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Prediction of interface yield stress and plastic viscosity of fresh concrete using a hybrid machine learning approach

机译:用混合机学习方法预测界面屈服应力和新混凝土塑性粘度

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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.
机译:界面屈服应力和混凝土混合的塑料粘度严重影响其泵送性。该研究构建并验证数据驱动方法以预测这两个重要参数。所提出的方法是最小二乘支持向量机(LSSVM)和粒子群优化(PSO)的杂交。 LSSVM用于推断两个混凝土混合参数与影响因素之间的映射函数。此外,为了克服微调LSSVM模型超参数的具有挑战性的任务,利用PSO算法,基于智能的基于智能的成群化,来优化LSSVM预测模型。在该研究中收集了包括142实验测试的数据集,以构建和验证提出的混合方法。 Wilcoxon签名秩检验支持的实验结果指出,LSSVM和PSO的杂交(具有测定系数= 0.71和0.77分别用于界面屈服应力和塑料粘度预测)可以将可预测结果提供优于基准模型的预测结果。因此,PSO和LSSVM的混合模型可以是有前途的替代方案,可以帮助工程师在混凝土结构结构的任务中。

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