基于WaveNet网络的盾构刀盘扭矩超前预测方法
王伯芝1丁爽2,3黄永亮1陈文明2,4谢浩1彭子晖5吴炜枫5王嘉烨5
Advanced Prediction Method for Shield Tunneling Cutterhead Torque Based on WaveNet Network
WANG Bozhi1DING Shuang2,3HUANG Yongliang1CHEN Wenming2,4XIE Hao1PENG Zihui5WU Weifeng5WANG Jiaye5
-
作者信息:1.济南轨道交通集团有限公司，250013，济南
2.上海同岩土木工程科技股份有限公司，200092，上海
3.土木信息技术教育部工程研究中心，200092，上海
4.上海地下基础设施安全检测与养护装备工程技术研究中心，200092，上海
5.上海市隧道工程轨道交通设计研究院，200235，上海
-
Affiliation:1.Jinan Rail Transit Group Co., Ltd., 250013, Jinan,China
2.Shanghai Tongyan Civil Engineering Technology Co., Ltd., 200092, Shanghai, China
3.Engineering Research Center of Ministry of Civil Information Technology Education, Tongji University, 200092, Shanghai, China
4.Shanghai Underground Infrastructure Safety Testing and Maintenance Equipment Engineering Technology Research Center, 200092, Shanghai, China
5.Shanghai Tunnel Engineering & Rail Transit Design and Research Institute, 200235, Shanghai, China
-
关键词:
-
Key words:
-
DOI:10.16037/j.1007-869x.2024.07.005
-
中图分类号/CLCN:U455.43
-
栏目/Col:学术专论
摘要:
［目的］刀盘扭矩是表征盾构掘进安全和设备运行状态的关键参数，为了解决刀盘扭矩预测难、掘进参数难以及时修正的问题，提出一种基于WaveNet网络的盾构刀盘扭矩超前预测方法。［方法］介绍了工况数据的预处理方法，并提出基于WaveNet网络的初始静态模型基本架构和构建方式；提取初始50 m掘进距离内的盾构施工监控历史数据，并构建训练集；基于当前状态前20个施工步的盾构监控数据，超前预测5个施工步后的刀盘扭矩；随着盾构掘进距离的增加，每隔5个施工步就利用新产生的数据集重新训练和更新模型，提出刀盘扭矩超前预测的长期动态模型；以济南轨道交通1号线玉符河站—王府庄站区间盾构隧道左线数据为例，对刀盘扭矩预测效果进行分析及验证。［结果及结论］前50m掘进距离超前预测的刀盘扭矩值与实际值变化趋势基本一致，且其平均相对误差为10.07%，初始静态模型具有较高的预测精度。随着掘进距离的增加，初始静态模型相对误差从10%上升至30%左右，而连续更新的长期动态模型相对误差始终稳定在10%左右。长期动态模型每次的更新时间基本分布在1～6 s，平均耗时为3.92 s，可满足模型高效动态更新的需求。
Abstracts:
［Objective］ Cutterhead torque is a crucial parameter that characterizes the safety of shield tunneling and the operating status of equipment. To address the difficulties in cutterhead torque prediction and excavation parameters timely correction, an advanced prediction method for shield tunneling cutterhead torque based on WaveNet network is proposed. ［Method］ The preprocessing method for working condition data is introduced, and the basic structure and construction method of the initial static model based on WaveNet network are proposed. A training set is constructed by extracting historical data of shield construction monitoring within the initial 50 m tunneling distance, and advanced prediction of the cutterhead torque is made after five construction steps based on the shield construction monitoring data of the previous 20 construction steps. With the increase of shield excavation distance, the model is retrained and updated every five construction steps using newly generated data set, thus a long-term dynamic model for cutterhead torque advanced prediction is proposed. Taking the left-line data of the Yufuhe Sta.-Wangfuzhuang Sta. shield tunnel interval of Jinan Rail Transit Line 1 as example, the prediction effect of cutterhead torque is analyzed and verified. ［Result & Conclusion］ The cutterhead torque values by advanced prediction for the first 50 m tunneling distance show a basic consistency with the changing trend of actual values, with an average relative error of 10.07%. The initial static model exhibits relatively high prediction accuracy. As the tunneling distance increases, the relative error of the initial static model increases from 10% to about 30%, while that of the continuously updated long-term dynamic model remains stable at around 10%. The update time of the long-term dynamic model is generally distributed between 1 and 6 seconds, with an average time consumption of 3.92 s, meeting the requirement for efficient dynamic model updates.
- 上一篇: 高铁物流运输模式及其可行性
- 下一篇: 动车组制动系统具有换代特征的技术进步
过刊浏览
资料下载
友情链接