[1]丁小彬,谢宇轩,薛皓文,等.基于神经网络算法的滚刀磨损量预测方法[J].郑州大学学报(工学版),2023,44(01):83-88.[doi:10.13705/j.issn.1671-6833.2022.04.009]
　DING Xiaobin,XIE Yuxuan,XUE Haowen,et al.A Method for Disc Cutter Wear Prediction Based on Neural Network[J].Journal of Zhengzhou University (Engineering Science),2023,44(01):83-88.[doi:10.13705/j.issn.1671-6833.2022.04.009]
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基于神经网络算法的滚刀磨损量预测方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年01期
页码:
83-88
栏目:
出版日期:
2022-12-06

文章信息/Info

Title:
A Method for Disc Cutter Wear Prediction Based on Neural Network
作者:
丁小彬1; 2;  谢宇轩1;  薛皓文1;  李世佳3
1. 华南理工大学 土木与交通学院,广东 广州 510640; 2. 华南理工大学 华南岩土工程研究院,广东 广州 510640; 3. 广州轨道交通建设监理有限公司,广东 广州 510010
Author(s):
DING Xiaobin1; 2;  XIE Yuxuan1;  XUE Haowen1;  LI Shijia3
1.School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China; 2.South China Institute of Geotechnical Engineering, South China University of Technology, Guangzhou 510640, China; 3.Guangzhou Mass Transit Engineering Consultant Co., Ltd., Guangzhou 510010, China
关键词:
Keywords:
shield tunnel;  disc cutter wear;  improved BP neural network;  optimization algorithm
分类号:
TU94
DOI:
10.13705/j.issn.1671-6833.2022.04.009
文献标志码:
A
摘要:
为了给盾构施工开仓换刀提供参考，本文将滚刀磨损简化为多元非线性拟合问题，构建了数据分析框架，综合考虑机械、地质、管理三大类因素，对滚刀磨损量进行预测。以广州地铁18号线番禺广场到南村万博站区间盾构区间为工程背景，选取共14种参数，结合公式获得标定后数据共2386条，包含34把正面滚刀，共81次滚刀磨损量。通过SMBO方法和LM算法改进BPNN算法训练过程，充分发挥神经网络优势，所得模型对83.3%的测试样本的预测值决定系数(R2)高于0.86，相比标记样本时参考的公式，准确度有较大提高，表明该方法所训练模型对滚刀磨损量的发展趋势预测更加准确。
Abstract:
To provide a reference for manual cutter inspection in shield tunneling, in this study the wear of disc cutters was simplified as a multivariate nonlinear regression problem, and constructs a data analysis framework was constructed to predict the cutter wear quantitatively by combining the effect of three kinds of factors, which were machinery, geology and management. The shield tunnel section from Panyu Square Station to Nancun Wanbo Station of Guangzhou Metro Line 18 was taken as the engineering background, 4 parameters were selected and 2 386 labeled data derived from 34 face cutters and 81 manual inspections were obtained. The training of BPNN was expedited by using the LM algorithm and SMBO method, which fully exploitd the regression ability of the neural network. The prediction got coefficients of determination (R2) over 0.86 for 83.3% of the test samples, and the accuracy was greatly improved compared with the reference formula used for data labeling. It showed that the model trained by this method had higher accuracy in the prediction of disc cutter wear.

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更新日期/Last Update: 2022-12-07