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Developing Resilient Modulus Prediction Models Based on Experimental Results of Crushed Hornfels Mixes with Different Gradations and Plasticity
Mechanistic pavement design procedures of flexible pavement based on elastic-layer theories require the determination or estimation of resilient modulus (MR) for each layer in the pavement structure. Considering the complexity and cost of testing required, regression models have been developed to predict resilient modulus of heavy-duty base crushed rock mixes from their physical and engineering properties. Testing results of MR for three different gradations of the same crushed rock with 15 different fines combinations of crusher dust (CD) and Claypro (CP) have been used to calibrate a universal three-parameter deviatoric stress model. The latter relates resilient modulus to both deviatoric and confining stresses and considers shear stresses and strains developed during loading. Physical properties of these mixes were then used to develop multiple linear regression models to predict the three parameters of the calibrated deviatoric stress model. The modelling process was repeated by including California bearing ratio as a predictor with the physical properties. Both sets of models show the properties considered herein, serve to explain a high variation in the three parameters, ranging between 71 and 91%. However, correlation between measured and predicted MR values from both sets of models is 0.90 and 0.87, respectively. Furthermore, the optimum clay content (CP) for the different mixtures was also assessed. The results indicated that 1% of CP can be considered the optimum content for all three gradations.
Developing Resilient Modulus Prediction Models Based on Experimental Results of Crushed Hornfels Mixes with Different Gradations and Plasticity
Mechanistic pavement design procedures of flexible pavement based on elastic-layer theories require the determination or estimation of resilient modulus (MR) for each layer in the pavement structure. Considering the complexity and cost of testing required, regression models have been developed to predict resilient modulus of heavy-duty base crushed rock mixes from their physical and engineering properties. Testing results of MR for three different gradations of the same crushed rock with 15 different fines combinations of crusher dust (CD) and Claypro (CP) have been used to calibrate a universal three-parameter deviatoric stress model. The latter relates resilient modulus to both deviatoric and confining stresses and considers shear stresses and strains developed during loading. Physical properties of these mixes were then used to develop multiple linear regression models to predict the three parameters of the calibrated deviatoric stress model. The modelling process was repeated by including California bearing ratio as a predictor with the physical properties. Both sets of models show the properties considered herein, serve to explain a high variation in the three parameters, ranging between 71 and 91%. However, correlation between measured and predicted MR values from both sets of models is 0.90 and 0.87, respectively. Furthermore, the optimum clay content (CP) for the different mixtures was also assessed. The results indicated that 1% of CP can be considered the optimum content for all three gradations.
Developing Resilient Modulus Prediction Models Based on Experimental Results of Crushed Hornfels Mixes with Different Gradations and Plasticity
Int. J. Pavement Res. Technol.
Fouad, Ali (author) / Hassan, Rayya (author) / Mahmood, Abdulrahman (author)
International Journal of Pavement Research and Technology ; 15 ; 124-137
2022-01-01
14 pages
Article (Journal)
Electronic Resource
English
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