机器学习和交互式GUI用于混凝土压力强度预测.
Mohamed Kamel Elshaarawy1, Mostafa M Alsaadawi2,3, Abdelrahman Kamal Hamed1
1Civil Engineering Department, Faculty of Engineering, Horus University-Egypt, New Damietta, 34517, Egypt.
Scientific reports
|July 19, 2024
概括
机器学习模型使用八个输入参数准确预测混凝土的压力强度 (CS). 分类梯度提升 (CatBoost) 模型获得了最高的准确性,混凝土年龄是最有影响力的因素.
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
- 计算科学 计算科学
背景情况:
- 混凝土的压力强度 (CS) 对结构完整性和设计至关重要.
- 准确的CS预测优化了材料使用,降低了成本,并最大限度地减少了试错.
- 机器学习 (ML) 为复杂的工程预测提供先进的解决方案,如CS.
研究的目的:
- 通过使用各种ML模型,提高混凝土压力强度预测.
- 分析八个关键输入参数对CS的影响.
- 为高效的CS估计开发一个实用的工具.
主要方法:
- 利用了1030个实验CS数据点用于训练和测试ML模型.
- 实现了非整体 (回归,进化,神经网络,模糊推理系统) 和整体 (自适应提升,随机森林,梯度提升) ML模型.
- 通过使用Shapley-Additive-explanations (SHAP) 和k-fold交叉验证进行敏感性分析进行模型评估.
主要成果:
- 分类梯度提升 (CatBoost) 模型表现出优异的预测性能.
- CatBoost实现了0.966的确定系数 (R2) 和3.06 MPa的根平均平方误差 (RMSE).
- 根据SHAP分析,具体年龄是影响CS预测的最重要因素.
结论:
- 机器学习模型,特别是CatBoost,提供高度准确的CS预测.
- 混凝土的年龄是压力强度的主要驱动因素.
- 图形用户界面 (GUI) 可以为设计师提供快速和成本效益的CS预测.
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