Dynamic characterization of recycled glass-recycled concrete blends using experimental analysis and artificial neural network modeling. Issue 142 (March 2021)

• Record Type: Journal Article
• Authors: Ghorbani, Behnam; Arulrajah, Arul; Narsilio, Guillermo; Horpibulsuk, Suksun; Bo, Myint Win
• Is Part Of: Soil dynamics and earthquake engineering. Issue 142(2021)
• Publication Date: 2021-03
• Subjects: Demolition waste -- Pavement base -- Recycled materials -- Repeated load triaxial -- Artificial neural network; Soil dynamics -- Periodicals; Earthquake engineering -- Periodicals; Sols -- Dynamique -- Périodiques; Génie parasismique -- Périodiques; 624.176205
• DOI: 10.1016/j.soildyn.2020.106544
• Languages: English
• ISSNs: 0267-7261

Abstract

Abstract: This study investigates the deformation properties of recycled concrete aggregate (RCA) when blended with up to 70% of recycled glass (RG) for pavement base applications. A multi-stage repeated load triaxial (RLT) testing procedure was proposed and utilized for evaluating the permanent deformation behavior of RCA/RG blends. The resilient modulus (Mr ) of the blends was examined by performing RLT test in different stress combinations using a proposed testing protocol. The shear strength response of the blends was also investigated. Shakedown theory was utilized to classify the permanent deformation behavior of the blends. Except for the RCA30/RG70 blend, all other blends exhibited either Range A or Range B response in the investigated stress levels. There was an increase in the permanent strain and a decrease in the Mr of blends as the RG content increased. The shear response of the blends exhibited a strain-hardening behavior in the post-peak zone when the RG content was more than 10%. Artificial neural network (ANN) models were developed for predicting the deformation properties of the blends and examining the effect of test variables on the deformation properties. The developed ANN models for prediction of permanent strain and Mr were converted to practical equations for pre-design purposes. Results of numerical modeling indicated that ANNs were robust for predicting the deformation properties as well as identifying the impact of input variables on the deformation properties of RCA/RG blends. Highlights: Multistage permanent strain characterization of recycled concrete-recycled glass blends. Shakedown response of the recycled concrete-recycled glass blends as pavement base materials. Developing neural network models for predicting the deformation properties of recycled materials. Resilient modulus and permanent deformation characterization using novel proposed protocols for base and subbase materials.