基于直角实验的EPS轻质结构混凝土混合设计和性能预测
1School of Civil Engineering and Architecture, Zhejiang Guangsha Vocational and Technical University of construction, Dongyang, 322100, Zhejiang Province, China. zqhsgdmn@126.com.
Scientific reports
|July 2, 2025
概括
这项研究优化了扩展聚乙烯 (EPS) 轻质混凝土混合物比例,以提高隔热和抗性能. 最好的组合使用机器学习预测平衡机械性能和性能.
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.
- 复合材料 复合材料 复合材料
背景情况:
- 轻质混凝土提供了更好的隔热,但往往会损害机械强度.
- 优化混合比例对于平衡性能特征至关重要.
- 像扩展聚钢 (EPS) 和聚氧甲 (POM) 纤维这样的新材料为先进的混凝土配方提供了机会.
研究的目的:
- 系统地研究EPS含量,水与粘合剂比率和POM纤维含量对轻质混凝土机械和热性能的影响.
- 开发和验证用于预测EPS轻质结构混凝土性能的机器学习模型.
- 为了确定一个最佳的混合比例,在机械强度,导热率和抗性之间实现平衡.
主要方法:
- 使用直角实验来有效地研究关键混合参数的影响.
- 为训练和测试机器学习模型创建了一个新的数据集.
- 用XGBoost算法进行性能预测,该算法由海优化算法 (SOA),鱼优化算法 (WOA) 和粒子群优化 (PSO) 进行了优化.
主要成果:
- 含有EPS的材料显著提高了保温和抗性能,但以牺牲了机械强度为代价.
- 通过减轻裂纹传播,POM纤维增强了抗拉强度和抗性.
- SOA-XGBoost模型显示出更高的预测准确性和稳定性,用于具体的性能.
- 确定了35%EPS,0.21水与粘合剂比率和0.65%POM纤维含量的最佳混合比例.
结论:
- 该研究成功地确定了EPS轻质结构混凝土的最佳混合比例,平衡了机械和热性能.
- 开发的机器学习框架,特别是SOA-XGBoost,提供了一种可靠的方法来预测混凝土的特性.
- 这些发现为设计先进的轻质混凝土提供了宝贵的见解,并作为工程中的其他复合材料的参考.
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