基于人工神经网络的井泉滑坡稳定性

针对组成边坡的地质条件、岩土体物理力学性质等因素的不确定性、模糊性特点,为了解决准确预测边坡稳定程度的问题,利用人工神经网络技术,在边坡稳定性评价方面进行了探索,建立了一种把人为因素降到最低程度、融定量与定性指标于一体的边坡稳定性综合评价模型,并应用该模型对井泉滑坡工程实例进行了分析。分析结果表明:该模型对井泉滑坡的安全系数与边坡状态判断准确,与实际情况吻合。该模型在边坡稳定性评价方面具有重要的工程应用。

Jingquan slope stability based on artificial neural network

In view of the uncertainty and fuzziness of the slope's geological conditions and mechanical properties of rock mass etc., in order to solve the problem of predicting the extent of slope stability correctly, the slope stability is discussed and a slope stability assessment model is established which can reduce the human factor to the lowest extent and can syncretize qualitative and quantitative stability indiceson on the basis of the artificial neural network technology, this model is used to analyze the engineering case of Jingquan landslide. The result shows that the artificial neural network model for judging the safety factors and slope state of Jingquan landslide has a high accuracy, which coincides with the practical situation. This model has an important engineering application.