Elhishi Sara, Elashry Asmaa Mohammed, El-Metwally Sara
Department of Information Systems, Faculty of Computers and Information, Mansoura University, P.O. Box: 35516, Mansoura, 35516, Egypt.
Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
Sci Rep. 2023 Nov 14;13(1):19892. doi: 10.1038/s41598-023-47169-7.
Concrete is a cost-effective construction material widely used in various building infrastructure projects. High-performance concrete, characterized by strength and durability, is crucial for structures that must withstand heavy loads and extreme weather conditions. Accurate prediction of concrete strength under different mixtures and loading conditions is essential for optimizing performance, reducing costs, and enhancing safety. Recent advancements in machine learning offer solutions to challenges in structural engineering, including concrete strength prediction. This paper evaluated the performance of eight popular machine learning models, encompassing regression methods such as Linear, Ridge, and LASSO, as well as tree-based models like Decision Trees, Random Forests, XGBoost, SVM, and ANN. The assessment was conducted using a standard dataset comprising 1030 concrete samples. Our experimental results demonstrated that ensemble learning techniques, notably XGBoost, outperformed other algorithms with an R-Square (R) of 0.91 and a Root Mean Squared Error (RMSE) of 4.37. Additionally, we employed the SHAP (SHapley Additive exPlanations) technique to analyze the XGBoost model, providing civil engineers with insights to make informed decisions regarding concrete mix design and construction practices.
混凝土是一种性价比高的建筑材料，广泛应用于各类建筑基础设施项目中。高性能混凝土以强度和耐久性为特征，对于必须承受重载和极端天气条件的结构至关重要。准确预测不同混合料和加载条件下的混凝土强度对于优化性能、降低成本和提高安全性至关重要。机器学习的最新进展为结构工程中的挑战提供了解决方案，包括混凝土强度预测。本文评估了八种流行的机器学习模型的性能，包括线性回归、岭回归和套索回归等回归方法，以及决策树、随机森林、XGBoost、支持向量机和人工神经网络等基于树的模型。评估使用了一个包含1030个混凝土样本的标准数据集。我们的实验结果表明，集成学习技术，特别是XGBoost，表现优于其他算法，其决定系数（R）为0.91，均方根误差（RMSE）为4.37。此外，我们采用SHAP（SHapley加性解释）技术分析XGBoost模型，为土木工程师在混凝土配合比设计和施工实践方面做出明智决策提供见解。
