Application of Support Vector Machine Regression for Predicting Critical Responses of Flexible Pavements
AbstractThis paper aims to assess the application of Support Vector Machine (SVM) regression in order to analysis flexible pavements. To this end, 10000 Four-layer flexible pavement sections consisted of asphalt concrete layer, granular base layer, granular subbase layer, and subgrade soil were analyzed under the effect of standard axle loading using multi-layered elastic theory and pavement critical responses including maximum tensile strain at the bottom of asphalt layer and maximum compressive strain at the top of subgrade soil were calculated. Then the support vector machine regression was used to predict these two critical responses. Results of this study show that the SVM can be used as a reliable tool to predict critical responses of flexible pavements. Analysis of flexible pavements using SVM needs less computing time and the SVM can be used as a quick tool for predicting fatigue and rutting lives of different pavement sections in comparison with multi-layer elastic theory and finite element method.
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