چكيده به لاتين
Asphalt mixes are the dominant type of procedures used in many roads in Iran and the world. These types of procedures have many advantages and disadvantages, which has made their use popular in many dimensions. Asphalt pavements are sensitive to factors such as consumables, weather conditions, traffic load, etc., and the common failures of this pavement, which reduce the useful life of this type of pavement and increase its maintenance costs. It depends on these factors. Fatigue cracking in asphalt mixtures is one of the most common problems in road pavements, microcracks at the interface of asphalt and aggregates propagate into wide interconnected cracks under repeated loads. Asphalt fatigue performance is influenced by the load and also the inherent nature of the asphalt-aggregate system. This type of cracks is known as one of the most important defects of asphalt pavements, and over the past years, different approaches have been used to investigate this failure by different standards and researchers. is placed the main purpose of this study is to collect information regarding the recognition and prediction of fatigue cracks with different types of methods; This has been done using machine learning methods. Machine learning is a technology that, using algorithms, allows computers to analyze data and perform other tasks that are similar to the human learning method, and has the ability to be accurate and improved by the algorithms themselves. The purpose of this research is to develop a model of machine learning methods to predict fatigue cracks in asphalt pavement; It has been done using multiple linear regression modeling, artificial neural network, random forest, gradient boosting decision tree. By comparing these methods, it was determined that the random forest method with an R^2 value of 0.7 for the selected variables, for the single state of Alaska (cold), the random forest model with an R^2 value of 0.83, the state of Hawaii (tropical) is a regression model. Linear with an R^2 value of 0.78, the performance category of the gradient amplification model with an R^2 value of 0.69, rain-heavy areas with a value of 0.67, linear regression and traffic-heavy areas with a value of 0.71, forest for the average intensities, the best performance It predicts fatigue.
