使用回收聚合物的自我压缩混凝土,基于梯度增强回归树的压力强度预测,使用贝叶斯优化混合模型的贝叶斯优化模型
Emad A Abood1, Zainab Abdulrdha Thoeny2, Noralhuda M Azize1
1Department of Material Engineering, College of Engineering, Al-Shatrah University, Al-Shatrah, 64007, Iraq.
Scientific reports
|August 1, 2025
概括
一个新的混合机器学习模型准确地预测了使用回收聚合物的自压缩混凝土 (SCC) 的28天压力强度. 这种具有成本效益的工具为可持续建筑提供了传统实验室测试的替代方案.
科学领域:
- 材料科学与工程 材料科学与工程
- 土木工程 土木工程是指土木工程.
- 计算智能是一种计算智能.
背景情况:
- 自压缩混凝土 (SCC) 需要高可加工性,在密集钢筋或复杂的成型中构成挑战.
- 估计SCC的28天压力强度,特别是在可持续性回收聚合物的情况下,依赖于耗时的实验室测试.
- 在混凝土混合物中的回收聚合物促进环保和可持续的建筑实践.
研究的目的:
- 开发和评估一种新的混合机器学习模型,以准确有效地估计含有回收聚合物的SCC的28天压力强度.
- 为SCC强度预测提供了传统实验室测试方法的成本效益和更快的替代方案.
- 通过可访问的决策支持工具,加强现实建筑项目的混合设计优化和质量控制.
主要方法:
- 开发了一种混合梯度增强回归树 (GBRT) 模型,与贝叶斯优化集成.
- 该模型的性能使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和R平方 ([公式:见文本]) 进行了评估.
- 使用SHAP (沙普利增量解释) 来解释黑子模型并确定预测因素的重要性.
主要成果:
- 混合型GBRT模型在五倍交叉验证中实现了平均RMSE6.000,MAE3.968和R平方0.806.
- 与支持向量回归 (SVR) 和K-Nearest Neighbors (KNN) 等单个学习者模型相比,开发的模型显示出更高的预测准确性和稳定性.
- SHAP分析揭示了影响压力强度及其趋势的关键预测因素,有助于模型的解释性.
结论:
- 开发的混合型GBRT模型是一个准确,快速和经济的替代品,用于预测使用回收聚合物的自压缩混凝土的28天压力强度.
- 该模型的强大预测能力和稳定性突出显示了其替代传统实验室测试的潜力.
- 创建了一个易于使用的图形界面,为土木工程师和混合设计和质量控制从业人员提供了宝贵的决策支持工具.
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