优化机器学习模型,通过多面性比较分析预测活性混凝土的压力强度
Guo-Hua Fang1, Zhong-Ming Lin1, Cheng-Zhi Xie2
1CCC-FHDI Engineering Corp., Ltd., Guangzhou 510290, China.
Materials (Basel, Switzerland)
|October 26, 2024
概括
机器学习使用工业副产品准确预测活性混凝土 (AAC) 的强度. 优化的XGBoost模型实现了高精度,为传统混凝土提供了可靠的替代方案,以减少碳排放.
科学领域:
- 材料科学与工程 材料科学与工程
- 可持续的建筑材料 可持续的建筑材料
- 计算材料科学科学 计算材料科学
背景情况:
- 活性混凝土 (AAC) 使用工业副产品 (飞,渣) 作为波特兰水泥的可持续替代品,大大减少碳排放.
- 由于配方的可变性,预测AAC压力强度具有挑战性,这阻碍了广泛采用.
- 准确的强度预测对于可靠的性能和材料优化至关重要.
研究的目的:
- 开发一个强大的机器学习 (ML) 模型,准确预测活性混凝土 (AAC) 的压力强度.
- 为了确定最佳的输入变量方案和AAC强度预测的机器学习算法.
- 为了提高模型的可解释性,并提供对AAC配方-强度关系的见解.
主要方法:
- 策划了1756种独特的AAC混合物的广泛数据集.
- 评估了四个输入变量方案并比较了ML算法:随机森林,AdaBoost,梯度增强回归树和极端梯度增强 (XGBoost).
- 使用灰狼优化器 (GWO),鱼优化算法 (WOA),甲虫天线搜索 (BAS) 和贝叶斯优化 (BO) 优化了性能最好的XGBoost模型.
- 应用了SHapely添加式解释 (SHAP) 来实现模型的可解释性.
主要成果:
- XGBoost模型在其他ML算法上表现出卓越的性能.
- 优化的XGBoost在训练集上实现了0.99的确定系数 (R2),在整个数据集上达到0.94.
- SHAP分析提供了各种输入参数对AAC压力强度的影响的见解.
结论:
- 结合仔细的输入选择,超参数优化和模型可解释性的协同方法显著提高了AAC强度预测的准确性.
- 优化的ML模型为预测AAC性能提供了强大而可扩展的解决方案.
- 这项研究证实了ML在推进活性混凝土等可持续建筑材料方面的潜力.
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