先进和混合机器学习技术用于预测棕油燃料灰改性混凝土的压力强度,使用SHAP分析
Tariq Ali1, Kennedy C Onyelowe2,3, Muhammad Sarmad Mahmood1
1Department of Civil Engineering, Swedish College of Engineering and Technology, Wah, 47080, Pakistan.
Scientific reports
|February 10, 2025
概括
机器学习准确地预测了棕油燃料灰 (POFA) 混凝土的压力强度. 像混合XGB-LGBM这样的先进模型为可持续建筑材料提供了可靠的框架.
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
- 在土木工程中的人工智能.
背景情况:
- 对可持续建筑材料的日益增长的需求需要替代传统水泥.
- 棕油燃料灰 (POFA) 被纳入混凝土中,以减少水泥使用和二氧化碳排放.
- 预测POFA混凝土的压力强度 (CS) 是复杂的,因为有许多可变因素.
研究的目的:
- 为了开发精确的预测模型的压力强度 (CS) 棕油燃料灰 (POFA) 合并混凝土.
- 为此预测任务评估各种先进机器学习模型的性能.
- 使用可解释的AI技术,识别影响POFA混凝土CS的关键因素.
主要方法:
- 收集了407个POFA混凝土样本的数据集,包括水泥含量,POFA剂量,水与粘合物比率,聚合物比率,超级可塑剂含量和固化年龄.
- 包括混合XGB-LGBM,ANN,Bagging,LSSVM,GEP,XGB和LGBM在内的机器学习模型被训练和测试 (70%训练,30%测试).
- 模型性能使用R2,RMSE,NRMSE,MAE和威尔莫特指数 (d) 进行评估;用于特征重要性的SHAP分析.
主要成果:
- 混合XGB-LGBM模型表现出卓越的预测准确性,达到最高的R2 (0.976) 和最低的RMSE.
- 人工神经网络 (ANN) 模型也表现出强的表现,R2为0.968.8.
- SHAP分析发现,水与粘合剂的比率是影响压力强度的最有影响的因素.
结论:
- 先进的机器学习模型,特别是混合XGB-LGBM,提供了一种可靠的方法来预测POFA混凝土的压力强度.
- 这些模型可以减少对广泛实验测试的需求,加速可持续混凝土的开发.
- 这些发现支持使用POFA混凝土作为环保和经济高效的建筑材料.
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