通过机器学习方法对碳酸盐聚合物的工程性质进行石油学控制
Javid Hussain1,2,3,4, Tehseen Zafar5, Xiaodong Fu1,2,3,4
1State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock, and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China.
Scientific reports
|December 31, 2024
概括
这项研究表明,粘土,石,地和多洛米特显著影响碳酸盐聚合物的工程性质. 机器学习,特别是梯度提升,准确地预测这些特性,帮助建筑材料的选择.
科学领域:
- 地质地质地质地质地质地
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 岩石聚合物在建筑中至关重要,工程特性决定了它们的适用性.
- 了解碳酸盐岩石的石化特征与工程性质之间的联系对于有效应用至关重要.
研究的目的:
- 评估石岩学特征对碳酸盐岩的工程性质的影响.
- 通过采用岩石数据建立总体工程行为预测模型.
主要方法:
- 收集并分析了来自巴基斯坦盐山脉的45个碳酸盐岩样本.
- 进行了岩石分析和标准聚合物质量控制测试 (例如,洛杉矶磨损,聚合物粉碎值,不受限制的压力强度).
- 应用统计方法和机器学习模型 (随机森林,梯度提升,多层感知器,分类提升) 用于关系阐明和预测.
主要成果:
- 确定了粘土,石,地和多洛米特作为工程性质的关键决定因素.
- 多重回归得出R2>0.84,但缺乏全面的见解.
- 与其他方法相比,渐变增强模型显示出更高的预测准确性 (r=0.998,R2=0.997).
结论:
- 机器学习技术,特别是梯度提升技术,可以从岩石数据中有效和可靠地预测聚合工程属性.
- 这种方法增强了对总体适度的初步评估,优化了建筑项目的资源配置.
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