. Compared with back propagation neural network, deep belief network method may possess better identification ability and stronger anti-noise ability. It also demonstrates good identification effect under the condition of incomplete measurement modal data.

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Fuzzy clustering of time-series model to damage identification of structures

Cited by 10 publications

References 46 publications

Event identification in acoustic emission from wire breaks in pre-stressing/post-tensioning cables

Event identification in acoustic emission from wire breaks in pre-stressing/post-tensioning cables

Exploration of Damage Identification Method for a Large-Span Timber Lattice Shell Structure in Taiyuan Botanical Garden based on Structural Health Monitoring

Study of damage identification for bridges based on deep belief network

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