High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.

中文翻译：

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混凝土细长梁剪切强度预测的先进计算机辅助模型的开发

高强度混凝土（HSC）高度适用于重型结构的建造。然而，HSC 的剪切强度 (Ss) 测定是结构设计师和决策者的关键问题。目前的研究提出了基于自适应神经模糊推理系统 (ANFIS) 与几种元启发式优化算法相结合的新模型，包括蚁群优化器 (ACO)、差分进化 (DE)、遗传算法 (GA) 和粒子群优化 (PSO)，预测 HSC 细长光束的 Ss。所提出的模型是使用几个输入组合构建的，其中包含几个相关的维度参数，例如梁的有效深度 (d)、剪切跨度 (a)、骨料的最大尺寸 (ag)、混凝土的抗压强度 (fc) 和张力百分比强化（ρ）。为了评估数据集的非同质性对预测结果准确性的影响，检查了两种可能的建模场景，（i）未处理（初始）数据集（NP）和（ii）预处理数据集（PP）通过几个性能指标。建模结果表明，ANFIS-PSO 混合模型比其他模型和预处理输入参数获得了最好的预测精度。检查了几个不确定性分析（即模型、变量和数据），结果表明预测 HSC 剪切强度对模型结构不确定性比输入参数更敏感。通过多项性能指标进行检查。建模结果表明，ANFIS-PSO 混合模型比其他模型和预处理输入参数获得了最好的预测精度。检查了几个不确定性分析（即模型、变量和数据），结果表明预测 HSC 剪切强度对模型结构不确定性比输入参数更敏感。通过多项性能指标进行检查。建模结果表明，ANFIS-PSO 混合模型比其他模型和预处理输入参数获得了最好的预测精度。检查了几个不确定性分析（即模型、变量和数据），结果表明预测 HSC 剪切强度对模型结构不确定性比输入参数更敏感。