The stability of rock slopes is a difficult problem in the field of geotechnical and geological engineering. Less than 20% of all landslides are predictable each year, so a simple, fast, reliable and low-cost method to predict the stability of slopes is urgently needed. This study investigates a new regularized online sequential extreme learning machine, incorporated with the variable forgetting factor (FOS-ELM), based on intelligence computation to predict the factor of safety of a rock slope (F). The Bayesian information criterion (BIC) is applied to establish seven input combinations based on the parameters of the Hoek-Brown criterion and geometrical and mechanical parameters of the slope, such as the geological strength index (GSI), disturbance factor (D), rock material constant (m_i), uniaxial compressive strength (σ_ci), unit weight of the rock mass (γ), slope height (H) and slope angle (β). Seven models are established and evaluated to determine the optimal input combination. Various statistical indicators are calculated for the prediction accuracy examination. Compared to the classical extreme learning machine (ELM) model predictions of F, the results of the applied FOS-ELM model demonstrate a better prediction accuracy and are more effective when accounting for an increase in data. The FOS-ELM model with all seven input parameters is used to establish stability charts with the influence coefficient of slope angle change (η_β), disturbance change (η_D) and slope height change (η_H). Using stability charts with a combination of η_β, η_D and η_H can be used to quickly and preliminarily analyze rock stability as a guide for engineering practitioners in rock slope design.

中文翻译：

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基于混合在线序贯极限学习机模型的岩质边坡稳定性分析与图表

岩质边坡的稳定性是岩土工程和地质工程领域的难题。每年只有不到20％的滑坡是可预测的，因此迫切需要一种简单，快速，可靠且低成本的方法来预测边坡的稳定性。这项研究研究了一种新的正规化在线序贯极限学习机，该机结合了变量遗忘因子（FOS-ELM），基于智能计算来预测岩石边坡（F）的安全因子。基于Hoek-Brown准则的参数以及边坡的几何和力学参数（如地质强度指数（GSI），扰动因子（D），岩石），应用贝叶斯信息准则（BIC）建立七个输入组合材料常数（m _i），单轴抗压强度（_σci），岩体的单位重量（γ），坡度高度（H）和坡度角（β）。建立并评估了七个模型，以确定最佳输入组合。计算各种统计指标以进行预测准确性检查。与F的经典极限学习机（ELM）模型预测相比，所应用的FOS-ELM模型的结果显示出更好的预测精度，并且在考虑数据增加时更有效。与所有七个输入参数的FOS-ELM模型被用于建立稳定的图表与倾斜角度变化的影响系数（η _β），干扰变化（η _d）和斜率高度变化（η _ħ）。使用稳定性的图表与η组合_β，η _d和η _ħ可以迅速用于和预先确定岩石稳定性作为在边坡设计工程实践者的指导。