The interface yield stress and the plastic viscosity of concrete mixes critically influence their pumpability. This study constructs and verifies a data-driven method for predicting these two important parameters. The proposed method is a hybridization of Least Squares Support Vector Machine (LSSVM) and Particle Swarm Optimization (PSO). The LSSVM is employed to infer the mapping function between the two concrete mix’s parameters and their influencing factors. Moreover, in order to overcome the challenging task of fine-tuning the LSSVM model hyper-parameters, the PSO algorithm, a swarm intelligence based metaheuristic, is utilized to optimize the LSSVM prediction model. A data set including 142 experimental tests has been collected in this study to construct and verify the proposed hybrid method. Experimental results supported by the Wilcoxon signed-rank test point out that the hybridization of LSSVM and PSO (with coefficients of determination = 0.71 and 0.77 for interface yield stress and plastic viscosity predictions, respectively) can deliver predictive results superior to those of benchmark models. Hence, the hybrid model of PSO and LSSVM can be a promising alternative to assist engineers in the task of concrete structure construction.

界面屈服应力和混凝土混合物的塑性粘度会严重影响其泵送性。这项研究构建并验证了预测这两个重要参数的数据驱动方法。所提出的方法是最小二乘支持向量机（LSSVM）和粒子群优化（PSO）的混合。用LSSVM推断两种混凝土配合料的参数及其影响因素之间的映射函数。此外，为了克服对LSSVM模型超参数进行微调的艰巨任务，PSO算法（一种基于群体智能的元启发式算法）被用于优化LSSVM预测模型。本研究收集了包括142个实验测试的数据集，以构建和验证所提出的混合方法。Wilcoxon符号秩检验支持的实验结果指出，LSSVM和PSO的杂交（界面屈服应力和塑性粘度预测的确定系数分别为0.71和0.77）可以提供优于基准模型的预测结果。因此，PSO和LSSVM的混合模型可以成为有希望的替代方案，以帮助工程师完成混凝土结构施工任务。