基于可解释机器学习的混凝土样品压力强度的预测
Wenhu Wang1, Yihui Zhong1, Gang Liao1
1Power China Chengdu Engineering Corporation Ltd., Chengdu 610031, China.
Materials (Basel, Switzerland)
|August 10, 2024
概括
本研究介绍了一种可解释的机器学习模型,用于预测混凝土的压力强度. XGBoost算法展示了卓越的准确性,与已建立的具体材料科学原则保持一致.
科学领域:
- 土木工程 土木工程是指土木工程.
- 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:
- 预测混凝土的压力强度对于结构完整性和建筑质量至关重要.
- 传统方法往往缺乏精度,以前的机器学习模型在解释性方面提出了挑战.
- 开发准确和可理解的预测模型对于推进具体技术至关重要.
研究的目的:
- 开发和验证可解释的机器学习模型,用于预测混凝土的压力强度.
- 为了比较这项预测任务的六种不同的机器学习算法的性能.
- 确保模型的预测是可以理解的,并与已建立的工程原理保持一致.
主要方法:
- 编制了228个混凝土压力强度样本的实验数据库.
- 六个机器学习算法,包括XGBoost,用于构建预测模型.
- 解释性技术应用于表现最好的模型进行深入分析.
主要成果:
- XGBoost模型实现了最高的预测准确性,R平方值为0.982 (训练) 和0.966 (测试).
- 功能重要性和贡献分析证实了该模型与传统的混凝土材料理论保持一致.
- 该模型成功地确定了影响压力强度及其影响的关键因素.
结论:
- XGBoost机器学习模型对于预测混凝土压力强度非常有效和准确.
- XGBoost模型的可解释性提高了其在土木工程中的实际应用性.
- 这种方法克服了先前在建筑材料预测中的"黑子"机器学习模型的局限性.
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