基于增强的集体机器学习模型用于预测地质聚合物稳定粘土土壤的无限制压力强度
Gamil M S Abdullah1, Mahmood Ahmad2,3, Muhammad Babur4
1Department of Civil Engineering, College of Engineering, Najran University, P.O. 1988, Najran, Saudi Arabia.
Scientific reports
|January 28, 2024
概括
新的机器学习模型,梯度增强 (GB) 和自适应增强 (AdaBoost),准确地预测地质聚合物稳定粘土土的无限制压力强度 (UCS). 地面颗粒高炉渣含量是对UCS最有影响的因素.
科学领域:
- 地质技术工程 地质技术工程
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 地质聚合物稳定是一种改善粘土土壤性能的可持续方法.
- 准确预测不受限制的压力强度 (UCS) 对地质技术应用至关重要.
- 现有的UCS预测模型在准确性和可靠性方面存在局限性.
研究的目的:
- 开发和验证用于UCS预测的基于增强组合的新型机器学习模型.
- 为了比较梯度增强 (GB) 和自适应增强 (AdaBoost) 模型与其他已知方法的性能.
- 确定影响地质聚合物稳定粘土土的UCS的关键参数.
主要方法:
- 使用270个地质聚合物稳定粘土土样本的数据集开发和验证GB和AdaBoost模型.
- 数据库的随机划分为80%的培训和20%的测试集.
- 使用R2,MAE,RMSE和MSE等指标评估模型性能.
主要成果:
- GB和AdaBoost模型在预测UCS方面表现出很高的准确性,R2值分别为0.980和0.975.
- 这些模型的表现优于人工神经网络,随机森林,极端梯度增强,多变量回归和多基因遗传编程模型.
- 敏感性分析确定地面颗粒高炉渣含量是影响UCS的最重要因素.
结论:
- 梯度增强和自适应增强是可靠和准确的机器学习模型,用于预测地质聚合物稳定粘土土的UCS.
- 这些模型对UCS预测的现有方法提供了显著的改进.
- 了解地面颗粒高炉渣含量的影响对于优化地质聚合物稳定至关重要.
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