PURPOSES: This study examines the performance changes of road networks according to the strength of a disaster, and proposes a method for estimating the quantitative resilience according to the road-network performance changes and damage scale. This study also selected highinfluence road sections, according to disasters targeting the road network, and aimed to analyze their hazard resilience from the network aspect through a scenario analysis of the damage recovery after a disaster occurred. METHODS: The analysis was conducted targeting Sejong City in South Korea. The disaster situation was set up using the TransCAD and VISSIM traffic-simulation software. First, the study analyzed how road-network damage changed the user’s travel pattern and travel time, and how it affected the complete network. Secondly, the functional aspects of the road networks were analyzed using quantitative resilience. Finally, based on the road-network performance change and resilience, priority-management road sections were selected. RESULTS: According to the analysis results, when a road section has relatively low connectivity and low traffic, its effect on the complete network is insignificant. Moreover, certain road sections with relatively high importance can suffer a performance loss from major damage, for e.g., sections where bridges, tunnels, or underground roads are located, roads where no bypasses exist or they exist far from the concerned road, including entrances and exits to suburban areas. Relatively important roads have the potential to significantly degrade the network performance when a disaster occurs. Because of the high risk of delays or isolation, they may lead to secondary damage. Thus, it is necessary to manage the roads to maintain their performance. CONCLUSIONS : As a baseline study to establish measures for traffic prevention, this study considered the performance of a road network, selected high-influence road sections within the road network, and analyzed the quantitative resilience of the road network according to scenarios. The road users’passage-pattern changes were analyzed through simulation analysis using the User Equilibrium model. Based on the analysis results, the resilience in each scenario was examined and compared. Sections where a road’s performance loss had a significant influence on the network were targeted. The study results were judged to become basic research data for establishing response plans to restore the original functions and performance of the destroyed and damage road networks, and for selecting maintenance priorities.

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other conc

High-performance concrete(HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance(MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction perfomance.

In this study, concrete slab with insulation and non-insulated formwork was used to monitor and control its strength using a wireless sensor network with equivalent age method. As a result, the temperature and compressive strength of the structures were monitored without any problems, which revealed a good management tool.

In this study examined the strength of wall concrete using insulated molds and non-insulated forms by monitoring the strength of the structure using a wireless sensor network by maturity method. It was confirmed that the temperature and compressive strength of the structures are monitored in real time, along with effective strength control.

우리나라의 경우 1990년대부터 환경오염문제의 사회화가 배경이 되어 환경에 대한 관심이 높아짐에 따라 1998년부터 배기가스의 탈황공정이 가동되어 화학석고가 발생하기 시작하였는데 이것이 화력발전소에서 부산물로 나오는 배연탈황석고이다. 국내의 석탄화력 발전소에 설치된 탈황설비는 흡착재로 석회석 분말을 사용하고 부산물로 석고를 생성하는 습식공정으로서, 배연탈황석고는 이수석고(CaSO4⋅2H2O)로 생성되는데, 인산석고와 비교할 때 pH가 중성이며 높은 순도의 균일한 품질을 가지고 있어 발생 전량이 시멘트 및 석고보드 원료로서 재활용되고 있다. 한편 최근 그 수요가 증가하는 고강도콘크리트 혼화재, 슬래그 시멘트에 사용하기 위하여 년간 30만톤 이상 수입되고 있는 천연무수석고는 우리나라에 광물로 부존하지 않는다. 선진국과 마찬가지로 배연탈황 석고가 전량 수입되고 있는 천연석고를 대체할 수 있다는 장점에 대하여 충분히 인식함에도 불구하고, 아직까지 전반적인 기술 기반의 취약성 및 인력 부족으로 석고보드 제조 등 초보적인 수준에 머물러 있으나 최근 콘크리트 혼화재료 제조기업은 중국의 값싼 제품으로 인해 가격 경쟁력을 상실하고 있어 미래 경쟁력 있는 분야로의 전환을 위해 배연탈황 석고를 이용한 고부가성 건설재료 제조 기술에 관심을 가지기 시작하고 있다. 이에 본 연구에서는 지속가능 친환경-고성능 건설용 복합재료의 생산 및 이의 활용 기술을 적극적으로 개발하고자 인공신경망 모델을 활용한 배연탈황석고 모르타르의 배합조건과 물리적 결과값의 데이터를 다양한 알고리즘에 적용하여 이의 분석과 예측의 정확성을 판별하여 기초데이터로 제공하고자 한다.

By using non-destructive inspection equipment, ultrasonic and schmidt hammer, this research performed non-destructive inspection of underwater concrete for more efficient management in underwater concrete structures. And based on instrumented rebound hardness and ultrasonic pulse velocity, we developed the underwater concrete structures-strength estimation models using artificial neural network.

본 연구에서는 화산재를 건설재료로 활용하기 위하여 백두산, 한라산의 화산재 및 다공성의 제올라이트에 시멘트 및 메타카올린을 첨가한 공시체에 대한 재령 0일, 7일, 28일 배합비별 압축강도 특성 데이터를 바탕으로 인공신경망 모델에 적용하여 학습, 예측함으로써 강도예측을 위한 인공신경망의 적용 가능성을 평가하였다. 인공신경망 모델에는 역전파 학습알고리즘(back-propagation learning algorithm)이 적용되었으며, 다양한 입력변수를 달리한 최적의 인공신경망 조건에서 학습을 시행하였다. 또한, 다양한 배합조건이 일축압축강도에 미치는 영향에 대한 민감도 분석을 실시하였다. 이러한 연구를 통해 얻어진 결과물은 화산재를 활용한 블록의 일축압축강도 특성을 파악하는데 좋은 툴이 될 것으로 기대된다.

Temperature of fresh concrete can be effectively used to predict the strength of concrete being cured and make an informed decision for stripping the molds. A hygrothermograph and thermo-couple sensors that require an extensive wiring have been applied to measure a temperature of concrete at the early stage of the curing process on site. Therefor, this study on the strength prediction using Maturity is mainly focused on, but the study on the concrete mixing blast furnace slag powder is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by Maturity function and is to compare and examine the strength prediction of concrete mixing Blast Furnace Slag Power using ACI and Logistic Curve prediction equation.

풍화토 사면은 장시간 공기에 노출되거나 물과 접촉을 하게 되면 전단강도가 급격히 저하되며 계절에 따른 수위의 변화가 매우 큰 댐사면의 경우는 강도저하가 더욱 크게 발생한다. 풍화토 사면의 강도저하 파악을 위하여 반복전단시험 및 수침․건조를 반복한 시료에 대한 잔류강도 시험을 통해 포화에 따른 강도의 저하를 파악하였다. 또한 소형동적콘관입시험기를 이용하여 관입타격횟수와 전단강도정수관계를 파악하고 인공신경망 해석을 통하여 관입타격횟수 Nc를 이용한 전단강도정수의 예측이 용이하도록 상관식을 구성하였다.