Statistical methods for damage detection applied to civil structures

Greś, Szymon; Ulriksen, Martin Dalgaard; Döhler, Michael; Johansen, Rasmus Johan; Andersen, Palle; Damkilde, Lars; Nielsen, Søren Nors

doi:10.1016/j.proeng.2017.09.280

Cited by 29 publications

(15 citation statements)

References 12 publications

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“…The results show the better performance of our proposed algorithm of "GMM/SA-SVM classification" than another algorithm. Our achieved results are confirmed by the results achieved by the reference paper we used for SA-SVM [9]. Both the reference paper and our results show a similar pattern in performance of SVM and SA-SVM.…”

Section: Comparing the Results Of "Feature Extraction And Sa-svm Classupporting

confidence: 88%

Statistical Features and Traditional SA-SVM Classification Algorithm for Crack Detection

Hoshyar¹,

Kharkovsky²,

Samali³

2018

JSIP

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In recent years, the interest in damage identification of structural components through innovative techniques has grown significantly. Damage identification has always been a crucial concern in quality assessment and load capacity rating of infrastructure. In this regard, researchers focus on proposing efficient tools to identify the damages in early stages to prevent the sudden failure in structural components, ensuring the public safety and reducing the asset management costs. The sensing technologies along with the data analysis through various techniques and machine learning approaches have been the area of interest for these innovative techniques. The purpose of this research is to develop a robust method for automatic condition assessment of real-life concrete structures for the detection of relatively small cracks at early stages. A damage identification algorithm is proposed using the hybrid approaches to analyze the sensors data. The data obtained from transducers mounted on concrete beams under static loading in laboratory. These data are used as the input parameters. The method relies only on the measured time responses. After filtering and normalization of the data, the damage sensitive statistical features are extracted from the signals and used as the inputs of Self-Advising Support Vector Machine (SA-SVM) for the classification purpose in civil Engineering area. Finally, the results are compared with traditional methods to investigate the feasibility of the hybrid proposed algorithm. It is demonstrated that the presented method can reliably detect the crack in the structure and thereby enable the real-time infrastructure health monitoring.

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Section: Comparing the Results Of "Feature Extraction And Sa-svm Classupporting

confidence: 88%

Statistical Features and Traditional SA-SVM Classification Algorithm for Crack Detection

Hoshyar¹,

Kharkovsky²,

Samali³

2018

JSIP

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show abstract

“…Then, the problem of finding the optimal decomposition layer of CVMD can be transformed into the order of effective singular values searching. The steps are as follows: Constructing the Hank matrix

[ 27 ] by using the signal

. …”

Section: Adaptive Complex Variational Mode Decompositionmentioning

confidence: 99%

Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications

Xia¹,

Yang²,

Cai³

et al. 2021

Sensors

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In order to suppress the strong clutter component and separate the effective fretting component from narrow-band radar echo, a method based on complex variational mode decomposition (CVMD) is proposed in this paper. The CVMD is extended from variational mode decomposition (VMD), which is a recently introduced technique for adaptive signal decomposition, limited to only dealing with the real signal. Thus, the VMD is extended from the real domain to the complex domain firstly. Then, the optimal effective order of singular value is obtained by singular value decomposition (SVD) to solve the problem of under-decomposition or over-decomposition caused by unreasonable choice of decomposition layer, it is more accurate than detrended fluctuation analysis (DFA) and empirical mode decomposition (EMD). Finally, the strongly correlated modes and weakly correlated modes are judged by calculating the Mahalanobis distance between the band-limited intrinsic mode functions (BLIMFs) and the original signal, which is more robust than the correlation judgment methods such as computing cross-correlation, Euclidean distance, Bhattachryya distance and Hausdorff distance. After the weak correlation modes are eliminated, the signal is reconstructed locally, and the separation of the micro-motion signal is realized. The experimental results show that the proposed method can filter out the strong clutter component and the fuselage component from radar echo more effectively than the local mean decomposition (LMD), empirical mode decomposition and moving target indicator (MTI) filter.

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“…One of traditional damage location detection algorithms is a model-based iteration process, which is based on some known impact locations, using system modelling and revising coefficient to obtain closer impact locations [21], [22]. The other algorithms are often used by means of computer artificial intelligence algorithm, such as, genetic algorithms, pattern recognition, fuzzy control etc., to simulate extremely complex relationships between input and output data [23] and learn to find final object damage location.…”

Section: A Classical Triangulation Procedures For Damage Locationmentioning

confidence: 99%

Design Concept of Polar-Class Propeller Ice Loading Measurements Using FBGs

Xu¹,

Liu²,

Zhao³

et al. 2020

IJMMM

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Propeller is one of the most important pieces of equipment for polar (Arctic and Antarctic) explorations and transportation, including ice breaking voyage. This paper developed a couple of design concepts to monitor the health of the blade structure to detect the ultimate ice loading as well to obtain measured experimental data from dynamics loading, for CFRP polar-class propellers by using imbedded FBGs. The main purpose of this study is to validate the polar-class propeller design software Rotorysics (formerly Propella), the ultimate ice load theory and the URI3 of Polar-Class Propeller Rules by the International Association of Classification Societies (IACS). These conceptual designs should be also applicable for aircraft propellers, wind and tidal turbines, especially impact loading via charpy testing. Index Terms-Arctic and Antarctic navigation safety, Carbon fiber reinforced polymer (CFRP), fiber Bragg Grating (FBG), polar class propeller. I. INTRODUCTION

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Statistical methods for damage detection applied to civil structures

Cited by 29 publications

References 12 publications

Statistical Features and Traditional SA-SVM Classification Algorithm for Crack Detection

Statistical Features and Traditional SA-SVM Classification Algorithm for Crack Detection

Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications

Design Concept of Polar-Class Propeller Ice Loading Measurements Using FBGs

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