基于人工神经网络的边坡岩体力学参数反演

Mechanical Parameter Inversion of Slope Rock Mass Based on Artificial Neural Network

摘要: 本文基于FLAC数值模拟实验和现场变形监测实验，依据均匀试验进行模拟实验方案的设计，采用人工神经网络算法进行了岩质边坡位移反分析，得出了岩质边坡四个重要的力学参数，通过对反演的力学参数做分析可较好预测边坡岩体稳定性。通过位移反分析得到的力学参数，因其考虑了时间与各种开挖等效应，结果更具有准确性，同时与FLAC计算结果具有较好的一致性。人工神经网络的边坡岩体力学参数反演对边坡支护和参数的选取有重要的参考价值，同时对于工程经济也具有重要的意义。

Abstract: Based on FLAC numerical simulation experiment and in-situ deformation monitoring, simulation experiment scheme was designed according to uniform test. The artificial neural network algorithm was used for rock slope displacement back analysis, and four important mechanical parameters of the rock slope were obtained. Further, by analyzing the mechanic parameters inversion, the slope rock mass stability could be better predicted. The obtained mechanical parameters by back analysis were more accurate and consistent with the calculated results of FLAC because the effects of time and various excavations were taken into account. The mechanical parameters inversion of slope rock mass by artificial neural network has important reference value for slope support and parameter selection, and also has an important effect on engineering economy.