Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data

2018 ◽  
Vol 92 ◽  
pp. 23-34 ◽  
Author(s):  
Wei Sun ◽  
Maolin Shi ◽  
Chao Zhang ◽  
Junhong Zhao ◽  
Xueguan Song
Keyword(s):  
Dynamic Load ◽  
Tunnel Boring Machine ◽  
Load Prediction ◽  
Tunnel Boring ◽  
In Situ Data

scholarly journals Geological Type Recognition by Machine Learning on In-Situ Data of EPB Tunnel Boring Machines

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qian Zhang ◽  
Kaihong Yang ◽  
Lihui Wang ◽  
Siyang Zhou
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Recognition Performance ◽  
Selection Strategy ◽  
Pressure Balance ◽  
Tunnel Boring ◽  
In Situ Data ◽  
Geological Conditions ◽  
Geological Information

At present, many large-scale engineering equipment can obtain massive in-situ data at runtime. In-depth data mining is conducive to the real-time understanding of equipment operation status or recognition of service environment. This paper proposes a geological type recognition system by the analysis of in-situ data recorded during TBM tunneling to address geological information acquisition during TBM construction. Owing to high dimensionality and nonlinear coupling between parameters of TBM in-situ data, the dimensionality reduction feature engineering and machine learning methods are introduced into TBM in-situ data analysis. The chi-square test is used to screen for sensitive features due to the disobedience to common distributions of TBM parameters. Considering complex relationships, ANN, SVM, KNN, and CART algorithms are used to construct a geology recognition classifier. A case study of a subway tunnel project constructed using an earth pressure balance tunnel boring machine (EPB-TBM) in China is used to verify the effectiveness of the proposed geological recognition method. The result shows that the recognition accuracy gradually increases to a stable level with the increase of input features, and the accuracy of all algorithms is higher than 97%. Seven features are considered as the best selection strategy among SVM, KNN, and ANN, while feature selection is an inherent part of the CART method which shows a good recognition performance. This work provides an intelligent path for obtaining geological information for underground excavation TBM projects and a possibility for solving the problem of engineering recognition of more complex geological conditions.

scholarly journals Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shanglin Liu ◽  
Kaihong Yang ◽  
Jie Cai ◽  
Siyang Zhou ◽  
Qian Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Short Term Memory ◽  
Time Window ◽  
Tunnel Boring Machine ◽  
Heavy Load ◽  
In Situ Data ◽  
Geological Conditions ◽  
Geological Information

A tunnel boring machine (TBM) is a type of heavy load equipment that is widely used in underground tunnel construction. The geological conditions in the tunneling process are decisive factors that directly affect the control of construction equipment. Because TBM tunneling always takes place underground, the acquisition of geological information has become a key issue in this field. This study focused on the internal relationships between the sequential nature of tunnel in situ data and the continuous interaction between equipment and geology and introduced the long short-term memory (LSTM) time series neural network method for processing in situ data. A method for predicting the geological parameters in advance based on TBM real-time state monitoring data is proposed. The proposed method was applied to a tunnel project in China, and the R2 of the prediction results for five geological parameters are all higher than 0.98. The performance of the LSTM was compared with that of an artificial neural network (ANN). The prediction accuracy of the LSTM was significantly higher compared with that of the ANN, and the generalization and robustness of LSTM are also better than those of ANN, which indicates that the proposed LSTM method could extract the sequence properties of the in situ data. The rule of equipment-geology interaction was reflected by increasing the memory structure of the model through the introduction of the “gate” concept, and the accurate prediction of geological parameters during tunneling was realized. Additionally, the influence of time window and distance of prediction on the model is discussed. The proposed method provides a new approach toward obtaining geological information during TBM construction and also provides a certain reference for the effective analysis of the in situ data with sequence properties.

Transient Property Improvement of Hydraulic Cylinder Control for Tunnel Boring Machine Using Opposite Reference Filter

2020 ◽  
Vol 140 (3) ◽  
pp. 320-325
Author(s):  
Yoshihiro Ohnishi ◽  
Takahisa Shigematsu ◽  
Takuma Kawai ◽  
Shinichi Kawamura ◽  
Noboru Oda
Keyword(s):  
Tunnel Boring Machine ◽  
Hydraulic Cylinder ◽  
Tunnel Boring

STORM ICE OIL WIND WAVE WATCH SYSTEM (SIOWS): WEB GIS APPLICATION FOR MONITORING THE ARCTIC

2017 ◽  
Author(s):  
Alexander Myasoedov ◽  
Alexander Myasoedov ◽  
Sergey Azarov ◽  
Sergey Azarov ◽  
Ekaterina Balashova ◽  
...  
Keyword(s):  
Satellite Data ◽  
Wind Wave ◽  
Arctic Region ◽  
Web Gis ◽  
The Arctic ◽  
Flexible Tool ◽  
In Situ Data ◽  
Current State ◽  
Watch System

Working with satellite data, has long been an issue for users which has often prevented from a wider use of these data because of Volume, Access, Format and Data Combination. The purpose of the Storm Ice Oil Wind Wave Watch System (SIOWS) developed at Satellite Oceanography Laboratory (SOLab) is to solve the main issues encountered with satellite data and to provide users with a fast and flexible tool to select and extract data within massive archives that match exactly its needs or interest improving the efficiency of the monitoring system of geophysical conditions in the Arctic. SIOWS - is a Web GIS, designed to display various satellite, model and in situ data, it uses developed at SOLab storing, processing and visualization technologies for operational and archived data. It allows synergistic analysis of both historical data and monitoring of the current state and dynamics of the "ocean-atmosphere-cryosphere" system in the Arctic region, as well as Arctic system forecasting based on thermodynamic models with satellite data assimilation.

Integral hoisting technology of tunnel boring machine in limited space

2016 ◽  
Vol 33 (3) ◽  
pp. 317
Author(s):  
Fei Wang ◽  
Mengbo Liu ◽  
Long Chen ◽  
Wen Liu ◽  
Linmeng Tang
Keyword(s):  
Tunnel Boring Machine ◽  
Tunnel Boring ◽  
Limited Space

scholarly journals Critical Dynamics of Defects and Mechanisms of Damage-Failure Transitions in Fatigue

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2554
Author(s):  
Oleg Naimark ◽  
Vladimir Oborin ◽  
Mikhail Bannikov ◽  
Dmitry Ledon
Keyword(s):  
Fatigue Damage ◽  
Kinetic Equations ◽  
Experimental Methodology ◽  
Preliminary Deformation ◽  
Scaling Properties ◽  
In Situ Data ◽  
Number Of Cycles ◽  
Fish Eye ◽  
Very High

An experimental methodology was developed for estimating a very high cycle fatigue (VHCF) life of the aluminum alloy AMG-6 subjected to preliminary deformation. The analysis of fatigue damage staging is based on the measurement of elastic modulus decrement according to “in situ” data of nonlinear dynamics of free-end specimen vibrations at the VHCF test. The correlation of fatigue damage staging and fracture surface morphology was studied to establish the scaling properties and kinetic equations for damage localization, “fish-eye” nucleation, and transition to the Paris crack kinetics. These equations, based on empirical parameters related to the structure of the material, allows us to estimate the number of cycles for the nucleation and advance of fatigue crack.

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