Exploring temperature-resilient recycled aggregate concrete with waste rubber: An experimental and multi-objective optimization analysis

Author:

Tang Yunchao123,Wang Yufei4,Wu Dongxiao1,Chen Mengcheng5,Pang Lan2,Sun Junbo6,Feng Wanhui1,Wang Xiangyu4

Affiliation:

1. College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China

2. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Civil Engineering and Architecture, Guangxi University , Nanning 530004 , China

3. Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, School of Civil Engineering and Architecture, Guangxi University , Nanning , 530004 , China

4. School of Design and Built Environment, Curtin University , Perth , WA 6102 , Australia

5. School of Civil Engineering and Architecture, East China Jiao Tong University , Nanchang 330013 , China

6. Institute for Smart City of Chongqing University in Liyang, Chongqing University , Chongqing , Jiangsu, 213300 , China

Abstract

Abstract For low-carbon sustainability, recycled rubber particles (RPs) and recycled aggregate (RA) could be used to make rubber-modified recycled aggregate concrete (RRAC). The characteristics (compressive strength and peak strain) of RRAC with various amounts of RA and RPs after heating at various temperatures were studied in this work. The results show that high temperatures significantly decreased the uniaxial compressive strength (UCS), whereas the addition of RA (e.g., 50%) and RPs (e.g., 5%) can mitigate the negative effect caused by high temperatures. The peak strain can also be improved by increasing the replacement ratios of RA and RP. Support vector regression (SVR) models were trained using a total of 120 groups of UCS and peak strain experimental datasets, and an SVR-based multi-objective optimization model was proposed. The excellent correlation coefficients (0.9772 for UCS and 0.9412 for peak strain) found to illustrate the remarkable accuracy of the SVR models. The Pareto fronts of a tri-objective mixture optimization design (UCS, strain, and cost) were successfully generated as the decision reference at varying temperature conditions. A sensitivity analysis was performed to rank the importance of the input variables where temperature was found as the most important one. In addition, the replacement ratio of RA is more important compared with that of the RP for both the UCS and strain datasets. Among the mechanical properties of concrete, compressive strength and peak strain are two key properties. This study provides guidance for the study of RRAC constitutive models under high temperatures.

Publisher

Walter de Gruyter GmbH

Subject

Condensed Matter Physics,General Materials Science

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