The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (Fy) and undrained shear strength of the cohesive soil (Cu) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R2) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

中文翻译：

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与多层感知器混合的遗传算法具有经济的边坡稳定性设计

目前的工作旨在评估和优化人工神经网络 (ANN) 与遗传算法 (GA) 优化算法相结合的设计，用于计算纯粘性边坡的边坡稳定安全系数 (SF)。为了为开发的预测模型制作训练和测试数据集，进行了 630 次有限元极限平衡 (FELE) 分析。与许多基于人工智能的解决方案类似，该数据库涉及189个测试数据集（例如整个数据库的30%）和441个训练数据集；例如，范围为总数据库的 70%。此外，多层感知器（MLP）算法的变量（例如，任何隐藏层中的节点数）和GA算法（如人口规模）通过一系列试错过程进行了优化。输入参数，分析中使用的参数包括坡角 (β)、后退距离比 (b/B)、施加在斜坡上的应力 (Fy) 和粘性土的不排水剪切强度 (Cu)，其中输出为 SF。根据许多统计指标评估从 MLP 和 GA-MLP 模型获得的两个数据集的网络输出。总共 72 次 MLP 试错（例如，参数研究）为 MLP 结构确定了 4 × 8 × 1 的最佳架构。两种提议的技术都会产生适当的性能；然而，根据统计指标，与 MLP 相比，GA-MLP 模型可以在一定程度上实现最小均方误差 (MSE)。在优化的 GA-MLP 网络中，确定系数 (R2) 和均方根误差 (RMSE) 值分别为 (0.975 和 0.097) 和 (0.969 和 0.107)，