通过机器学习评估体现的能量,碳影响和预测精度,用于用经过处理的回收建筑和拆除废弃物聚合物制造的铺路器
Prakhash Neelamegam1, Bhuvaneshwari Muthusubramanian1
1Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, 603203, Tamilnadu, India.
这项研究发现,3%薄膜复合聚胺纤维 (TFC PA) 优化了用回收建筑和拆除废物 (CDW) 制成的混凝土铺路器,减少了体现的能量和碳足迹,同时提高了强度.
科学领域:
- 材料科学 材料科学 材料科学
- 可持续建筑 可持续建筑
- 废物管理 废物管理
背景情况:
- 建筑和拆除废物 (CDW) 构成一个重大处置挑战.
- 回收材料为可持续建筑实践提供了潜力.
- 使用寿命结束的反透膜含有有价值的聚胺纤维.
研究的目的:
- 评估混凝土铺路器的嵌入式能源和碳足迹,这些混凝土铺路器包含回收的CDW.
- 研究使用RO膜中的薄膜复合聚胺纤维 (TFC PA) 作为混凝土添加剂.
- 使用机器学习预测这些新的混凝土铺路器的机械性能.
主要方法:
- 制造具有不同CDW更换百分比的混凝土铺路器.
- 添加来自RO膜的TFCPA来增强混凝土的强度.
- 人工神经网络 (ANN),支持向量回归 (SVR) 和响应表面方法 (RSM) 的应用用于属性预测.
主要成果:
- 对所有CDW替换级别的最佳TFCPA含量确定为3%.
- 这种最佳组合表明体内能量和碳含量减少.
- 与ANN和RSM相比,SVR模型在预测机械性能方面表现优越.
结论:
- 纳入再生CDW和TFCPA是可持续铺路器生产的可行策略.
- 3%的TFCPA添加剂有效地提高了机械性能,同时最大限度地减少了对环境的影响.
- 机器学习,特别是SVR,为材料性能提供了准确的预测能力.
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