机器学习驱动优化生物医学废弃物灰混凝土,以实现可持续建筑
Christo George1, Sathvik Sharath Chandra1, Prashant M Topalakatti2,3
1Department of Civil Engineering, Dayananda Sagar College of Engineering, Bengaluru, India.
PloS one
|March 13, 2026
概括
生物医学废弃物灰 (BMWA) 混凝土提供了一个可持续的建筑材料. 机器学习模型准确预测其机械性能,支持其用于减少水泥行业对环境的影响.
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.
- 环境科学 环境科学
背景情况:
- 水泥生产对环境污染作出了重大贡献.
- 生物医疗废弃物灰 (BMWA) 为水泥材料提供了一个潜在的可持续替代品.
- 开发可靠的方法来预测BMWA混凝土特性对于其采用至关重要.
研究的目的:
- 从现有文献中开发BMWA混凝土性能数据库.
- 应用先进的机器学习 (ML) 技术来预测BMWA混凝土的机械性能.
- 用一个全面的指数来评估BMWA混凝土的可持续性.
主要方法:
- 编制BMWA混凝土性能数据库.
- 实施和评估四个ML模型:随机森林 (RF),SAINT,TabNet和一个集体模型.
- 超参数优化和十倍交叉验证用于模型性能评估.
- 外部验证和计算可持续性指数 (SI).
主要成果:
- 对于强度参数,RF和TabNet模型实现了最高的预测精度 (R2 = 0.82).
- 圣地显示稳定的概括,但特定强度参数的精度较低.
- 与独立模型相比,整体模型表现不佳,突出了强大的个体模型的有效性.
- 外部验证证实了开发的ML模型的可靠性.
- 15%的替代BMWA混凝土显示出积极的可持续性指标.
结论:
- 提出了一个集成的数据驱动框架,将ML,可解释性和可持续性索引结合起来.
- 该研究验证了BMWA混凝土作为可持续建筑材料的潜力.
- 这种方法通过利用生物医疗废物和减少水泥行业的环境足迹来支持循环经济.
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