Abstract
Strength and fatigue life are essential parameters of pavement structure design. To accurately determine the pavement structure resistance of rubber asphalt mixture, the strength tests at various temperatures, loading rate, and fatigue tests at different stress levels were conducted in this research. Based on the proposed experiments, the change law of rubber asphalt mixture strength with different temperatures and loading rates was revealed. The phenomenological fatigue equation of rubber asphalt mixture was established. The genetic algorithm optimized backpropagation neural network (GA-BPNN) is highly reliable for optimizing production processes in civil engineering, and it has a remarkable application effect. A GA-BPNN strength and fatigue life prediction model was created in this study. The reliability of the prediction model was verified through experiments. The results showed that the rubber asphalt mixture strength decreases and increases with the increase of temperature and loading rate, respectively. The goodness of fit of the rubber asphalt mixture strength and fatigue life prediction model based on the GA-BPNN could reach 0.989 and 0.998, respectively. The indicators of the fatigue life prediction model are superior to the conventional phenomenological fatigue equation model. The GA-BPNN provides an effective method for predicting the rubber asphalt mixture strength and fatigue life, which significantly improves the accuracy of the resistance design of the rubber asphalt pavement structure.
Highlights
Rubber asphalt mixture has excellent fatigue resistance and water stability, which is an excellent choice for road engineering [1,2,3,4]
Since the center point of the specimen in the indirect tensile test is in transverse tension and longitudinal compression, which is more consistent with the actual stress state of the asphalt pavement longitudinal compression, which is more consistent with the actual stress state of the asphalt structure, the indirect tensile method was selected to study the strength and fatigue properties of pavement structure, the indirect tensile method was selected to study the strength and fatigue rubber asphalt mixture in this research
The prediction model of the strength and fatigue life of rubber asphalt mixture is established by using the genetic algorithm optimized backpropagation neural network (GA-backpropagation neural network (BPNN))
Summary
Rubber asphalt mixture has excellent fatigue resistance and water stability, which is an excellent choice for road engineering [1,2,3,4]. The. Materials 2020, 13, 3325 strength, stiffness, and fatigue parameters are essential parameters in calculating the load response and establishing the mechanical model, which plays a vital role in the design of the pavement structure. The factors that affect the fatigue performance of asphalt mixture mainly include the test method, material factor, loading frequency, test temperature, etc. Considering the above reasons, GA-BPNN will be used to forecast the strength and fatigue life of rubber asphalt mixture in this research. The accurate prediction of the rubber asphalt mixture strength and fatigue life is essential for ensuring the scientific and reasonable anti-fatigue design of rubber asphalt pavement structures. The prediction model of rubber asphalt mixture strength and fatigue life is created based on a genetic algorithm optimized backpropagation neural network (GA-BPNN). In addition to the training data, the strength and fatigue tests are carried out to verify the feasibility of the model
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