Concrete compressive strength prediction is an essential process for material design and sustainability. This study investigates several novel hybrid adaptive neuro-fuzzy inference system (ANFIS) evolutionary models, i.e., ANFIS-particle swarm optimization (PSO), ANFIS-ant colony, ANFIS-differential evolution (DE), and ANFIS-genetic algorithm to predict the foamed concrete compressive strength. Several concrete properties, including cement content (C), oven dry density (O), water-to-binder ratio (W), and foamed volume (F) are used as input variables. A relevant data set is obtained from open-access published experimental investigations and used to build predictive models. The performance of the proposed predictive models is evaluated based on the mean performance (MP), which is the mean value of several statistical error indices. To optimize each predictive model and its input variables, univariate (C, O, W, and F), bivariate (C-O, C-W, C-F, O-W, O-F, and W-F), trivariate (C-O-W, C-W-F, O-W-F), and four-variate (C-O-W-F) combinations of input variables are constructed for each model. The results indicate that the best predictions obtained using the univariate, bivariate, trivariate, and four-variate models are ANFIS-DE- (O) (MP = 0.96), ANFIS-PSO- (C-O) (MP = 0.88), ANFIS-DE- (O-W-F) (MP = 0.94), and ANFIS-PSO- (C-O-W-F) (MP = 0.89), respectively. ANFIS-PSO- (C-O) yielded the best accurate prediction of compressive strength with an MP value of 0.96.

混凝土抗压强度预测是材料设计和可持续发展的重要过程。这项研究调查了几种新型的混合自适应神经模糊推理系统（ANFIS）进化模型，即ANFIS粒子群优化（PSO），ANFIS蚁群，ANFIS差分进化（DE）和ANFIS遗传算法来预测泡沫混凝土的抗压强度。输入变量包括水泥含量（C），烘箱干密度（O），水胶比（W）和泡沫体积（F）等几种混凝土性能。相关数据集可从公开获取的公开实验研究中获得，并用于建立预测模型。建议的预测模型的性能是基于平均性能（MP）进行评估的，MP是几个统计误差指标的平均值。为了优化每个预测模型及其输入变量，单变量（C，O，W和F），双变量（CO，CW，CF，OW，OF和WF），三变量（COW，CWF，OWF）和四变量为每个模型构造输入变量的变量（COWF）组合。结果表明，使用单变量，双变量，三变量和四变量模型获得的最佳预测是ANFIS-DE-（O）（MP = 0.96），ANFIS-PSO-（CO）（MP = 0.88），ANFIS- DE-（OWF）（MP = 0.94）和ANFIS-PSO-（COWF）（MP = 0.89）。ANFIS-PSO-（CO）产生的抗压强度最准确的预测，MP值为0.96。并为每个模型构造了输入变量的四变量（COWF）组合。结果表明，使用单变量，双变量，三变量和四变量模型获得的最佳预测是ANFIS-DE-（O）（MP = 0.96），ANFIS-PSO-（CO）（MP = 0.88），ANFIS- DE-（OWF）（MP = 0.94）和ANFIS-PSO-（COWF）（MP = 0.89）。ANFIS-PSO-（CO）产生了最精确的抗压强度预测值，MP值为0.96。并为每个模型构建了输入变量的四变量（COWF）组合。结果表明，使用单变量，双变量，三变量和四变量模型获得的最佳预测是ANFIS-DE-（O）（MP = 0.96），ANFIS-PSO-（CO）（MP = 0.88），ANFIS- DE-（OWF）（MP = 0.94）和ANFIS-PSO-（COWF）（MP = 0.89）。ANFIS-PSO-（CO）产生了最精确的抗压强度预测值，MP值为0.96。