본 연구는 도로 노면의 결빙을 방지하기 위해 열적 특성을 갖는 콘크리트를 개발했습니다. 팽창 점 토에 상변화 물질(PCM)을 함침 시키고, 고열 전도성 에폭시와 실리카 흄으로 이중코팅을 하여 PCM 물질의 유출 방지, 골재의 부착성 개선, 열적 성능 개선을 하였으며 이를 DSC를 통해 열적 성능 평가 를 진행하여 확인했습니다. 또한 상변화 물질과 경량골재의 사용으로 인한 강도 감소 개선을 위한 CNT 혼합으로 강도 감소를 25% 개선하였습니다.

PURPOSES : This aim of this study is to develop a model for predicting road surface temperature using an LSTM network to predict road surface temperature associated with road icing. METHODS : A long short-term memory (LSTM) neural network suitable for time-series data with time correlation is used in the analysis. Moreover, an optimal neural network architecture is designed via hyperparameter search and verification using learning and validation data. Finally, the generalization performance is evaluated based on the RMSE using unseen data as test data. RESULTS : The results show that the predicted data are similar to the actual road surface temperature patterns , and that the network appears to be generalized. CONCLUSIONS : The LSTM model improves the accuracy and generalization of road surface temperature prediction, as compared with other machine learning models.

Road surfaces and tires have a great influence on road noise in automobiles. Therefore, this study attempted to investigate the effect of changes in road surface and tire tread on road noise. For six road surfaces, road noise was measured and analyzed while changing two types of tire treads. In all frequency bands, the sound pressure of the road surface with a relatively large roughness was higher than that of other roads. And in the case of a road surface with relatively large pore, it was investigated that noise was reduced compared to other road surfaces due to the sound absorption effect in the low frequency area. On roads with irregular road roughness, the high sound pressure was exhibited in all frequency bands regardless of tire tread, indicating an increase in road noise due to irregular wear on roads. It was confirmed that the noise deviation due to the change in road surface was larger than the noise deviation due to the tire structure, and it is judged that noise research according to the structure and condition of the road surface.

PURPOSES : This study aims to develop and evaluate computer vision-based algorithms that classify the road roughness index (IRI) of road specimens with known IRIs. The presented study develops and compares classifier-based and deep learning-based models that can effectively determine pavement roughness grades. METHODS : A set road specimen was developed for various IRIs by generating road profiles with matching standard deviations. In addition, five distinct features from road images, including mean, peak-to-peak, standard variation, and mean absolute deviation, were extracted to develop a classifier-based model. From parametric studies, a support vector machine (SVM) was selected. To further demonstrate that the model is more applicable to real-world problems, with a non-integer road grade, a deep-learning model was developed. The algorithm was proposed by modifying the MNIST database, and the model input parameters were determined to achieve higher precision. RESULTS : The results of the proposed algorithms indicated the potential of using computer vision-based models for classifying road surface roughness. When SVM was adopted, near 100% precision was achieved for the training data, and 98% for the test data. Although the model indicated accurate results, the model was classified based on integer IRIs, which is less practical. Alternatively, a deep-learning model, which can be applied to a non-integer road grade, indicated an accuracy of over 85%. CONCLUSIONS : In this study, both the classifier-based, and deep-learning-based models indicated high precision for estimating road surface roughness grades. However, because the proposed algorithm has only been verified against the road model with fixed integers, optimization and verification of the proposed algorithm need to be performed for a real road condition.

PURPOSES : This study investigates the abrasion characteristics of coarse aggregate using the Los Angeles (L.A.) abrasion test and the accelerated polishing machine (APM) test. The coarse aggregates are randomly exposed on the surface of asphalt concrete pavements and on exposed aggregate concrete pavements. The exposed aggregates play a very important role in providing skid resistance. Therefore, the adequate abrasion resistance of coarse aggregate must be ensured to maintain the skid resistance during service life. In Korea, the LA abrasion test is conducted according to the KS F 2508 standard for the evaluation of the abrasion resistance of coarse aggregate. However, the road surface abrasion is caused by the friction between the tire and the road surface structure; hence, whether the LA abrasion test, which evaluates the abrasion caused by the impact of coarse aggregates and steel balls, can evaluate the road surface abrasion is questionable. A comparison and an analysis between the APM and LA abrasion tests were conducted herein to evaluate the road abrasion. An analysis was also performed to analyze whether the abrasion characteristics appeared depending on the type of coarse aggregate. METHODS: The results of the APM and LA abrasion tests for various aggregate types were obtained through a series of experiments and literature reviews. The correlation between the LA abrasion loss and the PV data was derived. In addition, the influence of the aggregate type on the abrasion resistance was investigated. RESULTS : An abrasion resistance database was established, and the relationship between the rock types and the abrasion resistance was statistically determined. The results showed that the PV was increased to 0.54 along with a 1% increasing rate of the LA abrasion loss with a 0.67 coefficient of determination. The abrasion resistance was also influenced by the aggregate type, which was found to be statistically significant. CONCLUSIONS: A good relationship between the PV and the LA abrasion loss was obtained, allowing the use of the LA abrasion test (KS F 2508) alone, to reasonably evaluate the abrasion resistance of the exposed aggregate texture. The aggregate types were also found to have an impact on the abrasion resistance.

PURPOSES : Exposed aggregate concrete pavements have been adopted in several countries because of their advantages of pavement texture characteristics, which can produce low tire-pavement noise and higher load-carrying capacities. The magnitude of tire-pavement noise greatly depends on the wavelength of pavement texture. The wavelength of exposed aggregate concrete pavement can be controlled with maximum sizing and by controlling the amount of coarse aggregates in the concrete mixture. In this study, the maximum size and the amount of coarse aggregate in the exposed aggregate concrete pavement are investigated to produce equal levels of wavelength in the asphalt pavement. METHODS: A simple method to measure the average wavelength of pavement texture is introduced. Subsequently, the average wavelength of typical asphalt pavement is investigated. A set of mixture designs of exposed aggregate concrete with three maximum-sized coarse aggregates, and three amounts of coarse aggregate are used. The average wavelengths are measured to find the mixture design needed to produce equal levels of wavelength as typical asphalt pavement. RESULTS : With a cement content of 420 kg/m3 and fine aggregate modulus of 30%, the number of exposed aggregates was 48, and the shortest texture depth provided a wavelength of 4.2 mm. According to the number of exposed aggregates, the exposed aggregate concrete pavement could be rendered low-noise, because its wavelength was similar to that of asphalt pavement ranging from 3.9 to 4.4 mm. CONCLUSIONS : Selection of appropriate maximum sizes and the amount of coarse aggregates for exposed aggregate concrete pavement can produce a wavelength texture closely resembling that of asphalt pavement. Therefore, the noise level of exposed aggregate concrete pavement can be reduced with an appropriate maximum size and the amount of coarse aggregates are employed.

PURPOSES: This study aimed to evaluate the performance of a model developed for road surface temperature change pattern in reflecting specific road characteristics. Three types of road sections were considered, namely, basic, tunnel, and soundproof tunnel. METHODS: A thermal mapping system was employed to collect actual road surface temperature and locational data of the survey vehicle. Data collection was conducted 12 times from 05:30 am to 06:30 am on the test route, which is an uninterrupted flow facility. A total of 9010 road surface temperature data were collected, and half of these were selected based on a random selection process. The other half was used to evaluate the performance of the model. The model used herein is based on machine learning algorithms. The mean absolute error (MAE) was used to evaluate the accuracy of the estimation performance of the model. RESULTS: The MAE was calculated to determine the difference between the estimated and the actual road surface temperature. A MAE of 0.48℃ was generated for the overall test route. The basic section obtained the smallest error whereas that of the tunnel was relatively high. CONCLUSIONS: The road surface temperature change is closely related to the air temperature. The process of data pre-processing is very important to improve the estimation accuracy of the model. Lastly, it was difficult to determine the influence of the data collection date on the estimation of the road surface temperature change pattern due to the same weather conditions.

The road surface condition in winter is important for road maintenance and safety. To estimate the road surface condition in winter, the RWIS(Road Weather Information System) is used. However RWIS is not measured the continuous road surface information but measured the locational road surface information. To overcome the current RWIS limitation, the thermal mapping sensor which can collect the road surface condition employed in some countries. Although the thermal mapping sensor can collect the continuous road surface information, it is difficult to collect vast data due to apply few probe car. This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors and collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. To find out the correlation between road surface and ambient temperature which may affect patterns of road surface temperature variation, the various weather and topographical conditions along with the test route were considered. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

In recent years, there have been applied methods for minimizing noise by adjusting the method of installing soundproof walls, soundproof tunnels, soundproofing rims, environmental facilities, etc., and the shape of the surface texture of tire treads and packaging materials for the purpose of reducing road noise. Low noise pavement methods such as rubber asphalt (CRM), open graded asphalt concrete (OGAC), permeable Friction Courses (PFC), open graded friction courses (OGFC) and porous asphalt have been applied to reduce road noise. Especially, porous pavement is the most widely used low noise pavement with porous structure, which can reduce noise and drain water through continuous void of pavement. On the other hand, porous asphalt pavement has problems such as reduction of noise reduction effect and difficulty of road surface management due to void closing and increase of construction cost. The purpose of this study is to develop ultra-thin layer hot mix asphalt pavement method which maximizes road noise reduction effect by surface micro voids (Recover asphalt pavement) to improve void clogging of present porous pavement method. For this study, maximum size 5mm aggregate and cationic-treated fiber reinforced asphalt modifier (CSM) were used. The Marshall design method was applied grain-size distribution curve was based on SMA mix design. Marshall test, TSR, MMLS3 test and Hamburg test were carried out to evaluate the mechanical properties of ultra -thin layered asphalt pavement method with surface micro voids. Also, the effect of road noise reduction was evaluated through field application in urban area.

In this study, we introduce the technology that provides the driver with the information related to the traffic information application such as the traffic center or T-map when the slippery situation occurs on the road by collecting the slip information by the vehicle speed on the road surface. Road surface detection technology collects road surface information by using black box and DTG which are installed in commercial vehicles and detects dangerous sections for road safety. It transmits information to a traffic center and transmits it to a rear vehicle driver It is aimed at delivering safe driving information.

PURPOSES: This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS: Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS: Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

PURPOSES:This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms.METHODS:Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error.RESULTS:According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance.CONCLUSIONS :When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.