• Structural Engineering and Mechanics
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• v.46 no.1
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• pp.75-90
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• 2013

In order to identify damage of highway bridges rapidly, a method for damage identification using dynamic response of bridge induced by moving vehicle and static test data is proposed. To locate damage of the structure, displacement energy damage index defined from the energy of the displacement response time history is adopted as the indicator. The displacement response time histories of bridge structure are obtained from simulation of vehicle-bridge coupled vibration analysis. The vehicle model is considered as a four-degree-of-freedom system, and the vibration equations of the vehicle model are deduced based on the D'Alembert principle. Finite element method is used to discretize bridge and finite element model is set up. According to the condition of displacement and force compatibility between vehicle and bridge, the vibration equations of the vehicle and bridge models are coupled. A Newmark-${\beta}$ algorithm based professional procedure VBAP is developed in MATLAB, and used to analyze the vehicle-bridge system coupled vibration. After damage is located by employing the displacement energy damage index, the damage extent is estimated through the least-square-method based model updating using static test data. At last, taking one simply supported bridge as an illustrative example, some damage scenarios are identified using the proposed damage identification methodology. The results indicate that the proposed method is efficient for damage localization and damage extent estimation.

• Smart Structures and Systems
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• v.21 no.5
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• pp.677-686
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• 2018

The structural responses are often used to identify the structural local damages. However, it is usually difficult to gain the responses of the track, as the sensors cannot be installed on the track directly. The vehicles running on a track excite track vibration and can also serve as response receivers because the vehicle dynamic response contains the vibration information of the track. A damage identification method using the vehicle responses and sensitivity analysis is proposed for the vehicle-track coupling system in this paper. Different from most damage identification methods of vehicle-track coupling system, which require the structural responses, only the vehicle responses are required in the proposed method. The local damages are identified by a sensitivity-based model updating process. In the vehicle-track coupling system, the track is modeled as a discrete point supported Euler-Bernoulli beam, and two vehicle models are proposed to investigate the accuracy and efficiency of damage identification. The measured track irregularity is considered in the calculation of vehicle dynamic responses. The measurement noises are also considered to study their effects to the damage identification results. The identified results demonstrate that the proposed method is capable to identify the local damages of the track accurately in different noise levels with only the vehicle responses.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.225-231
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• 2016

A military vehicle is driven at different usage modes with the army application and servicing conditions. For practical durability validation, DT(Development Test) on a new military vehicle should be run up to the durability target kilometers on test courses in the specified proving ground. Driving velocities with test courses at the endurance mode of DT are established definitely. However, OT(Operational Test) and initial endurance test of production car can't be performed only in the DT courses due to the development period limit. Therefore, this paper focuses on the method to analyze the relative road damages between the endurance test in DT and other endurance test. Road load acquisition tests on KLTV(Korean Light Tactical Vehicle) were implemented at 15 driving modes in off-road and cross-country courses of two tests. Wheel accelerations were processed through band-pass filter, and then the main frequency and maximum power of the signals were computed by PSD analysis. Finally, using the proving ground optimization based on RDS(Relative Damage Spectrum) characterization, the damage factors between off-roads of test courses were determined.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.1
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• pp.107-112
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• 2013

Base on insurance vehicle collision and bodily injury claim reports, 23,655 cases of vehicle to vehicle accidents occurred in Korea 2010 are investigated in order to understand vehicle damage severities, repair costs and occupant injury types. The results of our statistical analysis reveal that minor damages with small dent or scratches on vehicle body panels which is assumed to imply during very low speed crashes are major portion of accident severities types. The most vulnerable body regions due to the real-world accident are neck. The 86.3% of total injured driver in minor rear damaged vehicles has reported neck pains and they are followed by whole bodies and head but with much lower occurrence rates.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.191-197
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• 2000

This paper provides the test results of tracked vehicle at each driving condition and life cycle. Fatigue Damage Spectrum(FDS) has evaluated with the Power Spectrum Density(PSD) and the life time of equipment. Finally, provisional vibration qualification test level is evaluated.

• Journal of the Korean Society of Safety
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• v.30 no.4
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• pp.32-38
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• 2015

In this paper, we analyzed the vehicle fire accidents due to damage of vehicle's electrical components, which is applied to a vehicle. In recent development of electrical components technology, approximately 40% of vehicle manufacturing parts have applied electronic circuit technology. Phenomenon such deterioration of insulating performance or electric breakdown on the vehicle's electrical components and printed circuit boards(PCBs) resulted from moisture, contamination and aging due to repetitive operations, lead to the vehicle fire. Therefore, the application of electrical components with adequate electric capacity for vehicle and usage of molding techniques using a non-combustible materials to shut off the oxygen should be applied in order to prevent vehicle fire due to damage of the electrical components and PCBs.

• Journal of Biosystems Engineering
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• v.21 no.1
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• pp.34-43
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• 1996

The present pesticide application technology widely used with a power sprayer in Korea is assessed as the problem awaiting solution in the point of view of its ineffectiveness, inefficiency, and environmental contamination. As one approach to get rid of these problems, the boom spraying with ultra-low volume and precision application technology has been recommended. The study was undertaken to investigate plants damages incurred by the self-propelled boom-sprayer vehicle, to develop the design criteria of vehicle wheel, and to compare plant damages caused by the front wheel steering vehicle, the 4-wheel drive vehicle and the articulated vehicle, by the computer simulation. The experiment showed that the amount of damaged plants incurred by the self-propelled boom sprayer were about 0.29% in average in the field size of 100m$\times$50m(0.5ha), about 60~80% of which recovering while growing. The recommandable wheel size was analyzed to be 70~100cm in diameter, 8~15cm in width from the vehicle-plant-soil relationship. The simulation on damaged plants anticipated to be incurred by various steering vehicles showed that the smaller the turning radius, the lesser the damaged plants within its range of 3~5m. Average plant damage rate by the front wheel steering vehicle, the 4-wheel drive vehicle and articulated vehicle was relatively assessed to be 2 : 1.8 : 1.

• Interaction and multiscale mechanics
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• v.3 no.2
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• pp.173-191
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• 2010

Damage detection plays a very important role to the maintenance of bridge structures. Traditional damage detection methods are usually based on structural dynamic properties, which are acquired from pre-installed sensors on the bridge. This is not only time-consuming and costly, but also suffers from poor sensitivity to damage if only natural frequencies and mode shapes are concerned in a noisy environment. Recently, the idea of using the dynamic responses of a passing vehicle shows a convenient and economical way for damage detection of bridge structures. Inspired by this new idea and the well-established tap test in the field of non-destructive testing, this paper proposes a new method for obtaining the damage information through the acceleration of a passing vehicle enhanced by a tapping device. Since no finger-print is required of the intact structure, this method can be easily implemented in practice. The logistics of this method is illustrated by a vehicle-bridge interaction model, along with the sensitivity analysis presented in detail. The validity of the method is proved by some numerical examples, and remarks are given concerning the potential implementation of the method as well as the directions for future research.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.556-567
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• 2021

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.

• Journal of IKEEE
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• v.25 no.4
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• pp.750-755
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• 2021

This paper proposes techniques for detecting the damage status of each part of a vehicle using YOLOv4. The proposed algorithm learns the parts and their damages of the vehicle through YOLOv4, extracts the coordinate information of the detected bounding boxes, and applies the algorithm to determine the relationship between the damage and the vehicle part to derive the damage status for each part. In addition, the technique using VGGNet, the technique using image segmentation and U-Net model, and Weproove.AI deep learning model, etc. are included for objectivity of performance comparison. Through this, the performance of the proposed algorithm is compared and evaluated, and a method to improve the detection model is proposed.