Citation:
Xianguo Wu, Jun Liu, Hongyu Chen, Wen Xu, Xi Liu. Digital Twin Frame System of Shield Tunneling System. Journal of Information Technologyin Civil Engineering and Architecture,
2023, 15(4): 105-110.
doi: 10.16670/j.cnki.cn11-5823/tu.2023.04.18
2023, 15(4): 105-110. doi: 10.16670/j.cnki.cn11-5823/tu.2023.04.18
Digital Twin Frame System of Shield Tunneling System
| 1. | Shool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
| 2. | School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore |
The accurate prediction and parameter control are the key points of shield tunneling system control, hence it is crucial to establish a virtual model which is consistent and synchronous with the tunneling system. In order to realize the intelligent control of the shield tunneling system, this paper introduces the digital twin technology and suitable intelligent equipment into the shield tunneling system to achieve the virtual and real interaction. According to the related requirements of tunneling system, the paper also constructs the digital twin framework of shield tunneling system. Based on the real-time mapping between virtual model and physical entity, the information interaction is realized. Moreover, the paper designs the tunneling load advance prediction system and the shield tunneling construction parameter control and decision system under the service application layer of the tunneling system., which can help intelligent decision-making for shield tunneling system and reach the intelligent management and control of shield tunneling construction.
| [1] | |
| [2] |
刘占省, 李安修, 孟鑫桐, 等. 装配式建筑吊装安全风险管理数字孪生模型建立方法[J]. 土木建筑工程信息技术, 2022, 14(3): 26-33. |
| [3] |
Bondarenko O, Fukuda T. Development of a diesel engine's digital twin for predicting propulsion system dynamics [J]. Energy, 2020, 196: 117126.doi: 10.1016/j.energy.2020.117126 |
| [4] |
郭飞燕, 刘检华, 邹方, 等. 数字孪生驱动的装配工艺设计现状及关键实现技术研究[J]. 机械工程学报, 2019, 55(17): 110-32. |
| [5] |
Wang M, Wang C, Hnydiuk-Stefan A, et al. Recent progress on reliability analysis of offshore wind turbine support structures considering digital twin solutions [J]. Ocean Engineering, 2021, 232: 109168.doi: 10.1016/j.oceaneng.2021.109168 |
| [6] |
Coraddu A, Oneto L, Baldi F, et al. Data-driven ship digital twin for estimating the speed loss caused by the marine fouling [J]. Ocean Engineering, 2019, 186: 106063.doi: 10.1016/j.oceaneng.2019.05.045 |
| [7] |
黄澄, 袁东风, 张海霞. 基于狮群算法的数字孪生车间调度问题优化[J]. 山东大学学报(工学版), 2021, 51(4): 17-23+34. |
| [8] |
郭岳. 基于数字孪生技术的矿井风流调控虚拟系统设计与开发[D]; 长安大学, 2020. |
| [9] |
陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25(1): 1-18. |
| [10] |
经海翔, 黄友锐, 徐善永, 等. 基于数字孪生和概率神经网络的矿用通风机预测性故障诊断研究[J]. 工矿自动化, 2021, 47(11): 53-60. |
| [11] |
李双宇, 张明凯, 刘艳臣, 等. 基于LSTM模型的排水系统流量预测研究[J]. 中国给水排水, 2022, 38(5): 59-64. |
Metrics
- PDF Downloads(37)
- Abstract views(928)
- HTML views(659)
