The antiskid performance of the cement concrete pavement in the seasonal frost regions is an important factor determining the safety of road use. However, due to the low efficiency and high cost of on-site detection, it is very important to reasonably predict it. Five key factors such as ice film thickness, tire pressure, tire load, driving speed, and structural depth were determined. The response surface test was performed to determine the corresponding range of the five factors when the model was optimally predicted. A prediction model of antiskid performance for cement concrete pavement in the seasonal frost regions was created, the goodness of model fitting and the normal distribution of the model were tested, and the applicability of the model was verified. The results show that when the thickness of the ice film is 1.5 mm ~ 3 mm, the tire pressure is 180 kPa ~ 240 kPa, the driving speed is 40 km/h ~ 80 km/h, the structural depth is 0.24 mm ~ 0.62 mm and the tire load is 3250 N ~ 4000 N , the prediction level of the model is the best; SRI can be interpreted by the model accounting for 98.5 %, and the regression model has a high degree of fit, which meets the assumption of normal distribution; the model's SRI predicted value fits the field measured SRI value to 0.995, and the degree of fit is high. The prediction model of antiskid performance is of great significance to prolong the service life of cement concrete pavement in seasonal frost regions.

• Antiskid prediction model for cement pavements in seasonal frost regions
  • 1. Introduction
  • 2. Methods
    • 2.1. Screening of influencing factors for antiskid
    • 2.2. Identification of key factors
    • 2.3. Determination of model parameter value range
    • 2.4. Determination of the optimal prediction space of the model
  • 3. Results and Discussion
    • 3.1. Establishment of Model Relationship
    • 3.2. Significance test of regression equation
    • 3.3. Significance test of regression coefficient
    • 3.4. Model goodness of fit test
    • 3.5. Model normal distribution test
    • 3.6. Model verification
  • 4. Conclusions
  • 5. Acknowledgements

Magazine of Civil Engineering / Санкт-Петербургский политехнический университет Петра Великого. — Санкт-Петербург: СПбПУ, 2020-. — Периодичность: 8 раз в год. — Выходит с 2020 г. — Загл. с титул. экрана. — Свободный доступ из сети Интернет (чтение, печать, копирование). — Электрон. журнал. — Текст: электронный

Magazine of Civil Engineering / Санкт-Петербургский политехнический университет Петра Великого. — Санкт-Петербург: СПбПУ, 2020-. № 6 (98), 2020. — 1 файл (16,2 Мб). — Свободный доступ из сети Интернет (чтение, печать, копирование). — Электрон. журнал. — <URL:http://elib.spbstu.ru/dl/2/j21-183.pdf>.