整合孔隙/粘合剂指数和机器学习方法来模拟水泥土的强度和刚度
Jair De Jesús Arrieta Baldovino1, Oscar E Coronado-Hernandez2, Yamid E Nuñez de la Rosa3
1Department of Civil Engineering, Universidad de Cartagena, Cartagena de Indias 130015, Colombia.
Materials (Basel, Switzerland)
|December 31, 2025
概括
这项研究表明,将碎聚合物残留物 (CAR) 与水泥相结合,可以提高土壤的强度和刚性. 整合毛孔结合剂指数和机器学习 (ML) 准确预测土壤机械性能,以更好地设计混合物.
科学领域:
- 地质技术工程 地质技术工程
- 材料科学 材料科学 材料科学
背景情况:
- 土壤稳定对于基础设施发展至关重要.
- 使用废物,如压碎聚合物残留物 (CAR),提供可持续的解决方案.
- 波特兰水泥是土壤改善的常见粘合剂.
研究的目的:
- 评估用CAR和波特兰水泥稳定细粒度土壤的机械性能.
- 开发土壤强度和刚度的预测模型,使用性-粘合剂指数 (η/Civ) 和机器学习 (ML).
- 评估CAR,水泥, η/Civ和ML在土壤稳定中的联合有效性.
主要方法:
- 对土壤-水泥-CAR混合物的实验室测试,以获得不受限制的压力强度 (qu) 和小应变剪切模量 (Go).
- CAR (15%,30%),水泥 (3%,6%) 和压缩密度的系统变化.
- 应用性结合剂指数 (η/Civ) 和ML技术用于预测建模.
主要成果:
- 增加的CAR和水泥含量显著提高了土壤的强度和刚性.
- 孔隙结合剂指数与qu (R2 = 0.94) 和Go (R2 = 0.92) 显示出强烈的相关性.
- 机器学习模型为qu (R2 = 0.99) 和Go (R2 = 0.97) 实现了高预测准确度,单独超过了指数.
结论:
- 碳酸盐和水泥的组合有效地改善了细粒度土壤的机械性能.
- 孔隙结合剂指数为预测土壤行为提供了可靠的基础.
- 将ML与 η/Civ指数集成,为土壤-水泥-CAR混合物设计和性能预测提供了强大而准确的框架.
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