再生粗骨料高性能自密实混凝土:配合比设计参数的综合系统综述

IF 3 Q2 ENGINEERING, CIVIL
A. Alyaseen, Arunava Poddar, Hussain Alahmad, Navsal Kumar, P. Sihag
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引用次数: 4

摘要

摘要技术进步和环境问题启发了在建筑行业引入更多高性能工程材料的重要性。最近,用再生粗骨料(RCA)部分取代混凝土中的天然粗骨料(NCA)已成为世界各地研究人员在环境方面可持续性的主要焦点。本综述的主要目的是了解设计参数在确定包括再生粗骨料(RCA)在内的高性能自密实(HP-SCC)的机械特性方面的影响。本次审查中提取并考虑了七个设计参数。研究表明,具有RCA的HP-SCC的设计参数对不同等级的HP-SCC的力学特性有不同的影响。此外,目前的研究旨在促进环境友好发展,生产可持续材料,以在缺乏精确评估技术的情况下改善混凝土的力学相关特性。基于三个统计指标,利用设计参数建立了用于预测混凝土力学性能的人工神经网络(ANN)模型。在灵敏度分析的帮助下,使用文献的这七个输入对基于ANN的模型进行了归因,以指示最关键的设计参数HP-SCC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-performance self-compacting concrete with recycled coarse aggregate: comprehensive systematic review on mix design parameters
ABSTRACT 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.
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来源期刊
CiteScore
3.90
自引率
9.50%
发文量
24
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