The aim of this study is to develop a new framework for the prediction of stress intensity factor (SIF) using newly developed hybrid artificial intelligence (AI) models. To do so, an adaptive neuro‐fuzzy inference system optimized by two meta‐heuristic algorithms as genetic algorithm (ANFIS‐GA) and particle swarm optimization (ANFIS‐PSO) is proposed. Moreover, a database composed of 150 SIF values obtained using the finite element method (FEM) calculations is used for training and validating the two proposed AI models. The efficiency and accuracy of the proposed AI models were investigated through several assessment criteria. Results showed the outperformance of the ANFIS‐PSO model for accurate prediction of SIF values with R² = 0.9913, root mean square error (RMSE) = 23.6 and mean absolute error (MAE) = 18.07, whereas both AI models indicate a robust performance in the presence of input variability. Overall, the performed study provides a hybrid AI framework that can serve as an efficient numerical tool for SIF prediction and analysis.

中文翻译：

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基于粒子群优化和遗传算法的新型混合自适应神经模糊推理系统模型

这项研究的目的是使用新开发的混合人工智能（AI）模型开发一种用于预测应力强度因子（SIF）的新框架。为此，提出了一种通过遗传算法（ANFIS-GA）和粒子群优化（ANFIS-PSO）两种元启发式算法优化的自适应神经模糊推理系统。此外，由150个使用有限元方法（FEM）计算获得的SIF值组成的数据库用于训练和验证两个拟议的AI模型。通过几种评估标准对所提出的AI模型的效率和准确性进行了研究。结果表明，ANFIS-PSO模型在准确预测SIF值（R ² = 0.9913，均方根误差（RMSE）= 23.6，平均绝对误差（MAE）= 18.07，而两种AI模型都表明在存在输入可变性的情况下性能稳定。总的来说，进行的研究提供了一个混合AI框架，可以作为SIF预测和分析的有效数值工具。