Effect of response type and excitation frequency range on the structural damage detection method using correlation functions of vibration responses

Research works on photogrammetry have received tremendous attention in the past few decades. One advantage of photogrammetry is that it can measure displacement and deformation of a structure in a fully non-contact, full-field manner. As a non-destructive evaluation method, photogrammetry can be used to detect structural damage by identifying local anomalies in measured deformation of a structure. Numerous methods have been proposed to measure deformations by tracking exterior features of structures, assuming that the features can be consistently identified and tracked on sequences of digital images captured by cameras. Such feature-tracking methods can fail if the features do not exist on captured images. One feasible solution to the potential failure is to artificially add exterior features to structures. However, painting and mounting such features can introduce unwanted permanent surficial modifications, mass loads, and stiffness changes to structures. In this article, a photogrammetry-based structural damage detection method is developed, where a visible laser line is projected to a surface of a structure, serving as an exterior feature to be tracked; the projected laser line is massless and its existence is temporary. A laser-line-tracking technique is proposed to track the projected laser line on captured digital images. Modal parameters of a target line corresponding to the projected laser line can be estimated by conducting experimental modal analysis. By identifying anomalies in curvature mode shapes of the target line and mapping the anomalies to the projected laser line, structural damage can be detected with identified positions and sizes. An experimental investigation of the damage detection method was conducted on a damaged beam. Modal parameters of a target line corresponding to a projected laser line were estimated, which compared well with those from a finite element model of the damaged beam. Experimental damage detection results were validated by numerical ones from the finite element model.

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Structural damage detection using the polynomial annihilation edge detection method

Australian Journal of Structural Engineering ◽

10.7158/s12-043.2014.15.1 ◽

2014 ◽

Vol 15 (1) ◽

Cited By ~ 5

Author(s):

C Surace ◽

G Yan ◽

R Archibald ◽

R Saxena ◽

R Feng

Keyword(s):

Edge Detection ◽

Damage Detection ◽

Structural Damage ◽

Detection Method ◽

Structural Damage Detection ◽

Polynomial Annihilation ◽

Edge Detection Method

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Experimental validation of a structural damage detection method based on marginal Hilbert spectrum

10.1117/12.2260298 ◽

2017 ◽

Author(s):

Srishti Banerji ◽

Timir B. Roy ◽

Ardalan Sabamehr ◽

Ashutosh Bagchi

Keyword(s):

Damage Detection ◽

Structural Damage ◽

Experimental Validation ◽

Detection Method ◽

Structural Damage Detection ◽

Hilbert Spectrum

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Structural damage detection via combining weighted strategy with trace Lasso

Advances in Structural Engineering ◽

10.1177/1369433218795310 ◽

2018 ◽

Vol 22 (3) ◽

pp. 597-612 ◽

Cited By ~ 3

Author(s):

Chengbin Chen ◽

Chudong Pan ◽

Zepeng Chen ◽

Ling Yu

Keyword(s):

Damage Detection ◽

Swarm Intelligence ◽

Structural Damage ◽

Optimization Problem ◽

Detection Method ◽

Rapid Development ◽

Detection Methods ◽

Detection Accuracy ◽

Structural Damage Detection ◽

Selection Operator

With the rapid development of computation technologies, swarm intelligence–based algorithms become an innovative technique used for addressing structural damage detection issues, but traditional swarm intelligence–based structural damage detection methods often face with insufficient detection accuracy and lower robustness to noise. As an exploring attempt, a novel structural damage detection method is proposed to tackle the above deficiency via combining weighted strategy with trace least absolute shrinkage and selection operator (Lasso). First, an objective function is defined for the structural damage detection optimization problem by using structural modal parameters; a weighted strategy and the trace Lasso are also involved into the objection function. A novel antlion optimizer algorithm is then employed as a solution solver to the structural damage detection optimization problem. To assess the capability of the proposed structural damage detection method, two numerical simulations and a series of laboratory experiments are performed, and a comparative study on effects of different parameters, such as weighted coefficients, regularization parameters and damage patterns, on the proposed structural damage detection methods are also carried out. Illustrated results show that the proposed structural damage detection method via combining weighted strategy with trace Lasso is able to accurately locate structural damages and quantify damage severities of structures.

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

Structural Health Monitoring ◽

10.1177/1475921720942836 ◽

2020 ◽

pp. 147592172094283 ◽

Cited By ~ 1

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.

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