锚固变化引起岩体表面位移的BP神经网络预测模型

Chunhui Fang, Xiaoyue Zhang
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引用次数: 1

摘要

岩质边坡群锚的预应力损失随时间增加,导致群锚区结构面压缩带减弱,岩面向自由面方向的变形逐渐增大,导致边坡稳定性急剧降低。以某拱坝坝肩岩边坡群锚布设、锚杆监测序列、岩体表面位移监测序列为基础,根据变步长自适应BP神经网络,建立了群锚区预应力变化引起岩体表面位移的预测模型。利用该模型对典型日岩体表面位移进行了模拟,结果与监测数据吻合较好,验证了在群锚区锚杆预应力损失已知的情况下,本文建立的BP神经网络模型能够正确预测岩体表面位移。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BP Neural Network Prediction Model of Rock Surface Displacement Caused by Anchor-Hold Change
The prestressed loss of group anchor in rock slope increase with time, which leads to the compression belt of structure plane in group anchor area was weakened, deformation of rock surface toward the free surface direction increase gradually, as a result, the slope stability was drastically reduced. Based on the group anchor layout of the abutment rock slope of an arch dam, the anchor-hold monitoring series, the rock surface displacement monitoring series, and according to the adaptive BP neural network with variable steps, a prediction model for rock surface displacement caused by the prestressed change in group anchor area was established in this paper. By using the model, the displacement of rock surface for typical days is simulated, with the result perfectly consistent with the monitoring data, which verify that when the anchor prestressed loss in group anchor area is known, the rock surface displacement can be correctly predicted by the BP neural network model established in this paper.
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