An Effective Artificial Intelligence Approach for Slope Stability Evaluation
An Effective Artificial Intelligence Approach for Slope Stability Evaluation
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DOI：
10.1109/access.2022.3141432
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发表时间：
2022-01-01
期刊：
影响因子：
3.9
通讯作者：
Jebeli, Mohammadreza
中科院分区：
文献类型：
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作者：
Khajehzadeh, Mohammad;Taha, Mohd Raihan;Jebeli, Mohammadreza
在这项研究中，一个有效的智能系统的基础上，人工神经网络（ANN）和一个新版本的正弦余弦算法（SCA）的开发，以评估和预测FOS的均质边坡在静态和动态载荷。第一步，提出了一种基于自适应正弦余弦算法（ASCA）和模式搜索（PS）的混合优化算法，即ASCPS，并使用一组基准测试函数进行了验证。然后，将新算法与Morgenstern和Price法一起沿着用于地震边坡稳定性评价。为了提供一个神经网络训练数据集，一组189个斜坡与不同值的坡高，坡角，土壤摩擦角，土壤粘聚力，和水平加速度系数进行了分析，并记录了相应的FOS。在接下来的步骤中，所提出的ASCPS算法被实现用于使用收集的数据库来训练ANN模型。利用均方根误差（RMSE）和相关系数（R）对模型的性能和预测能力进行了评价。结果表明，该模型的RMSE值为0.023，R值为0.984，是一种可靠、简便、有效的计算模型，可用于边坡稳定性的评估。此外，开发的人工神经网络模型应用于一个案例研究的边坡稳定性从以前的研究，结果表明，该模型可以提供更好的最优解，并优于现有的方法。
In this study, an effective intelligent system based on artificial neural networks (ANN) and a new version of the sine cosine algorithm (SCA) is developed to evaluate and predict the FOS of homogenous slopes under static and dynamic loading. In the first step, an effective hybrid optimization algorithm based on the adaptive sine cosine algorithm (ASCA) and pattern search (PS), namely ASCPS, is proposed and verified using a set of benchmark test functions. Then, the new algorithm, along with the Morgenstern and Price method is applied for seismic slope stability evaluation. To provide a neural network training dataset, a set of 189 slopes with different values of slope height, slope angle, friction angle of soil, soil cohesion, and horizontal acceleration coefficient have been analyzed and their corresponding FOS have been recorded. In the next step, the proposed ASCPS algorithm is implemented for training the ANN model using the collected database. The performance and prediction capacity of the developed model are evaluated using root mean square error (RMSE) and correlation coefficient (R). According to the obtained results, the ANN model with the RMSE value of 0.023 and the R value of 0.984 is a reliable, simple, and valid computational model for estimating the FOS and evaluating the slope stability under static and earthquake loads. In addition, the developed ANN model is applied to a case study of slope stability from previous studies, and the results reveal that the proposed model may provide better optimal solutions and outperform existing methods.