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Predicting resilient modulus of recycled concrete and clay masonry blends for pavement applications using soft computing techniques

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Frontiers of Structural and Civil Engineering 2019年第6期13卷 1379-1392页

作者： Mosbeh R.KALOOP Alaa R.GABR Sherif M.EL-BADAWY Ali ARISHA sayed shwally Jong Wan HU Department of Civil and Environmental Engineering Incheon National UniversityIncheon 22012South Korea Incheon Disaster Prevention Research Center Incheon National UniversityIncheon 22012South Korea Department of Public Works and Civil Engineering Mansoura UniversityMansoura 35516Egypt

To date,very few researchers employed the Least Square Support Vector Machine(LSSVM)in predicting the resilient modulus(Mr)of Unbound Granular Materials(UGMs).This paper focused on the development of a LSSVM model to predict the MT of recycled materials for pavement applications and comparison with other different models such as Regression,and Artificial Neural Network(ANN).Blends of Recycled Concrete Aggregate(RCA)with Recycled Clay Masonry(RCM)with proportions of 100/0,90/10,80/20,70/30,55/45,40/60,20/80,and 0/100 by the total aggregate mass were evaluated for use as ***/RCM materials were collected from dumps on the sides of roads around Mansoura city,*** investigated blends were evaluated experimentally by routine and advanced tests and the Mr values were detennined by Repeated Load Triaxial Test(RLTT).Regression,ANN,and LSSVM models were utilized and compared in predicting the Mr of the investigated blends optimizing the best design *** showed that the Mx values of the investigated RCA/RCM blends were generally increased with the decrease in RCM *** analyses were utilized for evaluating the performance of the developed models and the inputs sensitivity ***,the results approved that the LSSVM model can be used as a novel tool to estimate the Mx of the investigated RCA/RCM blends.

关键词： Least Square Support Vector Machine Artificial Neural Network resilient modulus Recycled Concrete Aggregate Recycled Clay Masonry

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