使用机器学习工具加强生态混凝土的压力强度的评估
Houcine Bentegri1, Mohamed Rabehi1, Samir Kherfane1
1Civil Engineering and Sustainable Development Laboratory, University of Djelfa, PO, Box 3117, 17000, Djelfa, Algeria.
Scientific reports
|February 11, 2025
概括
预测压缩地球块 (CEB) 的压力强度是复杂的. 使用PyCaret和Extra Trees Regressor的自动机器学习提供了一种高效和准确的方法来预测CEB强度,其性能优于传统测试.
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.
- 计算科学 计算科学
背景情况:
- 压缩土块 (CEB) 的压缩强度预测因水泥,粘土,沙子,泥和纤维等组件之间的非线性相互作用而复杂.
- 传统的CEB强度实验室测试资源密集,耗时,需要更有效的预测方法.
研究的目的:
- 开发和评估用于预测CEB的压力强度的机器学习模型.
- 评估自动机器学习平台PyCaret在模拟复杂材料行为的有效性.
- 确定最佳的机器学习算法,以准确预测CEB强度.
主要方法:
- 利用PyCaret,一个自动化的机器学习平台,来构建和比较各种预测模型.
- 测试了多个机器学习算法,包括额外树木回归器.
- 使用R平方 (R2),根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 等指标评估模型性能.
主要成果:
- 纤维含量与水泥含量呈现强烈的正相关性 (0.9444),显著影响压力强度.
- 额外树木回归器获得了最高的精度,R2 = 0.9444,RMSE = 0.4909,MAE = 0.1899.
- PyCaret成功地自动化了复杂材料强度预测的机器学习工作流.
结论:
- 自动机器学习,特别是PyCaret和Extra Trees Regressor,为预测CEB压力强度的传统方法提供了一种高效可靠的替代方案.
- 额外树木回归器处理非线性和多变量数据的能力使其非常适合此应用.
- 这种方法为工程师和材料科学研究人员提供了有价值的工具,提高了对CEB强度的预测.
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