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Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials
Computers & Structures ( IF 4.4 ) Pub Date : 2025-01-06 , DOI: 10.1016/j.compstruc.2025.107644
Abdelrahman Kamal Hamed, Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi
Computers & Structures ( IF 4.4 ) Pub Date : 2025-01-06 , DOI: 10.1016/j.compstruc.2025.107644
Abdelrahman Kamal Hamed, Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi
Compressive strength is a key factor in the design and durability of concrete structures. Accurate prediction of compressive strength helps optimize material use and reduce construction costs. This study proposes a novel stacked model for predicting compressive strength, integrating three base models with linear regression. The base models include Artificial Neural Networks, Random Forest, and Extreme Gradient Boosting, while the stacked model uses Linear Regression as the metamodel. A dataset of 1,030 concrete mix samples covering eight critical input parameters, including cement, blast furnace slag, coarse aggregates, fine aggregates, fly ash, water, superplasticizer, and curing days, was used for training and evaluation. The dataset was split into training (80%), validation (10%), and testing (10%) subsets. The models were trained independently, and their predictions were used to develop the stacked model. Among the base models, the Extreme Gradient Boosting model achieved the highest accuracy, with an R2 of 0.947 during testing. However, the stacked model outperformed it, attaining an R2 of 0.953 in the testing phase. Shapley additive explanations analysis identified curing duration as the most influential factor in compressive strength prediction. A user-friendly graphical interface was developed to facilitate efficient prediction of compressive strength in concrete structures.
中文翻译:
基于堆叠的机器学习预测混凝土材料的单轴抗压强度
抗压强度是混凝土结构设计和耐久性的关键因素。准确预测抗压强度有助于优化材料使用并降低施工成本。本研究提出了一种新的堆叠模型来预测抗压强度,将三个基本模型与线性回归相结合。基本模型包括人工神经网络、随机森林和极端梯度提升,而堆叠模型使用线性回归作为元模型。使用 1,030 个混凝土混合样品的数据集进行培训和评估,涵盖 8 个关键输入参数,包括水泥、高炉矿渣、粗骨料、细骨料、粉煤灰、水、高效减水剂和养护天数。该数据集分为训练 (80%) 、验证 (10%) 和测试 (10%) 子集。这些模型是独立训练的,它们的预测被用来开发堆叠模型。在基本模型中,Extreme Gradient Boosting 模型实现了最高的准确性,在测试期间的 R2 为 0.947。然而,堆叠模型的表现优于它,在测试阶段获得了 0.953 的 R2。Shapley 添加剂解释分析确定固化持续时间是抗压强度预测中最具影响力的因素。开发了一个用户友好的图形界面,以促进混凝土结构中抗压强度的有效预测。
更新日期:2025-01-06
中文翻译:
基于堆叠的机器学习预测混凝土材料的单轴抗压强度
抗压强度是混凝土结构设计和耐久性的关键因素。准确预测抗压强度有助于优化材料使用并降低施工成本。本研究提出了一种新的堆叠模型来预测抗压强度,将三个基本模型与线性回归相结合。基本模型包括人工神经网络、随机森林和极端梯度提升,而堆叠模型使用线性回归作为元模型。使用 1,030 个混凝土混合样品的数据集进行培训和评估,涵盖 8 个关键输入参数,包括水泥、高炉矿渣、粗骨料、细骨料、粉煤灰、水、高效减水剂和养护天数。该数据集分为训练 (80%) 、验证 (10%) 和测试 (10%) 子集。这些模型是独立训练的,它们的预测被用来开发堆叠模型。在基本模型中,Extreme Gradient Boosting 模型实现了最高的准确性,在测试期间的 R2 为 0.947。然而,堆叠模型的表现优于它,在测试阶段获得了 0.953 的 R2。Shapley 添加剂解释分析确定固化持续时间是抗压强度预测中最具影响力的因素。开发了一个用户友好的图形界面,以促进混凝土结构中抗压强度的有效预测。