红砂岩单轴压缩宏细观参数映射关系研究

颗粒流离散元软件PFC2D在岩土工程中的应用十分广泛,存在的主要问题是如何标定其细观参数,目前大多使用"试凑法",此方法的缺点在于工作量大、效率低等,本文提出以反向传播算法(back propagation,BP)神经网络的方式代替此方法,利用PFC2D内置的FISH以及Python语言对其进行二次开发,使之自动运行并获取40组宏细观参数样本。结果表明:BP神经网络可以快速准确地建立宏细观参数映射关系,误差均保持在0.01以内,模拟得到的应力-应变曲线与室内试验曲线高度吻合,且无需大量的数据样本便可创建网络,效率较高;另外,经验证本文选用的平直节理模型,可以有效地解决平行粘结模型UCS/TS值偏小问题,确定平直节理模型可以更好地模拟岩石。

MACRO AND MICROSCOPIC PARAMETER MAPPING RELATIONSHIP FOR RED SANDSTONE

The particle flow discrete element software PFC2D is widely used in geotechnical engineering.The main problem is how to calibrate its mesoscopic parameters.At present,the"trial and error method"is mostly used.The disadvantage of this method is large amount of work and low efficiency.This paper proposes to replace this method with the back propagation(BP)neural network method,and use the PFC2D built-in FISH and Python language to re-develop a software,which then automatically runs to obtain 40 sets of macro mechanical parameter samples.The results show that the BP neural network method can quickly and accurately establish the macro and meso parameter mapping relationship,and the error is kept within 0.01.The simulated stress-strain curve is highly consistent with the indoor test curve,and the network can be created without large data samples.The efficiency is higher;and it is verified that the straight joint model selected in this paper can effectively determine the small UCS/TS value of the parallel bond model,indicating that the straight joint model can be used to better simulate the rock.