• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2006년도 정기학술발표대회 논문집
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• pp.384-389
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• 2006

최근 경량전철 건설사업에 대한 수요가 증가하고 있으며, 경량전철 건설 사업은 주로 민자사업의 형태로 추진되고 있다. 따라서 사업에 투자할 비용을 계약기간 동안 해당 시설물 운영을 통해 수익창출을 해야 하는 민간건설업체 입장에서는 생애주기측면에서 보다 정확한 비용의 산정이 요구되고 있다. 특히, 경량전철 건설사업에 있어서 비용측면에서 많은 부분을 차지하고 있는 교량은 기존의 생애주기비용 산출방식보다 정밀한 비용 산출이 필요하였다. 이에 본 연구에서는 문헌고찰을 통해 LCC 분석 모델을 개발하고, 비용분류체계를 제시하였다. 제시된 비용분류체계를 바탕으로 경량전철 교량 상부구조 형식별 공사비와 보수 ${\cdot}$ 보강 ${\cdot}$ 교체 이력자료를 바탕으로 비용발생주기 및 비용단가 등을 수집 ${\cdot}$ 분석하였다. 또한, LCC분석을 위한 기본적인 가정사항을 설정한 후, 각 항목별 실적자료 정보를 활용하여 LCC 측면에서의 경제성 평가를 실시하였다. 그간 경량전철을 비롯한 철도교량에 대한 LCC분석연구가 많이 이루어지고 있지 않은 상황임을 감안할 때, 본 연구를 통해 제시된 비용분류체계와 유지관리 관련 데이터는 철도교량의 체계적인 유지관리 활동에 대한 기반이 될 것으로 기대된다.

• Smart Structures and Systems
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• 제14권2호
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• pp.209-224
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• 2014

With more and more built long-term structural health monitoring (SHM) systems, it has been considered to apply monitored data to learn the reliability of bridges. In this paper, based on a long-term SHM system, especially in which the sensors were embedded from the beginning of the construction of the bridge, a method to calculate the localized reliability around an embedded sensor is recommended and implemented. In the reliability analysis, the probability distribution of loading can be the statistics of stress transferred from the monitored strain which covered the effects of both the live and dead loads directly, and it means that the mean value and deviation of loads are fully derived from the monitored data. The probability distribution of resistance may be the statistics of strength of the material of the bridge accordingly. With five years' monitored strains, the localized reliabilities around the monitoring sensors of a bridge were computed by the method. Further, the monitored stresses are classified into two time segments in one year period to count the loading probability distribution according to the local climate conditions, which helps us to learn the reliability in different time segments and their evolvement trends. The results show that reliabilities and their evolvement trends in different parts of the bridge are different though they are all reliable yet. The method recommended in this paper is feasible to learn the localized reliabilities revealed from monitored data of a long-term SHM system of bridges, which would help bridge engineers and managers to decide a bridge inspection or maintenance strategy.

• Smart Structures and Systems
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• 제24권5호
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• 한국방재안전학회논문집
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• 제15권3호
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• pp.79-88
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• 2022

특수교는 중요한 대형 시설물로 장기적이고 체계적인 유지관리 전략을 필요로 한다. 특히, 시설물 부재별 및 위치별로 다양한 센서를 설치하고 계측 항목별 관리 기준치 설정과 같은 시설물의 안전 확보를 위해 여러 방안들이 제시되고 있다. 이 중 지속적으로 증가하는 특수교의 수와 여러 센서에서 수집되는 데이터를 효율적으로 관리하기 위한 전략적인 방안을 제시해야 할 필요가 있다. 본 연구에서는 특수교 계측 시스템에서 수집되는 광범위한 데이터를 효율적으로 분석하기 위한 목적으로 자동적으로 이상신호를 처리하고 통계 결과를 산출할 수 있는 분석 툴을 개발하고자 한다. 분석 툴 개발을 위해 우선 특수교에 설치된 주요 센서 종류 및 수량과 같은 기본적인 정보와 수집된 데이터에 대한 신호 특성을 분석하였다. 이후 험펠 필터 기법을 활용 신호의 이상 유무를 판별하고 필터링하여 통계 결과를 산출하였다. 마지막으로 개발된 분석 툴의 성능 검증을 위해 현재 공용 중인 사장교와 현수교 형식의 교량을 각 1개소씩 성능검증 대상 교량으로 선정하여 신호처리 및 자동 통계 분석 성능을 실시하였고, 기존의 통계 작업 결과와 유사한 결과를 산출 할 수 있었다.

• Animal Bioscience
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• 제35권10호
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• pp.1461-1478
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• 2022

The maintenance of poultry gut health is complex depending on the intricate balance among diet, the commensal microbiota, and the mucosa, including the gut epithelium and the superimposing mucus layer. Changes in microflora composition and abundance can confer beneficial or detrimental effects on fowl. Antibiotics have devastating impacts on altering the landscape of gut microbiota, which further leads to antibiotic resistance or spread the pathogenic populations. By eliciting the landscape of gut microbiota, strategies should be made to break down the regulatory signals of pathogenic bacteria. The optional strategy of conferring dietary fibers (DFs) can be used to counterbalance the gut microbiota. DFs are the non-starch carbohydrates indigestible by host endogenous enzymes but can be fermented by symbiotic microbiota to produce short-chain fatty acids (SCFAs). This is one of the primary modes through which the gut microbiota interacts and communicate with the host. The majority of SCFAs are produced in the large intestine (particularly in the caecum), where they are taken up by the enterocytes or transported through portal vein circulation into the bloodstream. Recent shreds of evidence have elucidated that SCFAs affect the gut and modulate the tissues and organs either by activating G-protein-coupled receptors or affecting epigenetic modifications in the genome through inducing histone acetylase activities and inhibiting histone deacetylases. Thus, in this way, SCFAs vastly influence poultry health by promoting energy regulation, mucosal integrity, immune homeostasis, and immune maturation. In this review article, we will focus on DFs, which directly interact with gut microbes and lead to the production of SCFAs. Further, we will discuss the current molecular mechanisms of how SCFAs are generated, transported, and modulated the pro-and anti-inflammatory immune responses against pathogens and host physiology and gut health.