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Abstract：
There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research.
摘要：
高性能混凝土(HPC)抗压强度与其组分之间存在复杂的高维非线性映射关系,对抗压强度的准确预测有很大影响。在本文中,结合BP神经网络(BPNN)的高效稳健软件计算策略，提出了支持向量机（SVM）和遗传算法（GA）用于HPC的抗压强度预测。从以前的文献中提取了8个特征，构建了包含454组数据的抗压强度数据库。对模型进行了训练和测试,以及4个机器学习(ML)模型的性能，即BPNN,SVM,GA-BPNN和GA-SVM,比较。结果表明，耦合模型优于单一模型。此外，由于GA-SVM具有较好的泛化能力和理论基础,其收敛速度和预测精度均优于GA-BPNN。然后利用灰色关联分析(GRA)和Shapley分析验证了GA-SVM模型的可解释性,结果表明，水胶比对抗压强度的影响最大。最后,多输入变量的组合来评估抗压强度，补充了本研究，并再次验证了水胶比的显著影响，为后续研究提供参考价值。
