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有色金属(矿山部分):2022,74(3):65-70
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基于AdaBoost卷积神经网络的矿山边坡稳定性预测
冯小鹏1,李 勇1,袁于思1,黄定于1,张 磊2
((1.中铁武汉电气化局集团第一工程有限公司,武汉430074; 2.武汉科技大学,武汉430081))
Forecasting of mine slope stability based on Adaboost Convolutional Neural Network
FENG Xiaopeng1, LI Yong1, YUAN Yusi1, HUANG Dingyu1, ZHANG Lei2
((1. China Railway Wuhan Electrification Bureau Group Co., Ltd., Wuhan 430074, China; 2. Wuhan University of Science and Technology, Wuhan 430081, China))
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投稿时间:2021-12-09    修订日期:2021-12-27
中文摘要: 为了准确快速地分析露天矿边坡的稳定性,本文提出 Adaboost卷积神经网络(Adaboost-CNN)建立露天矿边坡稳定性和影响因素之间的非线性关系。Adaboost-CNN是结合自适应Boosting算法(Adaboost)和卷积神经网络(CNN)的一种新的机器学习方法。其核心思想是将CNN的特征提取能力和AdaBoost处理非平衡数据的能力结合起来,具有高可靠性、高精度性、训练时间少等优点。Adaboost-CNN利用迁移学习,不仅消除了传统CNN需要大量训练样本的限制,而且解决了AdaBoost算法序列化过程中存在着的降低实际性能的问题。本文分别采用BP神经网络、支持向量机(SVM)、卷积神经网络以及Adaboost-CNN对工程实测数据进行建模与分析,通过对比均方根误差(RMSE)和相对预测误差(RPE),发现Adaboost-CNN的预测精度最高、模型泛化能力最强。结果表明,Adaboost-CNN能够较精确对边坡的稳定性进行预测,是边坡稳定性预测的可靠性工具
Abstract:To accurately and quickly analyze the stability of open-pit mine slopes, this paper proposes Adaboost-Convolutional Neural Network (AdaBoost-CNN) to establish the nonlinear relationship between open-pit mine slope stability and influencing factors. AdaBoost-CNN is a new machine learning method that combines AdaBoost and CNN. The core idea is to combine the feature extraction capabilities of CNN with the ability of AdaBoost to process unbalanced data, which has the advantages of high reliability, high accuracy, and less training time. Adaboost-CNN uses migration learning, which not only eliminates the limitation of CNN that require a large number of training samples but also solves the problem of reducing actual performance in the serialization process of AdaBoost. This paper uses BP neural network, Support Vctor Mchine, CNN, and AdaBoost-CNN to model and analyze the measured engineering data. By comparing RMSE and RPE, it is found that AdaBoost-CNN has the highest prediction accuracy and the strongest model generalization ability. The results show that AdaBoost-CNN can predict slope stability more accurately, and is a reliable tool for slope stability prediction.
文章编号:     中图分类号:P258;X43? ?????????????    文献标志码:
基金项目:国家自然科学基金资助项目(51805382);湖北省安全生产专项资金科技项目(KJZX202007003)
引用文本:
冯小鹏,李 勇,袁于思,黄定于,张 磊.基于AdaBoost卷积神经网络的矿山边坡稳定性预测[J].有色金属(矿山部分),2022,74(3):65-70.
FENG Xiaopeng,LI Yong,YUAN Yusi,HUANG Dingyu,ZHANG Lei.Forecasting of mine slope stability based on Adaboost Convolutional Neural Network[J].NONFERROUS METALS(Mining Section),2022,74(3):65-70.

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