Abstract
This paper is aimed to solve the overlearning problem of the neural network algorithm used to calculate the asphalt concrete pavement structural modulus in reverse. The firefly algorithm was adapted to optimize the selection of support vector machine (SVM) parameters. Based on the optimized SVM model, a new method for dynamic inversion of the semirigid base asphalt concrete pavement structural layer modulus was presented. The results show that the absolute value of relative error of each layer modulus is not more than 3.73% by using the proposed method. Then, the influences of temperature and humidity on the inversion modulus of semirigid base asphalt concrete pavement in the seasonal frozen area were analyzed, and the correction formula of the inversion modulus was established. The paper is of practical significance for improving the safety performance of semirigid base pavement in the seasonal frozen area in China.
Highlights
In recent years, China’s highway construction has developed rapidly in terms of construction scale and quantity. e highway has reached a total mileage of 5 million kilometers by 2019, including 143,000 km of expressways. e semirigid base asphalt concrete pavement is most widely used in expressways [1]
Wang developed a dynamic inversion program integrated with the artificial neural network and genetic algorithm (ANN-GA) to calculate the modulus of the structural layer
Erefore, this paper introduces the support vector machine (SVM) method, optimizes the inversion of the asphalt concrete pavement structural layer modulus by the firefly algorithm considering the effect of temperature and humidity, and analyzes the influence of temperature and humidity on the inversion modulus of semirigid base asphalt pavement in the seasonal frozen area
Summary
China’s highway construction has developed rapidly in terms of construction scale and quantity. e highway has reached a total mileage of 5 million kilometers by 2019, including 143,000 km of expressways. e semirigid base asphalt concrete pavement is most widely used in expressways [1]. Analyzing the influence of initial degree of compaction, freeze-thaw cycle times, and water content on modulus of resilience, he revealed the change law of modulus of resilience and gave the modulus reduction coefficient of subgrade in a seasonal frozen area [17]. Ere is no relevant research on the influence of temperature and humidity in the seasonal frozen area on the inversion modulus of the semirigid base pavement structure layer. Erefore, this paper introduces the SVM method, optimizes the inversion of the asphalt concrete pavement structural layer modulus by the firefly algorithm considering the effect of temperature and humidity, and analyzes the influence of temperature and humidity on the inversion modulus of semirigid base asphalt pavement in the seasonal frozen area. E research results have guiding significance to ensure the safety performance of the asphalt concrete pavement structure in the seasonal frozen area and extend its service cycle
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