• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.608-613
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• 2003

In this paper, the variability of modal properties caused by temperature effects is assessed to adjust modal data used for frequency-based damage detection in plate-girder bridges. First, experiments on model plate-girder bridges are described. Next, the relationship between temperature and natural frequencies is assessed and a set of empirical frequency-correction formula are analyzed for the test structure. Finally, a frequency-eased method is used to locate and estimate severity of damage in the test structure using experimental modal data which are adjusted by the frequency-correction formula. Here, local damage in beam-type structures is detected by using measured frequencies and analytical mode shapes.

• Journal of Ocean Engineering and Technology
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• v.30 no.4
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• pp.287-293
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• 2016

This paper presents an effective damage detection method for offshore jackets using natural frequency change ratios. Two parameters, cosine similarity and magnitude index, are considered to estimate the location and severity of the damage in the structure. A numerical jacket structure model is considered to verify the performance of the proposed method. As observed through analysis, the damages in the structure are detected accurately.

• Smart Structures and Systems
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• v.23 no.1
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• pp.15-29
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• 2019

This paper addresses the problem of damage detection in suspension bridge hangers, with an emphasis on the modal flexibility method. It aims at evaluating the capability and the accuracy of the modal flexibility method to detect and locate single and multiple damages in suspension bridge hangers, with different level of severity and various locations. The study is conducted numerically and experimentally on a laboratory suspension bridge mock-up. First, the covariance-driven stochastic subspace identification is used to extract the modal parameters of the bridge from experimental data, using only output measurements data from ambient vibration. Then, the method is demonstrated for several damage scenarios and compared against other classical methods, such as: Coordinate Modal Assurance Criterion (COMAC), Enhanced Coordinate Modal Assurance Criterion (ECOMAC), Mode Shape Curvature (MSC) and Modal Strain Energy (MSE). The paper demonstrates the relative merits and shortcomings of these methods which play a significant role in the damage detection ofsuspension bridges.

• Structural Engineering and Mechanics
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• v.30 no.4
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• pp.445-466
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• 2008

This paper presents a novel structural damage detection method with a new damage index based on the statistical moments of dynamic responses of a structure under a random excitation. After a brief introduction to statistical moment theory, the principle of the new method is put forward in terms of a single-degree-of-freedom (SDOF) system. The sensitivity of statistical moment to structural damage is discussed for various types of structural responses and different orders of statistical moment. The formulae for statistical moment-based damage detection are derived. The effect of measurement noise on damage detection is ascertained. The new damage index and the proposed statistical moment-based damage detection method are then extended to multi-degree-of-freedom (MDOF) systems with resort to the leastsquares method. As numerical studies, the proposed method is applied to both single and multi-story shear buildings. Numerical results show that the fourth-order statistical moment of story drifts is a more sensitive indicator to structural stiffness reduction than the natural frequencies, the second order moment of story drift, and the fourth-order moments of velocity and acceleration responses of the shear building. The fourth-order statistical moment of story drifts can be used to accurately identify both location and severity of structural stiffness reduction of the shear building. Furthermore, a significant advantage of the proposed damage detection method lies in that it is insensitive to measurement noise.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1341-1350
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• 2019

This research deals with algorithm for forest fire severity classification using multi-temporal KOMPSAT-3A image to mapping forest fire areas. The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution and multi-spectral imagery with infrared and high resolution electro-optical bands. However, there is a lack of research to classify forest fire severity using KOMPSAT-3A. Therefore, the purpose of this study is to analyze forest fire severity using KOMPSAT-3A images. In addition, this research used pre-fire and post-fire Sentinel-2 with differenced Normalized Burn Ratio (dNBR) to taking for burn severity distribution map. To test the effectiveness of the proposed procedure on April 4, 2019, Gangneung wildfires were considered as a case study. This research used the probability density function for the classification of forest fire damage severity based on R software, a free software environment of statistical computing and graphics. The burn severities were estimated by changing NDVI before and after forest fire. Furthermore, standard deviation of probability density function was used to calculate the size of each class interval. A total of five distribution of forest fire severity were effectively classified.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.243-260
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• 2018

Detection of damages in fibre reinforced plastic (FRP) composite structures is important from the safety and serviceability point of view. Usually, damage is realized as a local reduction of stiffness and if dynamic responses of the structure are sensitive enough to such changes in stiffness, then a well posed inverse problem can provide an efficient solution to the damage detection problem. Usually, such inverse problems are solved within the framework of pattern recognition. Support Vector Machine (SVM) Algorithm is one such methodology, which minimizes the weighted differences between the experimentally observed dynamic responses and those computed using the finite element model- by optimizing appropriately chosen parameters, such as stiffness. A damage detection strategy is hereby proposed using SVM which perform stepwise by first locating and then determining the severity of the damage. The SVM algorithm uses simulations of only a limited number of damage scenarios and trains the algorithm in such a way so as to detect damages at unknown locations by recognizing the pattern of changes in dynamic responses. A rectangular fiber reinforced plastic composite plate has been investigated both numerically and experimentally to observe the efficiency of the SVM algorithm for damage detection. Experimentally determined modal responses, such as natural frequencies and mode shapes are used as observable parameters. The results are encouraging since a high percentage of damage cases have been successfully determined using the proposed algorithm.

• Structural Engineering and Mechanics
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• v.24 no.6
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• pp.665-682
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• 2006

A vibration-based nondestructive damage evaluation technique for a curved thin beam is introduced. The proposed method is capable of detecting, locating, and sizing structural damage simultaneously by using a few of the lower natural frequencies and their corresponding mode shapes before and after a small damage event. The proposed approach utilizes modal flexibilities reconstructed from measured modal parameters. A rigorous system of equations governing damage and curvature of modal flexibility is derived in the context of elasticity. To solve the resulting system of governing equations, an efficient pseudo-inverse technique is introduced. The direct inspection of the resulting solutions provides the location and severity of damage in a curved thin beam. This study confirms that there is a strong linear relationship between the curvature of modal flexibility and flexural damage in the selected class of structures. Several numerical case studies are provided to justify the performance of the proposed approach. The proposed method introduces a way to avoid the singularity and mode selection problems from earlier attempts.

• Structural Engineering and Mechanics
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• v.29 no.3
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• pp.311-325
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• 2008

Noting that damage occurrence of offshore jacket platforms is concentrated in two structural regions that are in the vicinity of still water surface and close to the seabed, a damage detection method by using only partial measurement of vibration in a suspect region was presented in this paper, which can not only locate damaged members but also evaluate damage severities. Then employing an experiment platform model under white-noise ground excitation by shaking table and using modal parameters of the first three modes identified by a scalar-type ARMA method on undamaged and damaged structures, the feasibility of the damage detection method was discussed. Modal parameters from eigenvalue analysis on the structural FEM model were also used to help the discussions. It is demonstrated that the damage detection algorithm is feasible on damage location and severity evaluation for broken slanted braces and it is robust against the errors of baseline FEM model to real structure when the principal errors is formed by difference of modal frequencies. It is also found that Z-value changes of modal shapes also play a role in the precise detection of damage.

• Korean Journal of Cognitive Science
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• v.32 no.4
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• pp.169-191
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• 2021

With the development of artificial intelligence(AI), the jobs of many human experts are threatened, and this also applies to the legal profession. This study attempted to investigate whether AI can actually replace humans in the legal profession, especially the role of judges making final judgments. For this purpose, from the perspective of uniqueness neglect, this study was conducted to confirm the effect of involvement and the severity on acceptance of the judgment made by the AI judge (Experiment 1) and the AI jury (Experiment 2). The involvement was manipulated as if the subject who was sentenced for committing a crime was his or her family (mother, father) or stranger, and the severity was manipulated by the extent of the damage, the perception of the crime, and the number of applied crimes. In Experiment 1, the interactive effect of involvement and severity was found. Specifically, when the involvement was low, the acceptance of AI judges was higher in high severity (vs. low severity). Conversely, when the involvement was high, the acceptance of AI judges was higher in low severity (vs. high severity). The same interactions as in Experiment 1 occurred in Experiment 2. Specifically, when the involvement was low, a larger number of AI jury members were allocated in high severity (vs. low severity). On the other hand, when the involvement was high, the number of AI juries increased in low severity (vs. high severity). This study has implications in that it is the first experimental study in Korea on artificial intelligence legal judgment and that it presents the prospects for the jobs of legal experts.

• Fire Science and Engineering
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• v.12 no.1
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• pp.15-21
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• 1998

In this research, we studied reach the conclusion with more probability for predicting the severity which based on fire cases in domestic department stores for last 30 years. Considering the number of yearly fire cases in department stores and the cost of damage, we set the risk level. Moreover, this research shows the severity of fire in department stores through its scenario applying to FPETOOL program which NIST in USA has developed. By the result of FPETOOL program operation, we could acquired information about the time reaching the point where people are in danger in temperature, smoke layer and gas concentration. When a fire breaks out in a department store, a great loss of property and life is significant, as well as the potential risk is awfully considerable. Therefore, we should prevent a five from occuring.