机器学习驱动的超高性能混凝土的强度预测和可持续性分析
Hongliang Rong1, Wangwen Sun2, Haoran Ma2
1Xinjiang Jiaotou Construction Management Co., Ltd., Urumchi 830000, China.
Materials (Basel, Switzerland)
|November 27, 2025
概括
机器学习模型准确地预测了超高性能混凝土 (UHPC) 的压力强度. 该研究指导设计可持续的,低碳的UHPC,以最佳的性能和环境影响.
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.
- 计算力学 计算力学 计算力学
背景情况:
- 超高性能混凝土 (UHPC) 提供了卓越的强度和耐久性.
- 高昂的材料和环境成本阻碍了广泛采用超高压电池.
- 准确的压力强度预测对于具有成本效益和优化的UHPC设计至关重要.
研究的目的:
- 评估用于预测UHPC压力强度的机器学习模型.
- 确定影响超高压电压强度的关键因素.
- 评估UHPC性能与环境影响之间的权衡.
主要方法:
- 从已发表的文献中编制了一个800个样本的数据集.
- 应用了四种机器学习模型:随机森林 (RF),高斯过程回归 (GPR),梯度增强 (GB) 和人工神经网络 (ANN).
- 利用回归错误特征 (REC) 曲线和环境影响指数 (EII) 进行全面评估.
主要成果:
- 在强度预测方面,ANN和GPR模型表现出最高的准确性和稳定性.
- 固化年龄和水泥含量是最重要的预测因素,占影响的65%以上.
- 压力强度范围为150-250MPa,在性能和可持续性之间提供了最佳的平衡.
结论:
- 机器学习模型是预测UHPC压力强度的强大工具.
- 为开发绿色和低碳UHPC配方提供了指导.
- 优化UHPC设计需要平衡机械性能与环境考虑.
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