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Ice cover on pavement may reduce the road adhesion coefficient and increase the crash risks, which might result in more traffic crashes. The primary factor utilized to assess whether the wet pavement is icy or not is the pavement temperature. Therefore, forecasting pavement temperature is an effective method to judge road conditions and improve traffic safety. This paper proposes a combination model based on the extreme gradient boosting (XGBoost) model and long- and short-term time-series network (LSTNet) model to predict pavement temperature. Pavement temperature and meteorological data were collected for the cities along the Shandong part of the Beijing-Taipei Expressway (G3). In this study, nine meteorological variables were used. Subsequently, after correlation analysis, five variables, including air temperature, dew point temperature, relative humidity, evaporation, and potential evaporation, were selected for prediction. The method proposed in this study comprises the following steps. First, the XGBoost and the LSTNet models are respectively formulated based on the time-varying characteristics of pavement temperatures. Then, using the preset weight of the variable, the XGBoost model is used for preliminary prediction to add features. Finally, the experimental analysis is performed on the Qihe data set after the two models have been integrated using the inverse variance method. As revealed by the experimental results, the mean absolute error (MAE) and root-mean-square error (RMSE) of the proposed XGBoost-LSTNet model are 0.8235 and 1.2412, respectively. Compared with the long short-term memory (LSTM) model, random forest (RF) model, XGBoost model, and LSTNet model, the XGBoost-LSTNet model proposed in this paper has higher accuracy. The study’s findings can successfully increase wintertime expressway traffic safety and serve as a guide for managing maintenance and preventing icing-related accidents. [ABSTRACT FROM AUTHOR]

Pavement temperature is the main factor determining road icing, and accurate and timely pavement temperature prediction is of significant importance to regional traffic safety management and preventive maintenance. The prediction of pavement temperature at the micro-scale has been a challenge to be tackled. To solve this problem, a bidirectional extended short-term memory network model based on the attention mechanism (Att-BiLSTM) was proposed to improve the prediction performance by using the time series features of pavement temperature and meteorological factors. Pavement temperature data and climatic data were collected from a road weather station in Yunnan, China. The results show that the MAE, MSE, and MAPE of the proposed Att-BiLSTM model were 0.330, 0.339, and 10.1%, respectively, which were better than the other baseline models. It was shown that 93.4% of the predicted values had an error less than 1 °C, and 82.1% had an error less than 0.5 °C, indicating that the proposed Att-BiLSTM model enables significant performance improvement. In addition, this paper quantified and analyzed the effects of parameters such as the size of the sliding window, the number of hidden layer neurons, and the optimizer on the performance of the prediction model. [ABSTRACT FROM AUTHOR]

Temperatures in asphalt layers of a flexible pavement significantly influence its performance, and its accurate prediction is vital for effective life-cycle design that models asphalt as a viscoelastic material. In 2023, Rajapaksha et al. observed that convective heat transfer plays a significant role in temperature prediction, and this study focused on developing a more accurate model for Convection Heat Transfer Coefficient (CHTC) to achieve that using a five-step approach. The cyclical nature of temperatures measured at 5-minute time steps for one year prompted a model based on Fourier analysis. Step 1 conducted a parametric study of CHTC that compared predicted and measured temperatures to backcalculate an optimum value for each time step. Step 2 used these values as the basis to develop monthly Fourier models, but they were not sufficiently accurate. When key weather parameters were added to it in Step 3 , the resulting monthly empirical models developed using stepwise regression analysis improved to an acceptable level. In Step 4, the monthly CHTC regression models were used to predict pavement temperatures for the whole year that were compared with measured values from a field test section. An error analysis performed in Step 5 revealed that the RMSE of prediction error for the year was 2.178 °C which was very close to the benchmark set at the beginning of this work. In addition, the median error was estimated at −0.09 °C showing that the proposed CHTC model is sufficiently accurate to be used in pavement performance prediction. • Accurate temperature prediction is vital for life-cycle-based flexible pavement design. • Purely mechanistic approach is essential, but not sufficient for pavement temperature prediction. • Convection Heat Transfer Coefficient has a significant influence on pavement temperature. • Convection Heat Transfer Coefficient is a function of weather conditions and material properties. • Mechanistic-empirical approach can provide more accurate pavement temperature predictions. [ABSTRACT FROM AUTHOR]

This paper presents an infinite series solution to predict the time-dependent temperature profile within the pavement surface layer on the basis of measured pavement surface temperature data during falling weight deflectometer (FWD) testing. The infinite series solution was derived using the method of eigenfunction expansions. The model validation was conducted based on the long-term pavement performance (LTPP) program FWD temperature data. The interpolatory trigonometric polynomials were used to approximate a pavement surface temperature history by using temperatures measured at different times during FWD testing. Total of 2,066 and 1,968 pavement subsurface temperatures within surface layers were predicted by using the derived infinite series solution for flexible and rigid pavements, respectively. These temperature profile predictions were compared with the measured pavement subsurface temperatures, showing that the proposed solution can rapidly and accurately predict the transient temperature profile within the pavement surface layer during the short time period of FWD testing with limited inputs. [ABSTRACT FROM AUTHOR]

This paper presents an analytical solution for prediction of the one-dimensional (1D) time-dependent temperature profile in a multi-layered rigid pavement system. Temperature at any depth in a rigid pavement system can be estimated by using the proposed solution with limited input data, such as pavement layer thicknesses, material thermal properties, measured air temperatures and solar radiation intensities. This temperature prediction problem is modelled as a boundary value problem governed by the classic heat conduction equations, and the air temperatures and solar radiation intensities are considered in the surface boundary condition. Interpolatory trigonometric polynomials, based on the discrete least squares approximation method, are used to fit the measured air temperatures and solar radiation intensities during the time period of interest. The solution technique employs the complex variable approach along with the separation of variables method. A FORTRAN program was coded to implement the proposed 1D analytical solution. Field model validation demonstrates that the proposed solution generates reasonable temperature profile in the concrete slab for a four-layered rigid pavement system during two different time periods of the year. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of mix design factors on the thermal properties of Superpave asphalt concrete. The thermal properties were measured using a guarded testing device (k-alpha device) developed recently at the University of New Brunswick. Two aggregate types, three gradations (determined by the material retained on a 4.75 mm sieve), and four compaction levels (at 35, 70, 100, and 160 gyrations) were investigated. The aggregate was 12.5 mm maximum nominal size with PG58-34 asphalt binder used for all mixes. The findings show that the thermal conductivity of the asphalt concrete ranged from 1.7 to 2.1 W/m·K. The specific heat capacity varied with the aggregate source and ranged from 940 to 2000 J/kg·K. The thermal diffusivity was found to vary with aggregate source. The aggregate type was found to have the most significant effect on the thermal properties. The compaction level had an effect but not a statistically significant one. The range of thermal properties determined in this paper is comparable to that found in the literature. [ABSTRACT FROM AUTHOR]