使用先进的混合机器学习模型,估计海洋混凝土的表面化物度
Irfan Ullah1, Hisham Alabduljabbar2, Muhammad Faisal Javed3,4
1Department of Civil and Transportation Engineering, Hohai University, Nanjing, China. 19pwciv5205@uetpeshawar.edu.pk.
Scientific reports
|November 18, 2025
概括
这项研究开发了先进的混合机器学习 (ML) 模型,以准确估计海洋混凝土中的表面化物度,减少对物理测试的需求. 支持向量回归与灰狼优化 (SVR-GWO) 模型表现最好.
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:
- 海洋混凝土结构因化物进入而面临退化.
- 精确估计表面化物度 (CC) 对于预测混凝土的耐用性至关重要.
- 传统的CC评估方法是劳动密集型和昂贵的.
研究的目的:
- 开发和验证混合机器学习 (ML) 模型,用于估计海洋混凝土中的表面化物度 (CC).
- 提高CC预测模型的准确性和可解释性.
- 为CC评估提供物理实验的可靠替代方案.
主要方法:
- 通过结合人工神经网络 (ANN) 和支持向量回归 (SVR) 与元启发式优化算法 (灰狼优化 - GWO和大猩猩部队优化 - GTO) 开发了四种混合ML模型.
- 使用了部分依赖图和夏普利增量解释 (SHAP) 值来解释模型的解释性.
- 经过训练和验证的模型使用各种文献来源的数据集.
主要成果:
- 所有开发的混合ML模型都在估计表面CC方面表现出强的表现.
- SVR-GWO模型实现了最高的确定系数 (R2 = 0.96),优于其他模型.
- 细聚合物被确定为最有影响力的特征,其次是暴露类型,暴露时间和含量.
结论:
- 混合ML模型,特别是SVR-GWO,为估计海洋混凝土表面化物度提供了高度准确和可靠的方法.
- 这些模型提供了对关键影响因素的宝贵见解,增强了对化物进入机制的理解.
- 为了实际应用CC预测模型,开发了一个用户界面.
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