High-performance self-compacting concrete with recycled coarse aggregate: comprehensive systematic review on mix design parameters

被引:30
作者
Alyaseen, Ahmad [1 ]
Poddar, Arunava [1 ]
Alahmad, Hussain [2 ]
Kumar, Navsal [1 ]
Sihag, Parveen [3 ]
机构
[1] Shoolini Univ, Civil Engn Dept, Solan, India
[2] KTH Royal Inst Technol, Civil & Architectural Engn Dept, Stockholm, Sweden
[3] Chandigarh Univ, Civil Engn Dept, Mohali, India
关键词
Construction & demolition waste; compressive strength; splitting tensile strength; high-performance self-compacting concrete; artificial neural network; sensitivity analysis; HIGH-STRENGTH CONCRETE; MECHANICAL-PROPERTIES; DEMOLITION WASTE; CONSTRUCTION; DURABILITY; OPTIMIZATION; REPLACEMENT; SENSITIVITY; PREDICTION; CHINA;
D O I
10.1080/24705314.2023.2211850
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The technological advancements and environmental concerns enlighten the importance of incorporating more high-performance engineered materials in the construction sector. The partial replacement of natural coarse aggregates (NCA) with recycled coarse aggregates (RCA) in concrete has recently been a primary focus of worldwide researchers for sustainability in environmental aspects. The primary purpose of this review is to comprehend the effect of design parameters in determining the mechanical characteristics of high-performance self-compacting (HP-SCC) that include recycled coarse aggregates (RCA). Seven design parameters were extracted and considered in this review. It has been revealed that the design parameters of HP-SCC with RCA have a different effect on the mechanical characteristics of HP-SCC with various grades. In addition, the current research aims to promote environmental-friendly development and produce sustainable materials to improve mechanical-related characteristics in concrete in the absence of a precise evaluation technique. Artificial neural network (ANN) models have been implemented using the design parameters for predicting concrete mechanical properties based on three statistical indicators. The ANN-based model was attributed using these seven inputs of the literature with the help of sensitivity analysis for indicating the most critical design parameter HP-SCC.
引用
收藏
页码:161 / 178
页数:18
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