Shaaban Mohammed, Amin Mohamed, Selim S, Riad Islam M
Civil Engineering Department, Faculty of Engineering, Delta University for Science and Technology, International Coastal Road, Gamasa, Egypt.
Civil and Architectural Constructions Department, Faculty of Technology and Education, Suez University, Suez, Egypt.
Sci Rep. 2025 Jul 15;15(1):25567. doi: 10.1038/s41598-025-10342-1.
Identifying the mechanical properties of High Strength Concrete (HSC), particularly compressive strength, is critical for safety purposes. Concrete compressive strength is determined by using laboratory experiments, which are costly and time-consuming. Artificial intelligence (AI) methods reduce time and money. This research proposes a machine learning (ML) model using the Python programming language to predict the compressive strength of HSC. The dataset used for the models was obtained from original experimental tests. Important parameters, namely cement content, silica fume, water, superplasticizer, sand, gravel, and curing age, were taken as input to predict the output, which was the compressive strength. Various regression models were investigated for the prediction of outcome compressive strength. To optimize the models, hyperparameters were tuned, and measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared were used for evaluation. XGBoost (R ≈ 0.94) outperformed other models, demonstrating ML's potential for HSC strength prediction and demonstrated that Python can be successfully applied to establish accurate and reliable prediction models.
确定高强度混凝土（HSC）的力学性能，尤其是抗压强度，对于安全目的至关重要。混凝土抗压强度通过实验室实验来确定，这些实验成本高且耗时。人工智能（AI）方法可减少时间和金钱成本。本研究提出了一种使用Python编程语言的机器学习（ML）模型来预测HSC的抗压强度。用于模型的数据集来自原始实验测试。将重要参数，即水泥含量、硅灰、水、高效减水剂、砂、砾石和养护龄期作为输入来预测输出，即抗压强度。研究了各种回归模型用于预测抗压强度结果。为了优化模型，对超参数进行了调整，并使用平均绝对误差（MAE）、均方误差（MSE）和决定系数等指标进行评估。XGBoost（R≈0.94）优于其他模型，证明了ML在HSC强度预测方面的潜力，并表明Python可成功应用于建立准确可靠的预测模型。
