人工神经网络建模用于预测自压缩混凝土的压力强度和静态模量,使用不同百分比的回收混凝土聚合物
Sheetal Thapa1, Shashwati Soumya Pradhan2, Sonu Kumar Gupta3
1Alliance University, Bengaluru, 562106, India.
Environmental science and pollution research international
|August 12, 2025
概括
这项研究表明,在自压缩混凝土 (SCC) 中使用回收混凝土聚合物 (RCA) 保持可操作性并增强压力强度的预测模型. 在印度的Tripura使用RCA促进了可持续性,并减少了建筑垃圾.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 可持续建筑 可持续建筑
背景情况:
- 在印度的特里普拉,正常石材聚合物 (NSA) 的稀缺性需要替代材料.
- 回收混凝土聚合物 (RCA) 提供了一种可持续的解决方案,以减少聚合物的供需差距.
- 建筑垃圾管理和减少碳排放是关键的全球挑战.
研究的目的:
- 调查使用RCA作为自压混凝土 (SCC) 的粗聚合物的可行性.
- 评估RCA对SCC可加工性和机械性能的影响.
- 为了比较人工神经网络 (ANN) 的预测精度和使用RCA的混凝土强度的线性回归.
主要方法:
- 样本被造的不同百分比 (0-100%) 的RCA取代NSA.
- 使用沉,J环和L盒测试来评估可操作性.
- 使用ANN和线性回归模型预测压力强度和静态模量,包括聚合物类型,混凝土等级和年龄.
主要成果:
- 与传统模型相比,基于ANN的模型显著提高了压力强度和静态模量预测的准确性.
- 整合RCA使SCC的可操作性保持在可以接受的范围内.
- 该研究量化了在Tripura使用RCA实现的碳排放减少.
结论:
- 在SCC生产中,RCA是NSA的可行和可持续的替代品.
- 在使用RCA时,ANN模型为混凝土属性提供了卓越的预测能力.
- 在建筑领域采用RCA解决了材料稀缺和环境问题,特别是减少碳足迹.
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