• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.3
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• pp.513-520
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• 2017

For the last 60years, North Korea has constructed a lot of roadway bridges with different standard from that used in South Korea, and since North Korea prefer to take advantage of train more than truck for long distance transport, the construction and maintenance of roadway bridges have not been constructed effectively. Upon these situations, an exact evaluation of the resisting capacity for bridges in North Korea has been required to check of any bridge can be used in time of war. This paper introduces an evaluation of bridges in North Korea on the basis of Military Load Classification (MLC). Three different types of concrete bridges are considered, and the numerical analysis and design calculation give the military loadings which can pass through the bridges in North Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.8 no.1
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• pp.61-68
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• 1988

This study is intended to propose an approach to reliability-based safety evaluation as well as LRFR(Load and Resisitance Factor Rating) type capacity classification of military or civilian bridges based on the limit state models which are delived by incorporating all the uncertainties of resistance and load random variables including deterioration, and are used in a practical AFOSM (Advanced First Order Second Moment) method. The proposed methods for the assement of safety and load carrying capacity are applied for the evaluation of rating and classifications of several practical bridges against the crossing of military vehicles. Based on the observation of the numerical results, it can be concluded that the current NATO classification method which is based on the traditionl allowable stress concept can not provide real load carrying capacity but results in nominal classification, and therefore the reliability-based safety evaluation and LRFR-classification method or the corresponding rational allowable stress method proposed in this paper may have to be introduced into the classification practice.

• Journal of the Korean Society of Hazard Mitigation
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• v.6 no.2 s.21
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• pp.37-44
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• 2006

Any vehicle and equipment whose total weight is more than 40ton and its axle weight is 10ton or above is banned to cross any bridge in our country under section 54 in the Highway law. This restriction results from the accumulation and application of safety factors about which there is type specification in the "standard design vehicle". And in "standard design vehicle", Vehicle load to bridge is assumed concentrating one. Based on this restriction, there is an issue that military tank which has a total weight of 51ton (63ton in case of the US tank) can not cross any bridge. However, many research and practical examples concerned manifest that it is possible for military tanks to cross these bridges. The reasons of this issue in the current Highway law's provisions are analyzed in this paper. Correspondingly, feasibility of military tanks passing these bridges are discussed here. At last, considering economical efficiency and practicability for military, several suggestions and improving measures are put forward. This research has certain reality significance to guide bridge design considering the passage of military heavy vehicles.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.346-354
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• 2024

Deep learning models require high computing power due to a substantial amount of computation. It is difficult to use them in devices with limited computing environments, such as coastal surveillance equipments. In this study, a lightweight model is constructed by analyzing the weight changes of the convolutional layers during the training process based on MobileNet and then pruning the layers that affects the model less. The performance comparison results show that the lightweight model maintains performance while reducing computational load, parameters, model size, and data processing speed. As a result of this study, an effective pruning method for constructing lightweight deep learning models and the possibility of using equipment resources efficiently through lightweight models in limited computing environments such as coastal surveillance equipments are presented.