نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشیار، دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان
2 دستیار تحقیقاتی، دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The resilient modulus (MR) of road materials is one of the most important parameters in the analysis and design of pavement. This parameter is used in both empirical methods and mechanistic-empirical methods as the main parameter for expressing the stiffness and behavior of road construction materials. To determine this parameter in the laboratory, it is necessary to perform a dynamic tri-axial loading test under various confining and deviator stresses, which is a time- and cost-intensive approach. In this paper, a wavelet neural network (WNN) hybridized with the teacher learning based optimization (TLBO) algorithm was used to model the MR of unbound subbase materials. The input variables included maximum dry density, uniformity coefficient, curvature coefficient, percent passing No. 200 sieve, confining stress, and deviator stress and output variable was resilient modulus of the unbound subbase materials. The results of this study indicate that increasing the number of neurons in the hidden layer to more than 20 neurons has little effect on increasing the accuracy of the wavelet neural network and the Mexican Hat wavelet function has the best result in predicting the resilient modulus. The results of this study also indicate that the WNN-TLBO method is more accurate than the ANN method in predicting the MR of unbound subbase materials. External validation results indicate that the WNN-TLBO method satisfy all the necessary criteria, which indicates the high predictive potential of this method. The results of sensitivity analysis indicate that the degree of importance of the confined stress is higher than other variables for predicting the resilience modulus. A parametric analysis was also done to study the effects of each input variable on the MR.
کلیدواژهها [English]
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