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一种新的MBAS-RF方法来预测基于地聚合物的组合物的机械性质

Shuzhao Chen¹, Mengmeng Zhou¹, Xuyang Shi¹

¹School of Mines, China University of Mining and Technology, Xuzhou 221116, China.

Gels (Basel, Switzerland)

|June 27, 2023

概括

这项研究引入了一种混合机器学习模型来预测地质聚合物混凝土的压力强度 (CS),从而减少了昂贵的测试. 该模型实现了高精度,确定了NaOH度作为一个关键因素.

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.
人工智能的人工智能

背景情况:

地质聚合物混凝土为传统混凝土提供了一个可持续的替代方案,与绿色行业倡议保持一致.
准确预测地聚合物混凝土的压力强度 (CS) 是至关重要的,但传统上涉及昂贵和耗时的测试.
开发高效的预测模型对于推进地质聚合物混凝土的使用至关重要.

研究的目的:

开发一种混合机器学习模型,用于准确预测地聚合物混凝土的压力强度 (CS).
使用修改后的甲虫天线搜索 (MBAS) 算法来优化随机森林 (RF) 模型的超参数.
确定影响地质聚合物混凝土CS的关键参数.

主要方法:

采用了混合机器学习方法,结合了修改的甲虫天线搜索 (MBAS) 算法和随机森林 (RF) 算法.
使用MBAS算法对射频模型的超参数进行了微调.
模型性能使用10倍交叉验证 (10倍CV),相关系数 (R) 和根平均平方误差 (RMSE) 进行验证,并与其他模型进行比较.

主要成果:

混合型MBAS-RF模型显示出高预测准确度,在训练组中达到0.9162的R值,在测试组中达到0.9071的R值.
低RMSE值 (培训为7.111,测试为7.4345) 证实了模型的可靠性.
确定NaOH度是影响CS最重要的参数 (重要性得分:3.7848),而4/10毫米聚合物等级是最不重要的 (重要性得分:0.5667).

结论:

混合型MBAS-RF机器学习模型有效地预测了高精度的地聚合物混凝土压力强度.
对于材料科学应用,MBAS算法在优化射频模型超参数方面非常有效.
酸的度是控制地质聚合物混凝土压力强度的关键因素.

关键词:

压力强度压力强度的压力强度.混凝土混凝土混凝土是什么凝是一种凝.混合机器学习模型的混合机器学习模型.

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