Context: Artificial neural networks (ANNs) and genetic algorithms (GAs) have become widely used in various engineering fields due to their ability to solve complicated issues directly.

Aims: In this study, internal friction angle (ϕ) values for granular soils were calculated using ANNs, GAs, and empirical methods based on standard penetration test (SPT) data to designate the system that produced the best statistical outcomes.

Methods: Utilising the literature, experimentally determined internal friction angle (E_ϕ) values were obtained for a significant quantity of standard penetration test data. Analysis of variance was performed to ascertain whether there was a significant correlation between SPT-N₆₀ values and E_ϕ. A simulated network was created with ANNs, and a function was obtained with GAs for SPT-N₆₀–ϕ correlation. The outcomes obtained with ANNs and GAs were compared with empirical equations and experimental results. Optimisation analysis was conducted with the novel Improved Goal Attainment method to minimise the margin of error.

Key results: Compared to the GAs and empirical equations, the ANN has been determined to have a reasonable correlation with experimental results.

Conclusions: It was determined that by utilising ANNs, the current empirical equations indicating the relationship between different soil parameters and the data of tests such as SPT and cone penetration test (CPT) could be produced in improved correlations by employing a large number of data sets obtained from different regions.

Implications: Effective predictions can be achieved instead of present methods.

背景：人工神经网络 (ANN) 和遗传算法 (GA) 由于能够直接解决复杂问题，因此已广泛应用于各个工程领域。

目的：在本研究中，使用人工神经网络、遗传算法和基于标准渗透试验 (SPT) 数据的经验方法计算颗粒土壤的内摩擦角 ( ϕ ) 值，以指定产生最佳统计结果的系统。

方法：利用文献，通过实验确定的内摩擦角 ( E _ϕ ) 值获得了大量标准贯入试验数据。进行方差分析以确定 SPT- N ₆₀值与E _φ之间是否存在显着相关性。使用 ANN 创建模拟网络，并使用 GA 获得 SPT- N ₆₀ – φ相关函数。将使用 ANN 和 GA 获得的结果与经验方程和实验结果进行了比较。使用新颖的改进目标实现方法进行优化分析，以最大限度地减少误差范围。

主要结果：与 GA 和经验方程相比，ANN 已被确定与实验结果具有合理的相关性。

结论：可以确定的是，通过使用 ANN，可以通过使用大量数据集以改进的相关性来生成表示不同土壤参数与测试数据（例如 SPT 和锥入度测试 (CPT)）之间关系的当前经验方程式从不同地区获得。

启示：可以实现有效的预测，而不是目前的方法。