A Shannon Entropy-Based Methodology to Detect and Locate Cables Loss in a Cable-Stayed Bridge

2021 ◽  
Vol 13 (05) ◽  
Author(s):  
Jose M. Machorro-Lopez ◽  
Martin Valtierra-Rodriguez ◽  
Juan P. Amezquita-Sanchez ◽  
Francisco J. Carrion-Viramontes ◽  
Juan A. Quintana-Rodriguez
Keyword(s):  
Fuzzy Logic ◽  
Damage Detection ◽  
Shannon Entropy ◽  
Structural Damage ◽  
Recognition Algorithm ◽  
Economic Losses ◽  
Entropy Index ◽  
Vibration Data ◽  
Cable Stayed Bridge ◽  
Cable Stayed Bridges

As with any civil structure or mechanism, vehicular bridges can suffer structural damages which can conduct to devastating human and economic losses if they are not detected and corrected on time. In this work, a methodology based on the Shannon entropy index combined with statistical indexes and a fuzzy logic classifier to detect and locate a cable loss in cable-stayed bridges is proposed. Shannon entropy index is used to characterize the changes in the vibration signals associated with structural damage, which are integrated with statistical indexes for damage detection and damage location. On the other hand, the fuzzy logic classifier is used as a pattern recognition algorithm to detect structural damage automatically. For this study, the vibration data acquired experimentally from the Rio Papaloapan Bridge (Veracruz, Mexico) are analyzed. Results demonstrate the usefulness of the proposed method since 93.3% of effectiveness in the damage detection is obtained with a 100% of effectiveness in its location.

VIBRATION-BASED STRUCTURAL DAMAGE DETECTION UNDER VARYING TEMPERATURE CONDITIONS

2013 ◽  
Vol 13 (05) ◽  
pp. 1250082 ◽  
Author(s):  
XIAO-QING ZHOU ◽  
WEN HUANG
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Environmental Changes ◽  
Model Updating ◽  
Modal Testing ◽  
Structural Damage Detection ◽  
Vibration Data ◽  
Temperature Conditions ◽  
Vibration Properties

In vibration-based structural damage detection, it is necessary to discriminate the variation of structural properties due to environmental changes from those caused by structural damages. The present paper aims to investigate the temperature effect on vibration-based structural damage detection in which the vibration data are measured under varying temperature conditions. A simply-supported slab was tested in laboratory to extract the vibration properties with modal testing. The slab was then damaged and the modal testing was conducted again, in which the temperature varied. The modal data measured under different temperature conditions were used to detect the damage with a two-stage model updating technique. Some damage was falsely detected if the temperature variation was not considered. Natural frequencies were then corrected to those under the same temperature conditions according to the relation between the temperature and material modulus. It is shown that all of the damaged elements can be accurately identified.

L1 Regularized Model Updating for Structural Damage Detection

2018 ◽  
Vol 18 (12) ◽  
pp. 1850157 ◽  
Author(s):  
Yu-Han Wu ◽  
Xiao-Qing Zhou
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Regularization Parameter ◽  
Structural Vibration ◽  
Mode Shapes ◽  
Modal Data ◽  
Vibration Data ◽  
Stiffness Reduction ◽  
Regularized Model

Model updating methods based on structural vibration data have been developed and applied to detecting structural damages in civil engineering. Compared with the large number of elements in the entire structure of interest, the number of damaged elements which are represented by the stiffness reduction is usually small. However, the widely used [Formula: see text] regularized model updating is unable to detect the sparse feature of the damage in a structure. In this paper, the [Formula: see text] regularized model updating based on the sparse recovery theory is developed to detect structural damage. Two different criteria are considered, namely, the frequencies and the combination of frequencies and mode shapes. In addition, a one-step model updating approach is used in which the measured modal data before and after the occurrence of damage will be compared directly and an accurate analytical model is not needed. A selection method for the [Formula: see text] regularization parameter is also developed. An experimental cantilever beam is used to demonstrate the effectiveness of the proposed method. The results show that the [Formula: see text] regularization approach can be successfully used to detect the sparse damaged elements using the first six modal data, whereas the [Formula: see text] counterpart cannot. The influence of the measurement quantity on the damage detection results is also studied.

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

2020 ◽  
pp. 147592172094283 ◽  
Author(s):  
Zhiqiang Shang ◽  
Limin Sun ◽  
Ye Xia ◽  
Wei Zhang
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Deep Neural Networks ◽  
Beam Model ◽  
Structural Damage Detection ◽  
Denoising Autoencoder ◽  
Vibration Data

One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to obtain effective damage features from raw vibration data. However, most of the available deep neural networks are supervised, resulting in practical difficulties owing to the lack of damage labels. This article proposes a damage detection strategy based on an unsupervised deep neural network, referred to as deep convolutional denoising autoencoder, which accepts multi-dimensional cross-correlation functions as input. The strategy aims to extract damage features from field measurements of undamaged structures under the influence of noise and temperature uncertainties. In the proposed strategy, cross-correlation functions of vibration data are first calculated as basic features; then deep convolutional denoising autoencoder is developed to reconstruct cross-correlation functions from their noise-corrupted versions to extract desired features; exponentially weighted moving average control charts are finally established for these features to identify minor structural damages. The strategy is evaluated through a numerical simply supported beam model and an experimental continuous beam model. The mechanism of deep convolutional denoising autoencoder to extract damage features is interpreted by visualizing feature maps of convolutional layers in the encoder. It is found that these layers perform rough estimations of modal properties and preserve the damage information as the general trend of these properties in multiple extra frequency bands. The results show that the proposed strategy is competent for structural damage detection under the exposed environment and worth further exploring its capabilities in applications of real bridges.

scholarly journals Boundary Strategy for Optimization-based Structural Damage Detection Problem using Metaheuristic Algorithms

2020 ◽  
Author(s):  
Ali Kaveh ◽  
Seyed Milad Hosseini ◽  
Ataollah Zaerreza
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Optimization Method ◽  
Metaheuristic Algorithms ◽  
Structural Elements ◽  
Detection Problem ◽  
Vibration Data ◽  
New Strategy ◽  
Optimum Solution ◽  
Better Than

The present paper proposes a new strategy namely Boundary Strategy (BS) in the process of optimization-based damage detection using metaheuristic algorithms. This strategy gradually neutralizes the effects of structural elements that are healthy in the optimization process. BS causes the optimization method to find the optimum solution better than conventional methods that do not use the proposed BS. This technique improves both aspects of the accuracy and convergence speed of the algorithms in identifying and quantifying the damage. To evaluate the performance of the developed strategy, a new damage-sensitive cost function, which is defined based on vibration data of the structure, is optimized utilizing the Shuffled Shepherd Optimization Algorithm (SSOA). Different examples including truss, beam, and frame are investigated numerically in order to indicate the applicability of the proposed technique. The proposed approach is also applied to other well-known optimization algorithms including TLBO, GWO, and MFO. The obtained results illustrate that the proposed method improves the performance of the utilized algorithms in identifying and quantifying of the damaged elements, even for noise-contaminated data.

A Method Based on Probability Analysis for Structural Damage Early Warning

2014 ◽  
Vol 1079-1080 ◽  
pp. 386-389
Author(s):  
Yan Sheng Song ◽  
Wei Ning Ni ◽  
Zi Jun Li
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Natural Frequency ◽  
Health Monitoring ◽  
Early Warning ◽  
Structural Damage ◽  
Probability Analysis ◽  
Cable Stayed Bridge ◽  
Frequency Peak ◽  
Degree Of Damage

Damage early warning is the first stage of strucutural health monitoring and damage detection. Abnormal offset of natural frequency can reflect structure in some degree of damage. Based on the probability analysis of frequency peak offset, this paper presents a parameter and corresponding method for structural damage early warning. With this method, it achieves damage early warning for a cable stayed bridge of structural health monitoring.

scholarly journals Damage detection of beam bridge under a moving load using Auto-encoder

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Juntao Wu ◽  
Zhenhua Nie
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Structural Damage ◽  
Moving Load ◽  
Damage Index ◽  
Structural Vibration ◽  
Vibration Data ◽  
Vibration Responses ◽  
Hidden Layer ◽  
The Time Domain

A novel damage detection approach based on Auto-encoder neural network is proposed to identify damage in beam-like bridges subjected to a moving mass. In this approach, several sensors are used to measure structural vibration responses during a mass moving across the bridge. An auto-encoder (AE) neural network is designed to extract features from the measured responses. A fixed moving window is used to cut out the time-domain responses to generate inputs of the AE neural network. Moreover, some constraints are applied on the hidden layer to improve the performance of the AE network in training process. When the training is complete, the encoder was regarded as a feature extractor. And the damage index is defined as the cosine distance between two feature vectors obtained from adjacent data windows. By moving the window along the measured vibration data, we can calculate a damage index series and locate the damage position of the structure. To demonstrate the performance of the proposed method, numerical simulation is carried out. The results show that the proposed method can accurately locate both single and multiple damages using acceleration response. It infers the proposed method is promising for structural damage detection.

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