Capacitive deionization (CDI) process is an emerging process for water desalination. Recently, there has been a major development of architectures in CDI cells using carbon flow electrodes with membrane, called flow-electrode capacitive deionization (FCDI). In FCDI, the advantage is continuous desalination due to the carbon flow electrodes. Numerous research groups dedicated to develop the FCDI process, however, a clear pre-treatment of carbon flow electrodes was not suggested. Study herein, present a clear understanding of effects of pre-treatment of activated carbon based on sonication in the carbon flow electrodes for the basics results with respect to adsorption performance.

In this paper, the on-site applicability review was carried out on the actual site so that the inspection equipment for inspection of tunnel vertical-type vent can be developed to promote the safety of the inspection engineer and improve the inspection cost.

Using GPR(Ground Penetrating Radar) Precise Inspection, the real lining thickness of the tunnel damage was determined and detailed analysis was performed to provide a more accurate structural safety assessment of the tunnel damage. Through this study, It is presented to provide a guide for the application of the tunnel for inspection and diagnosis.

Recently, there have been many studies to classify the image-based damage of bridge using the deep learning and to evaluate the condition. These attempts are one of the ways to overcome limitations of visual inspection through inspectors, and it is also aimed to reduce the cost of necessary maintenance budget by enabling accurate and rapid damage assessment of rapidly growing old facilities and difficult parts of visual inspection. However, it is possible to classify and quantitatively express simple damage (one damage classification such as cracks) with image information (big data) of bridges, but classification and quantification of complex damage can be done by using one deep learning is a limit. Therefore, this study presents considerations and a method to be used for damage detection on the image basis using deep learning.

This study is to develop a bridge inspection technology through convergence of advanced technologies such as drone technology and hybrid image scanning technology. Through this study, a UAV(Unmanned Aerial Vehicle) user guideline framework for bridge condition evaluation is proposed. It is presented for writing UAV user guideline that is applicate a field of a bridge inspection using this proposal framework in this study.

In this study, the bridge management system framework for the application of UAV collection data was proposed. The framework of UAV Bridge Management System(UBMS) is designed based on algorithm of Bridge Management System(BMS), administered by MOLIT. The UAV Bridge Management System will help facility managers to conduct safety diagnosis by providing rapid procedure.

This paper is introduced to construction and operation of the BMS(Bridge Management System) for structure management of long-span bridge. one of the best long span bridge for marine bridge in korea is Incheon bridge. Incheon bridge have applied to BMS for bridge management from construction period to operation and management period. Incheon Bridge during the bridge life cycle by taking advantage of the BMS systematic and efficient inspection management system is available that can extend the life of the bridge by building bridges and management history, and during the operation

본 논문에서는 탄산화 콘크리트 구조물의 내구성을 예측하기 위한 새로운 접근 방법을 제시하였다. 제시된 예측 방법은, 새로운 계측 데이터가 있을 때 베이스 이론에 근거하여 지속적인 업데이팅이 가능하며 모델 매개변수의 확률론적인 특성이 고려된다. 탄산화 내구성 해석 모델의 절차는 라틴 하이퍼큐브 샘플 추출법(LHS)으로 간단하게 정리되고, 이를 통해 얻는 표본으로 결정된다. 이 방법은 콘크리트 구조물의 설계에 유용하게 사용될 수 있으며, 모니터링을 통한 콘크리트 구조물의 잔존수명을 예측할 수 있다. 본 논문에서 사전예측치는 탄산화에 노출된 국내 콘크리트 구조물 데이터(3700개 시편)를 이용하여 콘크리트 탄산계수의 확률 특성을 고려하여 나타내었으며, 우도함수는 현장 모니터링 데이터를 이용하였으며 사후예측치는 사전예측치와 우도함수를 조합하여 나타내었다. 또한, 몬테 카를로 시뮬레이션(MCS)과 LHS의 비교를 통하여 본 논문에서 수행된 LHS를 이용한 샘플링기법이 보다 효율적인 시뮬레이션 수행이 가능함을 확인하였다.