PURPOSES : The skid resistance between tires and the pavement surface is an important factor that directly affects driving safety and must be considered when evaluating the road performance. In especially wet conditions, the skid resistance of the pavement surface decreases considerably, increasing the risk of accidents. Moreover, poor drainage can lead to hydroplaning. This study aimed to develop a prediction equation for the roughness coefficient—that is, an index of frictional resistance at the interface of the water flow and surface material—to estimate the thickness of the water film in advance to prevent human and material damage. METHODS : The roughness coefficient can be changed depending on the surface material and can be calculated using Manning's theory. Here, the water level (h), which is included in the cross-sectional area and wetted perimeter calculations, can be used to calculate the roughness coefficient by using the water film thickness measurements generated after simulating specific rainfall conditions. In this study, the pavement slope, drainage path length, and mean texture depth for each concrete surface type (non-tined, and tined surfaces with 25-mm and 16-mm spacings) were used as variables. A water film thickness scale was manufactured and used to measure the water film thickness by placing it vertically on top of the pavement surface along the length of the scale protrusion. Based on the measured water film thickness, the roughness coefficient could be back-calculated by applying Manning's formula. A regression analysis was then performed to develop a prediction equation for the roughness coefficient based on the water film thickness data using the water film thickness, mean texture depth, pavement slope, and drainage path length as independent variables. RESULTS : To calculate the roughness coefficient, the results of the water film thickness measurements using rainfall simulations demonstrated that the water film thickness increased as the rainfall intensity increased under N/T, T25, and T16 conditions. Moreover, the water film thickness decreased owing to the linear increase in drainage capacity as the mean texture depth and pavement slope increased, and the shorter the drainage path length, the faster the drainage, resulting in a low water film thickness. Based on the measured water film thickness data, the roughness coefficient was calculated, and it was evident that the roughness coefficient decreased as the rainfall intensity increased. Moreover, the higher the pavement slope and the shorter the drainage path length, the faster the drainage reduced the water film thickness and increased the roughness coefficient (which is an indicator of the friction resistance). It was also evident that as the mean texture depth increased, the drainage capacity increased, which also reduced the roughness coefficient. CONCLUSIONS : As the roughness coefficient of the concrete road surface changes based on the environmental factors, road geometry, and pavement surface characteristics, we developed a prediction equation for the concrete pavement roughness coefficient that considered these factors. To validate the proposed prediction equation, a sensitivity analysis was conducted using the water film thickness prediction equation from previous studies. Existing models have limitations on the impact of the pavement type and rainfall intensity and can be biased toward underestimation; in contrast, the proposed model demonstrated a high correlation between the calculated and measured values. The water film thickness was calculated based on the road design standards in Korea—in the order of normal, caution, and danger scenarios—by using the proposed concrete pavement roughness coefficient prediction model under rainy weather conditions. Specifically, because the normal and caution stages occur before the manifestation of hydroplaning, it should be possible to prevent damage before it leads to the danger stage if it is predicted and managed in advance.

1. 서론
2. 연구 동향 및 이론적 배경
    2.1. 기존 WFT 산정 모델
    2.2. Manning’s Formula
3. Manning’s Formula에 기반한 조도계수 산정방법론
    3.1. 조도계수 산정을 위한 연구 전략
    3.2. 조도계수 산정을 위한 WFT 측정 실험 개요
    3.3. 콘크리트 노면의 WFT 측정 방법
4. WFT 측정 실험 결과 및 조도계수에 미치는 영향인자별 분석
    4.1. 강우강도, 포장 경사, 배수 거리, MTD에 따른 콘크리트 노면 유형별 WFT 측정 결과
    4.2. Manning’s Formula 기반의 조도계수 산정 결과
5. 콘크리트 노면의 조도계수 산정 모델 개발
    5.1. 조도계수 산정 모델 제안
    5.2. 조도계수 산정 모델 활용안
6. 결론
REFERENCES

• 최예인(강릉원주대학교 토목공학과 석사과정) | Choi Ye In
• 김재훈(한국건설기술연구원 국가건설기준센터 박사후연구원) | Kim Jae Hoon
• 신승숙(강릉원주대학교 스마트인프라연구소 연구교수, 공학박사) | Shin Seung Sook
• 이승우(강릉원주대학교 토목공학과 교수, 공학박사) | Lee Seung Woo
• 김영규(강릉원주대학교 스마트인프라연구소 연구교수, 공학박사) | Kim Young Kyu (Research Professor, Institute for Smart Infrastructure, Gangneung-Wonju National University, 7, Jukheon-gil, Gangneung city, Gangwon province 25457, Korea) Corresponding author