In this paper,a novel method for the design of flexible pavements is elaborated.The method is based on fuzzy inference system with genetic algorithm(GA)aided optimized rule base.The model is founded on layered fuzzy a...In this paper,a novel method for the design of flexible pavements is elaborated.The method is based on fuzzy inference system with genetic algorithm(GA)aided optimized rule base.The model is founded on layered fuzzy antecedent and consequent conjunctive rules.The data for the model consists of 300 flexible pavement design instances that breaks up in to 25%of the data drawn from research and real field applications and 75%of data generated in spread sheets compliant with Indian road congress(IRC)code guidelines.In the first step,the inputs and outputs were fuzzified and around 110 rules were generated using training data set.GA was implemented to find optimal and a compact rule set.GA was able to garner 35 rules that are adequate to predict the thickness of base course,sub base and surface course with high accuracy.The model with optimized rules was val idated using test data set.The results of the evaluation are encouraging with low values of RMSE ranging between 3.6–11 for GSB,binder course(BC)and surface course(SC).The coefficient of determination is also high and between 0.85–0.90 indicating accuracy in pre diction.Correlation coefficient values stood at an average of 0.92 indicating closeness between predicted and actual values of thickness of courses.展开更多
文摘In this paper,a novel method for the design of flexible pavements is elaborated.The method is based on fuzzy inference system with genetic algorithm(GA)aided optimized rule base.The model is founded on layered fuzzy antecedent and consequent conjunctive rules.The data for the model consists of 300 flexible pavement design instances that breaks up in to 25%of the data drawn from research and real field applications and 75%of data generated in spread sheets compliant with Indian road congress(IRC)code guidelines.In the first step,the inputs and outputs were fuzzified and around 110 rules were generated using training data set.GA was implemented to find optimal and a compact rule set.GA was able to garner 35 rules that are adequate to predict the thickness of base course,sub base and surface course with high accuracy.The model with optimized rules was val idated using test data set.The results of the evaluation are encouraging with low values of RMSE ranging between 3.6–11 for GSB,binder course(BC)and surface course(SC).The coefficient of determination is also high and between 0.85–0.90 indicating accuracy in pre diction.Correlation coefficient values stood at an average of 0.92 indicating closeness between predicted and actual values of thickness of courses.
