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

作     者：Mosbeh R.KALOOP Alaa R.GABR Sherif M.EL-BADAWY Ali ARISHA Sayed SHWALLY Jong Wan HU 

作者机构：Department of Civil and Environmental EngineeringIncheon National UniversityIncheon 22012South Korea Incheon Disaster Prevention Research CenterIncheon National UniversityIncheon 22012South Korea Department of Public Works and Civil EngineeringMansoura UniversityMansoura 35516Egypt 

出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿（英文版）)

年 卷 期：2019年第13卷第6期

页      面：1379-1392页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 0711[理学-系统科学] 07[理学] 0813[工学-建筑学] 0814[工学-土木工程] 

基　　金：supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science  ICT & Future Planning 

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

摘      要：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 UGMs.RCA/RCM materials were collected from dumps on the sides of roads around Mansoura city,Egypt.The 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 model.Results showed that the Mx values of the investigated RCA/RCM blends were generally increased with the decrease in RCM proportion.Statistical analyses were utilized for evaluating the performance of the developed models and the inputs sensitivity parameters.Eventually,the results approved that the LSSVM model can be used as a novel tool to estimate the Mx of the investigated RCA/RCM blends.