有色金属科学与工程2025，Vol.16Issue(1)：125-134,10.DOI:10.13264/j.cnki.ysjskx.2025.01.014
基于Bi-LSTM与SA融合模型的多台阶高陡边坡变形预测
Deformation prediction of multi-step high and steep slope based on Bi-LSTM and SA fusion model
摘要
Abstract
Factors such as rock type,structural characteristics of the rock body,hydrogeology,natural environment,and mining activities easily affect the deformation of open pit slopes,resulting in a high degree of temporal correlation,time-varying,high-dimensional,and non-linear characteristics of the slope deformation monitoring data.Aiming at the problem of traditional slope deformation prediction models being unable to exploit the back-and-forth dependence of monitoring data series,a multi-step high steep slope deformation prediction model with a fusion network of bi-directional long and short-term memory network(Bi-LSTM)and self-attention mechanism(SA)was proposed,which takes the advantages of Bi-LSTM network mining the pre and post dependence of monitoring data and SA network analyzing the correlation between monitoring data.The effective prediction of multi-step high and steep slope deformation was realized.The results show that,under the same input conditions,compared with the prediction results of the BP neural network,LSTM model and Bi-LSTM model,the overall prediction error of the Bi-LSTM-SA fusion model for the deformation prediction results of multi-step high slope in three monitoring directions is smaller.The prediction results of Bi-LSTM-SA fusion model are closer to the measured results.The Bi-LSTM-SA fusion model has better prediction performance,stability and robustness.关键词
露天矿/多台阶/高陡边坡/Bi-LSTM-SA/变形预测Key words
open-pit mine/multi-step/high and steep slope/Bi-LSTM-SA/deformation prediction分类
矿业与冶金引用本文复制引用
曾森华,赵宇,叶腾飞,贺平,郝文拯..基于Bi-LSTM与SA融合模型的多台阶高陡边坡变形预测[J].有色金属科学与工程,2025,16(1):125-134,10.基金项目
江西省教育厅科研资助项目(GJJ210859) （GJJ210859）
