Elshaarawy Mohamed Kamel, Alsaadawi Mostafa M, Hamed Abdelrahman Kamal
Civil Engineering Department, Faculty of Engineering, Horus University-Egypt, New Damietta, 34517, Egypt.
Structural Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt.
Sci Rep. 2024 Jul 19;14(1):16694. doi: 10.1038/s41598-024-66957-3.
Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable strength prediction reduces costs and time in design and prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Machine Learning (ML) models to enhance the prediction of CS, analyzing 1030 experimental CS data ranging from 2.33 to 82.60 MPa from previous research databases. The ML models included both non-ensemble and ensemble types. The non-ensemble models were regression-based, evolutionary, neural network, and fuzzy-inference-system. Meanwhile, the ensemble models consisted of adaptive boosting, random forest, and gradient boosting. There were eight input parameters: cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with the CS as the output. Comprehensive performance evaluations include visual and quantitative methods and k-fold cross-validation to assess the study's reliability and accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted to understand better how each input variable affects CS. The findings showed that the Categorical-Gradient-Boosting (CatBoost) model was the most accurate prediction during the testing stage. It had the highest determination-coefficient (R) of 0.966 and the lowest Root-Mean-Square-Error (RMSE) of 3.06 MPa. The SHAP analysis showed that the age of the concrete was the most critical factor in the predictive accuracy. Finally, a Graphical User Interface (GUI) was offered for designers to predict concrete CS quickly and economically instead of costly computational or experimental tests.
混凝土抗压强度（CS）是混凝土结构设计中的一个关键性能参数。可靠的强度预测可降低设计成本和时间，并避免因大量配合比试验导致的材料浪费。机器学习技术可解决诸如CS预测等结构工程挑战。本研究使用机器学习（ML）模型来改进CS预测，分析了来自先前研究数据库的1030个抗压强度实验数据，范围为2.33至82.60MPa。ML模型包括非集成和集成类型。非集成模型基于回归、进化、神经网络和模糊推理系统。同时，集成模型由自适应增强、随机森林和梯度增强组成。有八个输入参数：水泥、高炉矿渣、骨料（粗骨料和细骨料）、粉煤灰、水、高效减水剂和养护天数，以CS作为输出。综合性能评估包括可视化和定量方法以及k折交叉验证，以评估研究的可靠性和准确性。使用Shapley-Additive-exPlanations（SHAP）进行敏感性分析，以更好地了解每个输入变量如何影响CS。结果表明，分类梯度增强（CatBoost）模型在测试阶段的预测最为准确。它具有最高的决定系数（R）为0.966和最低的均方根误差（RMSE）为3.06MPa。SHAP分析表明，混凝土龄期是预测准确性的最关键因素。最后，为设计人员提供了一个图形用户界面（GUI），以便他们能够快速且经济地预测混凝土CS，而无需进行成本高昂的计算或实验测试。
