The performance of bituminous materials is mainly affected by the prevailing maximum and minimum temperatures, and their mechanical properties can vary significantly with the magnitude of the temperature changes. The given effect can be observed from changes occurring in the bitumen or asphalt mixture stiffness and the materials’ serviceable life. Furthermore, when asphalt pavement layer are used, the temperature changes can be credited to climatic factors such as air temperature, solar radiation and wind. Thus in relevance to the discussed issue, the contents of this paper displays a comprehensive review of the collected existing 38 prediction models and broadly classifies them into their corresponding numerical, analytical and statistical models. These models further present different formulas based on the climate, environment, and methods of data collection and analyses. Corresponding to which, most models provide reasonable predictions for both minimum and maximum pavement temperatures. Some models can even predict the temperature of asphalt pavement layers on an hourly or daily basis using the provided statistical method. The analytical models can provide straight-forward solutions, but assumptions on boundary conditions should be simplified. Critical climatic and pavement factors influencing the accuracy of predicting temperature were examined. This paper recommends future studies involving coupled heat transfer model for the pavement and the environment, particularly consider to be made on the impact of surface water and temperature of pavements in urban areas.

Air temperature is one of the critical factors influencing the bearing ability and performance of temperature-sensitive asphalt materials. This research investigates the relationship between air temperature at different depths and time to predict asphalt pavement temperature and evaluate asphalt performance. This paper discusses four deep learning-based regression models for calculating asphalt pavement temperature based on air temperature, depth from the asphalt surface, and time. Measurement of pavement temperature was made in the Gaza Strip. Monitoring stations were set up to measure asphalt pavement temperature and air temperature at different depths and times. The data were collected by hand measurement for the period from March 2012 to February 2013. The data is trained and validated using the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU). Bi-LSTM has an R 2 of 0.9555 for the generated dataset and outperforms other algorithms because of its superiority in feature extraction and multidimensional data processing. Through deep learning techniques, Bi-LSTM has demonstrated outstanding robustness and promising potential in predicting asphalt pavement temperature.

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The feasibility of utilizing waste material for road construction is encouraging as it can decrease waste material harmful to the environment. Hence, a more sustainable method and a meticulous study of the available admixtures utilized to substitute standard asphalt binders with waste material must be conducted. However, there are several concerns and doubts about the real situation arising from the chemical and physical traits, as well as the mechanical performance issuing from the integration of waste material within the asphalt pavement to alleviate roads surface's permanent deformation. This investigation was carried out to study physical improvements made on ACW-14 bitumen by adding waste Polyethylene Terephthalate (PET) to serve as a partial replacement for bitumen content compared to normal, conventional 80/100 bitumen physical and rheological behavior. PET percentage added to the bitumen content was 10%, 8%, 6%, 4% and 2% of optimum bitumen content weight. The outcomes concluded that the best performance of bitumen on its density, VTM, VFB, flow, stability, and stiffness was achieved when 5.8% of Optimum Modified-Bitumen Content using PET. All the results obtained have been compared according to JKR Standards results, and the conclusion has fulfilled these requirements.