PURPOSES : Pavement surface friction depends significantly on pavement surface texture characteristics. The mean texture depth (MTD), which is an index representing pavement surface texture characteristics, is typically used to predict pavement surface friction. However, the MTD may not be sufficient to represent the texture characteristics to predict friction. To enhance the prediction of pavement surface friction, one must select additional variables that can explain complex pavement surface textures. METHODS : In this study, pavement surface texture characteristics that affect pavement surface friction were analyzed based on the friction mechanism. The wavelength, pavement surface texture shape, and pavement texture depth were hypothesized to significantly affect the surface friction of pavement. To verify this, the effects of the three abovementioned pavement surface texture characteristics on pavement surface friction must be investigated. However, because the surface texture of actual pavements is irregular, examining the individual effects of these characteristics is difficult. To achieve this goal, the selected pavement surface texture characteristics were formed quantitatively, and the irregularities of the actual pavement surface texture were improved by artificially forming the pavement surface texture using threedimensionally printed specimens. To reflect the pavement surface texture characteristics in the specimen, the MTD was set as the pavement surface texture depth, and the exposed aggregate number (EAN) was set as a variable. Additionally, the aggregate shape was controlled to reflect the characteristics of the pavement surface texture of the specimen. Subsequently, a shape index was proposed and implemented in a statistical analysis to investigate its effect on pavement friction. The pavement surface friction was measured via the British pendulum test, which enables measurement to be performed in narrow areas, considering the limited size of the three-dimensionally printed specimens. On wet pavement surfaces, the pavement surface friction reduced significantly because of the water film, which intensified the effect of the pavement surface texture. Therefore, the pavement surface friction was measured under wet conditions. Accordingly, a BPN (wet) prediction model was proposed by statistically analyzing the relationship among the MTD, EAN, aggregate shape, and BPN (wet). RESULTS : Pavement surface friction is affected by adhesion and hysteresis, with hysteresis being the predominant factor under wet conditions. Because hysteresis is caused by the deformation of rubber, pavement surface friction can be secured through the formation of a pavement surface texture that causes rubber deformation. Hysteresis occurs through the function of macro-textures among pavement surface textures, and the effects of macro-texture factors such as the EAN, MTD, and aggregate shape on the BPN (wet) are as follows: 1) The MTD ranges set in this study are 0.8, 1.0, and 1.2, and under the experimental conditions, the BPN (wet) increases linearly with the MTD. 2) An optimum EAN is indicated when the BPN (wet) is the maximum, and the BPN decreases after its maximum value is attained. This may be because when the EAN increases excessively, the space for the rubber to penetrate decreases, thereby reducing the hysteresis. 3) The shape of the aggregate is closely related to the EAN; meanwhile, the maximum value of the pavement surface friction and the optimum EAN change depending on the aggregate shape. This is believed to be due to changes in the rubber penetration volume based on the aggregate shape. Based on the results above, a statistical prediction model for the BPN (wet) is proposed using the MTD, EAN, and shape index as variables. CONCLUSIONS : The EAN, MTD, and aggregate shape are crucial factors in predicting skid resistance. Notably, the EAN and aggregate shape, which are not incorporated into existing pavement surface friction prediction models, affect the pavement surface friction. However, the texture of the specimen created via three-dimensional printing differs significantly from the actual pavement surface texture. Therefore, the pavement surface friction prediction model proposed in this study should be supplemented with comparisons with actual pavement surface data in the future.

Evaluation of low temperature performance of asphalt mixture is significant not only for mitigating transverse thermal cracking but also for preventing potential traffic accidents. In addition, the engineers in pavement agency need to inform the proper pavement section where urgent management is needed. Since early 2000, Korea Expressway Corporation Research Division (KECRD) developed an 3D Pavement condition Monitoring profiler vehicle (3DPM) to survey expressway pavement surface condition precisely. The management of whole expressway network became more precise, effective and efficient than before due to application of 3DPM and HPMS. One thing recommended is: performing extensive mechanical test and corresponding data analysis work procedure to further strengthen the feasibility of current 3DPM approach and HPMS. In this paper two activities were considered: first, the pavement section where the urgent care is recommended is selected by means of 3DPM approach. Then asphalt mixture cores were acquired on that specified section then low temperature fracture test: Semi Circular Bending (SCB) test, was performed. The mechanical parameters, energy release rate and fracture toughness were computed then compared. It is concluded that the current 3DPM approach in KEC can successfully evaluate and analyze selected pavement condition. However, more extensive experimental works are needed to further strengthen the current pavement analyzing approaches.

노면 마찰력은 포장 표면과 타이어의 마찰력으로 인해 발생하는 현상으로 높은 노면의 마찰력은 제동 중 차량의 안정성과 조종성을 향상시킨다. 노면 마찰력이 증가함에 따라 교통사고 횟수가 감소하는 것으로 알려져 있으며 습윤 상태의 노면에서 교통사고가 증가하 는 것으로 알려져있다. 따라서 교통사고 발생 억제와 도로 안전의 확보를 위해서는 적정 수준의 노면 마찰력, 특히 습윤 상태의 노면 마찰력을 확보하는 것이 중요하다. 노면 마찰력은 adhesion과 hysteresis로 분류되며 특히 습윤상태 도로에서 hysteresis가 중요한 역 할을 한다. hysteresis는 고무의 변형에 의해 발생하기 때문에 고무 변형에 영향을 미치는 노면 조직 변수를 선정하여 노면 마찰력을 예측하고자 한다. 노면 마찰력은 노면 조직 특성과 밀접한 관련이 있으며, 이에 따라 노면 조직 특성을 나타내는 지수 중 하나인 MTD(Mean Texture Depth)가 노면 마찰력 예측을 위한 인자로 사용되고 있는 실정이다. 하지만 MTD는 노면 조직 깊이만을 평가하 는 인자로 다양한 요소가 결합되어 있는 노면 조직 특성을 모두 설명할 수 없으며, 노면 마찰력 예측을 위해서는 복잡한 노면 조직을 설명할 수 있는 추가 변수의 선정이 요구된다. 본 연구에서는 노면 마찰력의 메커니즘 분석을 토대로 노면 마찰력에 영향을 미치는 노면 조직 특성을 분석하였고, wave-length와 노면 조직의 형태, 노면 조직 깊이가 노면 마찰력에 미치는 영향이 클 것으로 예상하였 다. 이를 검증하기 위해서는 3가지 노면 조직 특성이 노면 마찰력에 미치는 영향에 대한 검토가 요구되나 실제 도로의 노면은 노면 조직이 불규칙하게 형성되어 있어 노면 조직 특성의 개별적 영향을 검토하기 어렵다. 이를 위해서는 선정한 노면 조직 특성의 정량적 형성이 요구되며 3D 프린팅 시편을 제작해 노면 조직을 인위적으로 형성함으로써 실제 도로 노면 조직의 불규칙성을 개선하였다. 노 면 조직 특성을 시편에 반영하기 위해 노면 조직 깊이는 MTD, wave-length는 노출 골재의 개수를 뜻하는 EAN을 변수로 설정하였 다. 또한 EAN(Exposed Aggregate Number)은 노출 골재의 형성이 필수적이므로 골재의 형상을 제어하여 노면 조직의 형태를 시편에 반영하였으며 골재 형상과 노면 마찰력의 통계학적 분석을 위해 형상 지수를 산출하여 분석하였다. 3D 프린팅 시편은 크기에 제한이 있어 좁은 영역에서 측정이 용이한 BPT(British Pendulum Test)를 사용해 노면 마찰력을 측정하였고, 습윤한 노면에서는 수막으로 인해 노면 마찰력이 크게 감소하여 노면 조직의 영향이 커지므로 습윤 상태에서 노면 마찰력을 측정하였다. 측정 데이터를 통한 분석 결과 노면 조직 변수인 MTD가 증가할수록 BPN(wet)이 선형적으로 증가하는 것이 확인되었으며, EAN에 따라서 BPN이 증가했다가 감소하는 경향이 나타났다. 이는 EAN이 과도하게 많아지면 고무가 침투할 공간이 줄어들어 hysteresis가 감소하기 때문으로 사료된 다. 또한 골재 형상에 따라 노면 마찰력의 최댓값과 optuimum EAN의 변화가 있었다. 이는 골재 형상에 따른 고무 침투 부피의 변화 에 의한 것으로 사료된다. 위의 결과를 통해 MTD, EAN, 골재 형상과 BPN(wet)의 관계를 통계학적으로 분석하여 BPN(wet) 예측 모 델을 제안하였다.

PURPOSES : The purpose of this study is to provide basic data to improve the service life of asphalt pavement using basalt aggregate in Jeju Island by evaluating the performance of asphalt pavement through analysis of material and structural aspects. METHODS : To evaluate the performance of Jeju Island's asphalt pavement, cracks, permanent deformation, and longitudinal roughness were analyzed for the Aejo-ro road, which has high traffic and frequent premature damage. Cores were collected from Aejo-ro sections in good condition and damaged condition, and the physical properties of each layer were compared and analyzed. In addition, plate cores were collected from two sections with severe damage and the cause of pavement damage was analyzed in detail. RESULTS : About 45% of the collected cores suffered damage such as layer separation and damage to the lower layer. The asphalt content of surface layer in the damaged section was found to be 1.1% lower on average than that in the good condition section, and the mix gradations generally satisfied the standards. The density difference between the cores of each layer was found to be quite large, and the air voids was found to be at a high level. CONCLUSIONS : Test results on the cores showed that, considering the high absorption ratio of basalt aggregate, the asphalt content was generally low, and the high air voids of the pavement was believed to have had a significant impact on damage. High air voids in asphalt pavement can be caused by poor mixture itself, poor construction management, or a combination of the two factors. Additionally, the separation of each layer is believed to be the cause of premature failure of asphalt pavement.

algorithms for deriving and analyzing retroreflectivity influence factors through regression analysis. METHODS : An experimental road lane was created to examine the trends of retroreflectivity and LiDAR intensity values, and a controlled indoor experiment was conducted to identify influencing factors. The optimal algorithm was developed by regression analysis of the experimental data. RESULTS : The significance probability (P-value) through SPSS linear regression analysis was 0.000 for measured height, 0.001 for perpendicular angle, 0.157 for vertical angle, and 0.000 for LiDAR intensity, indicating that measured height, vertical angle, and LiDAR intensity are significant factors because the significance probability is less than 0.05, and vertical angle is not significant. The NNR regression model performed the best, so the measurement data with height (1.2m, 2m, 2.2m) and vertical angle (11.3°, 12.3°, 13.5°) were analyzed to derive the optimal LiDAR Intensity measurement height and vertical angle. CONCLUSIONS : For each LiDAR measurement height and vertical angle, the highest correlation between LiDAR Intensity and retroreflectivity was found at a measurement height of 1.2 meters and a vertical angle of 12.3°, where the model learning accuracy (R2) was the highest.

PURPOSES : The evaluation of the low-temperature performance of an asphalt mixture is crucial for mitigating transverse thermal cracking and preventing traffic accidents on expressways. Engineers in pavement agencies must identify and verify the pavement sections that require urgent management. In early 2000, the research division of the Korea Expressway Corporation developed a three-dimensional (3D) pavement condition monitoring profiler vehicle (3DPM) and an advanced infographic (AIG) highway pavement management system computer program. Owing to these efforts, the management of the entire expressway network has become more precise, effective, and efficient. However, current 3DPM and AIG technologies focus only on the pavement surface and not on the entire pavement layer. Over the years, along with monitoring, further strengthening and verification of the feasibility of current 3DPM and AIG technologies by performing extensive mechanical tests and data analyses have been recommended. METHODS : First, the pavement section that required urgent care was selected using the 3DPM and AIG approaches. Second, asphalt mixture cores were acquired from the specified section, and a low-temperature fracture test, semi- circular bending (SCB) test, was performed. The mechanical parameters, energy-release rate, and fracture toughness were computed and compared. RESULTS : As expected, the asphalt mixture cores acquired from the specified pavement section ( poor condition – bad section) exhibited negative fracture performances compared to the control section (good section). CONCLUSIONS : The current 3DPM and AIG approaches in KEC can successfully evaluate and analyze selected pavement conditions. However, more extensive experimental studies and mathematical analyses are required to further strengthen and upgrade current pavement analysis approaches.

PURPOSES : The numeric-based Highway Pavement Management System (HPMS), along with an advanced three-dimensional pavement condition monitoring profiler vehicle (3DPM), in South Korea has presented remarkable advancements in pavement management since the early 2000. Based on these results, visual distress on pavement surfaces can be easily detected and analyzed. Additionally, the entire expressway pavement surface conditions in South Korea can be easily monitored using the current graphical user interface-based advanced information graphic (AIG) approach. Therefore, a critically negative pavement section can be detected and managed more easily and efficiently. However, the actual mechanical performance of the selected pavement layer still needs to be investigated in a more thorough manner not only to provide more accurate pavement performance results but also to verify the feasibility of the current 3DPM and AIG approaches. In this study, the low-temperature performance of the selected asphalt pavement layer section was evaluated to further verify and strengthen the feasibility of the current 3DPM and AIG approaches developed by the Korea Expressway Corporation. METHODS : Based on 3DPM and AIG approach, the positive and negative-riding-quality road sections were selected, respectively. The asphalt material cores were extracted from each section then bending beam rheometer mixture creep test was performed to measure their low-temperature properties. Based on the experimental results, thermal stress results were computed and visually compared. RESULTS : As expected, the asphalt material from the negative driving performance section presented a poorer low-temperature cracking resistance than that from the positive driving performance section. CONCLUSIONS : Current 3DPM equipment can successfully evaluate expressway surface conditions and the corresponding material performance quality. However, more extensive experimental studies are recommended to verify and strengthen the findings of this study

PURPOSES : In this paper, problems and improvements in pavement marking standards are proposed as a result of the analysis of domestic/foreign standards and field data. METHODS : In this study, the US FHWA and all state standards were analyzed and domestic pavement marking standards were compared and analyzed. In addition, national highway marking test data, namely the Yeoncheon Test-Bed data, and data from Hawkins et al. (2015) were analyzed. Based on this, problems and improvements in the Korean pavement marking standards are proposed. RESULTS : As a result of literature and field data analysis, various problems were found in the Road Traffic Act and National Police Agency manual. In this standard, there is no definition of Qd; the RD reference value is set too low; and the RW and RR reference values are difficult to be considered reasonable values. CONCLUSIONS : There is an aspect in which the standard items and values of pavement markings are set under some policy judgment. Therefore, it is necessary to properly revise the standards, considering technical and economic aspects.

PURPOSES : The increasing heat wave warnings during the summer season in Korea have significant impacts on daily life and industry as a whole, especially in urban areas (such as areas with asphalt and sidewalk pavements). Heat waves directly affect urban heat island and heat dome phenomena. Various urban temperature reduction measures are being discussed to reduce urban heat islands and heat dome phenomena and to improve citizen safety against summer heat waves; suggestions include thermal packaging, rooftop greening, and expansion of vegetation areas. There is a lack of analysis on the methodology for increasing the road spraying effect during summer heat waves (e.g., there is no systematic engineering study on the effect from reducing the temperature of the road spraying during a heat wave in the city) and on the types of road pavements in the city. In addition, as the asphalt pavements of roadways and block pavements installed in sidewalks account for a considerable portion of all pavements, this study provides a more systematic and scientific approach and procedures for reducing temperatures through road spraying in the city by tracking the effects of heat waves. METHODS : In this preliminary experiment, four types of road pavement materials were selected as test specimens: asphalt test specimens (AP- 300 mm × 300 mm × 50 mm), concrete test specimens (CP-300 mm × 300 mm × 50 mm), impermeable blocks (IB-200 mm × 200 mm × 60 mm), and self-permeable blocks (PB-200 mm × 200 mm × 60 mm). As a test method to evaluate the size and duration of each spray effect package type, the surface temperature of each specimen was measured using thermal imaging cameras every 20 min after spraying at the maximum temperature point of each specimen, and the average surface temperature was analyzed based on the collected temperature data. In addition, to conduct a quantitative analysis of the effect of reducing the surface temperature of road pavements by road spraying in summer, field tests were conducted on asphalt roads and watertight blocks for sidewalks. RESULTS : As a result of the comparative analysis of the spray effect under a 36 ℃ air temperature based on a heat wave warning, the surface temperatures were, from high to low, the asphalt (68.8 ℃), concrete (59.1 ℃), impermeable block (57.3 ℃), and permeable block (58.7 ℃). The asphalt pavement had the greatest effect on the heat island and heat dome phenomena. From measuring the temperature reduction effect and sustainability of each type of road pavement, the surface temperature reduction effects were ranked in the following order: water-permeable block (Δ18.0 ℃), asphalt test piece (Δ17.5 ℃), concrete test piece (Δ12.2 ℃), and water-permeable block (over 240 min). In the report pitching block, the average road surface temperature reduction between the pore recovery and treatment was expected to continue to decrease by approximately -4.3 ℃ on the day of work and approximately -2.4 ℃ on the next day. The expected effect of the temperature reduction owing to simple spraying on the surface of the pore block was evaluated to be limited to the day. CONCLUSIONS : In the road spray effect analysis conducted on the common asphalt road, there was a slight difference in the initial temperature reduction size as the test specimen was measured, but the surface temperature difference between the non-spray section and spray section tended to be approximately Δ3°C after 140 minutes of spraying. Therefore, it was determined that the asphalt pavement temperature reduction plan through road spraying in urban areas in summer would be the most effective if it was repeated twice or more in an hour (between 13:00 and 14:00) on the day of the heat wave.

PURPOSES : The purpose of this study was to develop techniques for forecasting black ice using historical pavement temperature data collected by patrol cars and concurrent atmospheric data provided by the Korea Meteorological Administration. METHODS : To generate baseline data, the physical principle that ice forms when the pavement temperature is negative and lower than the dew-point temperature was exploited. To forecast frost-induced black ice, deep-learning algorithms were created using air, pavement, and dew point temperatures, as well as humidity, wind speed, and the z-value of the historical pavement temperature of the target segment. RESULTS : The suggested forecasting models were evaluated against baseline data generated by the above-mentioned physical principle using pavement temperature and atmospheric data gathered on a national highway in the vicinity of Young-dong in the Chungcheongbukdo province. The accuracies of the forecasting models for the bridge and roadway segments were 94% and 90%, respectively, indicating satisfactory results. CONCLUSIONS : Preventive anti-icing maintenance activities, such as applying anti-icing chemicals or activating road heating systems before roadways are covered with ice (frost), could be possible with the suggested methodologies. As a result, traffic safety on winter roads, especially at night, could be enhanced.

본 연구는 도로 노면결빙 판정 알고리즘에 대해 알고리즘을 개선하고 실제 현장 측정 자료와 알고리즘 예측값을 비교하였을 때 알고리즘에 대한 적중률을 분석하였다. 분석을 위하여 포천시 신북면 금 동리의 도로 및 기상을 측정하였다. 알고리즘은 기존 도로 결빙 알고리즘을 선정하여 실제 결빙 조건 및 측정 수치에 맞춰 4차 알고리즘까지 개선하였다. 최종적으로 응결에 의한 결빙, 강수에 의한 결빙, 적설에 의한 결빙, 결빙상태의 지속, 풍속에 의한 결빙 5개의 알고리즘을 제작하였다. 포천 현장에서 알고리즘을 활용하여 예측할 경우 경우 결빙 적중률이 93.22%까지 개선되었다. 결빙 알고리즘에 대한 조합 비율에 대 해 도출하였을 때 응결에 의한 결빙과 결빙상태의 지속에 대한 알고리즘이 96%를 차지하였다.

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.