PURPOSES : The purpose of this study was to develop the evaluation methodologies for spraying amount and sprayed condition of curing compound based on IoT technology when concrete pavements are constructed. METHODS : To measure the spraying amount of curing compound, a turbine type flowmeter was selected and a number of laboratory experiments were performed to verify the applicability of the selected sensor. To evaluate the uniformity of the sprayed curing compound on the concrete pavement surface, image process technologies were examined using pictures taken from the actual construction sites and from the test specimens. RESULTS : By performing experiments using water and curing compound, the selected flowmeter was verified to properly be applied to measure the spraying amount of curing compound with an acceptable accuracy. By conducting image processing using pictures of the sprayed curing compound on the concrete pavement surface, it was found that the 8 color analysis method was the best to evaluate the uniformity of the sprayed curing compound. CONCLUSIONS : From this study, it was concluded that the spraying amount of curing compound could be accurately measured using a turbine type flowmeter and the uniformity of the sprayed curing compound on the concrete pavement surface could be properly evaluated using an image processing technology.

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.

PURPOSES : The purpose of this study was to evaluate the newly developed Guss mastic asphalt mixtures, called EQ-mastic asphalt mixtures, which contain melted additives for decreasing cooking time. METHODS : A series of experiments were performed to investigate the effectiveness of the melted additives in EQ-mastic asphalt mixtures. Both the existing Guss mastic asphalt mixture and the EQ-mastic asphalt mixture were produced with the same amounts of asphalt binders, aggregates, and fillers, but the existing Guss mastic asphalt mixture contained 3% Trinidad lake asphalt (TLA). The EQ-mastic asphalt mixture contained 3% of additives, including TLA and polyolefin. The physical material performances of both mastic asphalt mixtures were obtained by conducting the Luer fluidity test, penetration test, dynamic stability test, and low-temperature bending test. The results of the tests for the existing Guss mastic and EQ-mastic asphalt mixtures were compared. RESULTS : The fluidity, penetration, dynamic stability, and low-temperature bending strains of both the existing Guss mastic and EQmastic asphalt mixtures all satisfied the standard values provided in the production and construction guides of the Guss mastic asphalt pavement. CONCLUSIONS : When melted additives containing polyolefin are used in the production of Guss mastic asphalt mixtures, the cooking time decreases, so that the corresponding energy consumption and asphalt fume amount can be reduced. Therefore, an EQ-mastic asphalt mixture is proposed for use as an eco-friendly pavement material.

아산화질소(Nitrous oxide, N2O)는 지구온난화 물질의 하나로 이산화탄소에 비해 지구온난화효과가 310배 강하고 분해하는데 120년이 소요되기 때문에 오존층 파괴에 주범으로 알려져 있다. 따라서 본 연구에서는 N2O를 저감하기 위해 고온 열분해 기술을 적용하여 N2O 저감 공정에서 발생하는 NOx 배출 특성에 대해 조사하였다. 고온 유동장을 형성하기 위해 동축 분젠 예혼합 화염을 열원으로 채택하였으며 실험 변수로는 노즐출구속도, 동축류 속도 및 N2O 희석률로 설정하였다. 실험 결과, NO 생성률은 노즐출구속도 및 동축류 유량에 관계없이 N2O 희석률이 증가함에 따라 증가하였다. NO2의 경우에는 연소 불안정성(Kelvin Helmholtz 불안정)이 억제된 안정된 예혼합 화염에서 다량으로 배출되었는데, 이는 화염 면 부근에서 감소된 N2O의 체류시간으로 인해 열분해 시간이 충분하지 않기 때문인 것으로 사료 된다. 따라서 N2O의 저감 효율을 증진시키기 위해서는 K-H 불안정성이 발생되는 노즐출구속도를 선정하여 화염 면 부근에서 발생되는 와류(toroidal vortex) 형태의 유동 구조를 형성하는 것이 N2O의 체류시간을 증가시키는데 효과적인 것으로 판단된다.

PURPOSES : This study verifies the appropriateness of the observed traffic volume using car navigation traffic volume data. METHODS : In this study, we developed an annual average daily traffic (AADT) estimation model that can verify the total amount of traffic by using navigation traffic volume data. In addition, a method to verify the appropriateness of the observed traffic volume was developed using time-based navigation traffic volume data that can check the characteristics of traffic volume at each point. RESULTS : As a result of the analysis of this study, it was found that 674 of the 697 short-duration survey spots of the freeways were appropriate and that 23 spots needed to be revised. CONCLUSIONS : As a result of the analysis of this study, it was found that there was a strong positive correlation between the observed traffic volume and the car navigation traffic volume. Thus, the appropriateness of the observed traffic was determined by verifying the total amount of observed traffic and the observed traffic volume by time.