# [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] 点击复制 基于神经网络算法的滚刀磨损量预测方法() 分享到： var jiathis_config = { data_track_clickback: true };

44卷

2023年01期

83-88

2022-12-06

## 文章信息/Info

Title:
A Method for Disc Cutter Wear Prediction Based on Neural Network

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):
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

Keywords:

TU94
DOI:
10.13705/j.issn.1671-6833.2022.04.009

A

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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