Based on least-squares support vector machine optimized using a quantum particle swarm optimization algorithm (QPSO-LSSVM), a prediction model was established to predict the fracture properties of polyvinyl alcohol fiber-reinforced cementitious composites (CCs) containing nano-SiO₂ (PVA-CCNS) and improve its accuracy and effectiveness. Nineteen groups of measured data obtained from fracture performance tests on a three-point bending notched PVA-CCNS beam were selected for analysis and prediction. The prediction results of the QPSO-LSSVM model were compared with those of the least-squares support vector machine optimized with the particle swarm optimization algorithm, least-squares support vector machine and back-propagation neural network models. The simulation analysis results indicated that the goodness of fit (${\text{R}}^2$) values of the fracture energy, initial fracture toughness and unstable fracture toughness were 0.790, 0.940 and 0.950, respectively, for the QPSO-LSSVM prediction model. In addition, the fitting degree between the measured and predicted values of the QPSO-LSSVM prediction model was better than those of the other three models. The higher accuracy, better convergence, and robustness of the QPSO-LSSVM model than the other three models proves that the QPSO-LSSVM model is an optimal method for predicting the fracture performance of CCs. The proposed model can guide the mix proportion design of CC mixtures.

基于量子粒子群优化算法(QPSO-LSSVM)优化最小二乘支持向量机，建立预测模型，预测含纳米SiO ₂的聚乙烯醇纤维增强水泥基复合材料(CCs)的断裂性能(PVA-CCNS) 并提高其准确性和有效性。选择从三点弯曲缺口 PVA-CCNS 梁的断裂性能测试中获得的 19 组测量数据进行分析和预测。将QPSO-LSSVM模型的预测结果与粒子群优化算法优化的最小二乘支持向量机、最小二乘支持向量机和反向传播神经网络模型的预测结果进行了比较。仿真分析结果表明拟合优度（${\text{电阻}}^2$) 对于 QPSO-LSSVM 预测模型，断裂能、初始断裂韧性和不稳定断裂韧性的值分别为 0.790、0.940 和 0.950。此外，QPSO-LSSVM 预测模型实测值与预测值的拟合程度优于其他三个模型。QPSO-LSSVM 模型比其他三种模型具有更高的精度、更好的收敛性和鲁棒性，证明 QPSO-LSSVM 模型是预测 CC 断裂性能的最佳方法。该模型可以指导CC混合料的配合比设计。