基于机器学习的预测机械强度的自压缩混凝土,包括电子废弃物聚合物
Saranya Kandasamy1, Ramadevi Kanagaraj2
1Department of Civil Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, 641022, India. ksaranyacivil08@gmail.com.
Environmental science and pollution research international
|March 17, 2026
概括
这项研究调查了在自压缩混凝土 (SCC) 中使用电子废物 (E-waste),发现15%的替代品优化了机械性能和可持续性. 较高的电子废物含量降低了可加工性和强度,但机器学习准确地预测了性能.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 可持续建筑 可持续建筑
背景情况:
- 电子垃圾 (E-waste) 的增长带来了环境挑战.
- 在建筑材料中回收电子废物提供了一个可持续的解决方案.
- 自压缩混凝土 (SCC) 需要特定的特性才能有效使用.
研究的目的:
- 评估使用电子废物作为SCC中部分聚合物的替代品的可行性.
- 确定电子垃圾在机械性能和可加工性方面的最佳百分比.
- 评估SCC强度的机器学习模型的预测精度.
主要方法:
- 13个SCC混合物被制备,电子废物取代了石,每次增加0-30%.
- 测试了新鲜性质 (沉降流量,V道,L盒) 和硬化性质 (压力,拉力,屈曲强度,弹性模量,密度,结合强度).
- 在两点负荷下测试了六个梁;机器学习模型预测了机械强度.
主要成果:
- 最佳的机械性能是在15%的电子废物更换后实现的.
- 可加工性和强度下降,电子废物含量超过15%.
- 对于强度预测,XGBoost模型获得了R2>0.93;15%的电子废物混合物显示,比10%的混合物曲强度增加了12%.
结论:
- 在SCC中15%的电子废物替代增强了可持续性,而不会损害结构完整性.
- 电子废物可以有效地作为SCC中的部分聚合物的替代品.
- 机器学习提供了一种可靠的方法来预测电子废物改性SCC的机械强度.
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