使用机器学习模型对生态友好的混凝土进行基于压力强度的压缩分类
Daniel Alcala-Gonzalez1, Luis F Mateo2,3, M Ángeles Quijano1,3
1Departamento de Ingeniería Civil: Hidráulica, Energía y Medio Ambiente, ETSI Caminos, Canales y Puertos-Edificio Retiro, Universidad Politécnica de Madrid, Alfonso XII, 3, 28014 Madrid, Spain.
Materials (Basel, Switzerland)
|December 11, 2025
概括
机器学习模型使用回收玻璃粉准确地预测了环保混凝土的压力强度. 天真海湾和随机森林表现最好,支持可持续的水泥替代品.
科学领域:
- 材料科学 材料科学 材料科学
- 可持续建筑 可持续建筑
- 人工智能的人工智能
背景情况:
- 准确的压力强度预测对于可持续建筑材料至关重要.
- 用回收玻璃粉代替水泥在材料性能评估中提出了挑战.
- 环保混凝土需要可靠的质量控制和优化方法.
研究的目的:
- 评估和比较五种机器学习模型,用于用回收玻璃粉对混凝土的压力强度进行分类.
- 评估回收玻璃粉作为可持续水泥替代品的可行性.
- 确定最有效的AI算法,以优化低碳混凝土性能.
主要方法:
- 利用了846个实验混凝土样本的数据集,各种混合设计和固化年龄.
- 对比了天真湾,随机森林,决策树,支持向量机 (SVM) 和k-最近邻近 (k-NN) 模型的性能.
- 分析了模型准确性,概括性和可解释性,用于压力强度分类.
主要成果:
- 纯粹的贝叶斯模型和随机森林模型显示出最高的准确性和通用性.
- 再循环玻璃粉的加入并没有导致显著的数据不稳定性.
- 决策树在混合物参数影响方面提供了高的解释性;SVM和k-NN在极端强度类别中表现出色.
结论:
- 概率学和集体学习方法优于确定性和基于近距离的算法来对可变混凝土组成进行分类.
- 人工智能为优化可持续混凝土性能提供了一种可靠,可扩展和非破坏性的工具.
- 回收玻璃粉是一种可行的,可持续的替代传统水泥.
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01:21Impact Strength of Concrete
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