• Smart Structures and Systems
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• v.14 no.3
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• pp.307-325
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• 2014

A newly developed method based on energy is presented to study the damage pattern of FRP material. Basalt fiber reinforced polymer (BFRP) is employed to monitor the damage under fatigue loading. In this study, acoustic emission technique (AE) combined with scanning electronic microscope (SEM) technique is employed to monitor the damage evolution of the BFRP specimen in an approximate continuous scanning way. The AE signals are analyzed based on the wavelet transform, and the analyses are confirmed by SEM images. Several damage patterns of BFRP material, such as matrix cracking, delamination, fiber fracture and their combinations, are identified through the experiment. According to the results, the cumulative energy (obtained from wavelet coefficients) of various damage patterns are closely related to the damage evolution of the BFRP specimens during the entire fatigue tests. It has been found that the proposed technique can effectively distinguish different damage patterns of FRP materials and describe the fatigue damage evolution.

• Advanced Composite Materials
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• v.16 no.2
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• pp.115-134
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• 2007

This study proposes two new approaches for identifying damage patterns in a holed CFRP cross-ply laminate using an embedded fiber Bragg grating (FBG) sensor. It was experimentally confirmed that the reflection spectrum from the embedded FBG sensor was significantly deformed as the damage near the hole (i.e. splits, transverse cracks and delamination) extended. The damage patterns were predicted using forward analysis (a damage analysis and an optical analysis) with strain estimation and the proposed damage-identification method as well as the forward analysis only. Forward analysis with strain estimation provided the most accurate damage-pattern estimation and the highest computational efficiency. Furthermore, the proposed damage identification significantly reduced computation time with the equivalent accuracy compared to the conventional identification procedure, by using damage analysis as the initial estimation.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.173-190
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• 2019

Steel-confined reinforced concrete (SCRC) columns feature highly complex and invisible mechanisms that make damage evaluation and pattern recognition difficult. In the present article, the prevailing acoustic emission (AE) technique was applied to monitor and evaluate the damage process of steel-confined RC columns in a quasi-static test. AE energy-based indicators, such as index of damage and relax ratio, were proposed to trace the damage progress and quantitatively evaluate the damage state. The fuzzy C-means algorithm successfully discriminated the AE data of different patterns, validity analysis guaranteed cluster accuracy, and principal component analysis simplified the datasets. A detailed statistical investigation on typical AE features was conducted to relate the clustered AE signals to micro mechanisms and the observed damage patterns, and differences between steel-confined and unconfined RC columns were compared and illustrated.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.357-365
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• 2003

The major problems with the conventional neural network, especially Back Propagation Neural Network, arise from the necessity of many training data for neural network learning and ambiguity in the relation of neural network structure to the convergence of solution. In this paper, the PNN is used as a pattern classifier to detect the damage of structure to avoid those drawbacks of the conventional neural network. In the PNN-based pattern classification problems, the probability density function for patterns is usually assumed by Gaussian distribution. But, in this paper, several probability density functions are investigated in order to select the most approriate one for structural damage assessment.

• Journal of Korean Institute of Fire Investigation
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• v.11 no.1
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• pp.37-40
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• 2008

This paper deals with damage patterns of cabinet panel for low voltage deteriorated by flame. In order to analyze damage patterns, we used Metallurgical Microscope, x-ray system, and Fourier Transform Infrared spectroscopy. Firstly, Metallurgical microscope was used for analysis of electrical causes, such as electric short and overload Secondly, X-ray system was used for analysis of internal characteristics of circuit breakers. Lastly, Fourier Transform Infrared spectroscopy was used for analysis of damage direction by flame.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.04a
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• pp.269-272
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• 2008

This paper deals with damage patterns of cabinet panel for low voltage deteriorated by flame. In order to analyze damage patterns, we used Metallurgical Microscope, x-ray system, and Fourier Transform Infrared spectroscopy. Firstly, Metallurgical microscope was used for analysis of electrical causes, such as electric short and overload. Secondly, X-ray system was used for analysis of internal characteristics of circuit breakers. Lastly, Fourier Transform Infrared spectroscopy was used for analysis of damage direction by flame. The following results were obtained.

• Structural Monitoring and Maintenance
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• v.5 no.4
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• pp.507-519
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• 2018

In this paper, damage assessment in wind-turbine towers using vibration-based artificial neural networks (ANNs) is numerically investigated. At first, a vibration-based ANNs algorithm is designed for damage detection in a wind turbine tower. The ANNs architecture consists of an input, an output, and hidden layers. Modal parameters of the wind turbine tower such as mode shapes and frequencies are utilized as the input and the output layer composes of element stiffness indices. Next, the finite element model of a real wind-turbine tower is established as the test structure. The natural frequencies and mode shapes of the test structure are computed under various damage cases of single and multiple damages to generate training patterns. Finally, the ANNs are trained using the generated training patterns and employed to detect damaged elements and severities in the test structure.

• Smart Structures and Systems
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• v.4 no.5
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• pp.583-603
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• 2008

In this study, a real-time damage detection method using output-only acceleration signals and artificial neural networks (ANN) is developed to monitor the occurrence of damage and the location of damage in structures. A theoretical approach of an ANN algorithm that uses acceleration signals to detect changes in structural parameters in real-time is newly designed. Cross-covariance functions of two acceleration responses measured before and after damage at two different sensor locations are selected as the features representing the structural conditions. By means of the acceleration features, multiple neural networks are trained for a series of potential loading patterns and damage scenarios of the target structure for which its actual loading history and structural conditions are unknown. The feasibility of the proposed method is evaluated using a numerical beam model under the effect of model uncertainty due to the variability of impulse excitation patterns used for training neural networks. The practicality of the method is also evaluated from laboratory-model tests on free-free beams for which acceleration responses were measured for several damage cases.

• Toxicological Research
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• v.34 no.3
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• pp.199-210
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• 2018

Skeletal muscle can be ultrastructurally damaged by eccentric exercise, and the damage causes metabolic disruption in muscle. This study aimed to determine changes in the metabolomic patterns in urine and metabolomic markers in muscle damage after eccentric exercise. Five men and 6 women aged 19~23 years performed 30 min of the bench step exercise at 70 steps per min at a determined step height of 110% of the lower leg length, and stepping frequency at 15 cycles per min. $^1H$ NMR spectral analysis was performed in urine collected from all participants before and after eccentric exercise-induced muscle damage conventionally determined using a visual analogue scale (VAS) and maximal voluntary contraction (MVC). Urinary metabolic profiles were built by multivariate analysis of principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA) using SIMCA-P. From the OPLS-DA, men and women were separated 2 hr after the eccentric exercise and the separated patterns were maintained or clarified until 96 hr after the eccentric exercise. Subsequently, urinary metabolic profiles showed distinct trajectory patterns between men and women. Finally, we found increased urinary metabolites (men: alanine, asparagine, citrate, creatine phosphate, ethanol, formate, glucose, glycine, histidine, and lactate; women: adenine) after the eccentric exercise. These results could contribute to understanding metabolic responses following eccentric exercise-induced muscle damage in humans.

• Smart Structures and Systems
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• v.13 no.5
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• pp.869-890
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• 2014

In this study, a damage detection approach using train-induced vibration response of the bridge is proposed, utilizing only direct structural analysis by means of introducing soft computing methods. In this approach, the possible damage patterns of the bridge are assumed according to theoretical and empirical considerations at first. Then, the running train-induced dynamic response of the bridge under a certain damage pattern is calculated employing a developed train-bridge interaction analysis program. When the calculated result is most identical to the recorded response, this damage pattern will be the solution. However, owing to the huge number of possible damage patterns, it is extremely time-consuming to calculate the bridge responses of all the cases and thus difficult to identify the exact solution quickly. Therefore, the soft computing methods are introduced to quickly solve the problem in this approach. The basic concept and process of the proposed approach are presented in this paper, and its feasibility is numerically investigated using two different train models and a simple girder bridge model.