Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network

2021 ◽  
Vol 125 ◽  
pp. 103647
Author(s):  
Zaobao Liu ◽  
Long Li ◽  
Xingli Fang ◽  
Wenbiao Qi ◽  
Jimei Shen ◽  
...  
Keyword(s):  
Big Data ◽  
Hard Rock ◽  
Attention Mechanism ◽  
Lithology Prediction ◽  
Lstm Network ◽  
Rock Tunnel

scholarly journals Multidisciplinary design optimization of hard rock tunnel boring machine using collaborative optimization

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401875472 ◽  
Author(s):  
Wei Sun ◽  
Xiaobang Wang ◽  
Maolin Shi ◽  
Zhuqing Wang ◽  
Xueguan Song
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Hard Rock ◽  
Tunnel Boring Machine ◽  
Collaborative Optimization ◽  
Optimization Process ◽  
Multidisciplinary Design ◽  
Tunnel Boring ◽  
Geological Conditions ◽  
Rock Tunnel

A multidisciplinary design optimization model is developed in this article to optimize the performance of the hard rock tunnel boring machine using the collaborative optimization architecture. Tunnel boring machine is a complex engineering equipment with many subsystems coupled. In the established multidisciplinary design optimization process of this article, four subsystems are taken into account, which belong to different sub-disciplines/subsytems: the cutterhead system, the thrust system, the cutterhead driving system, and the economic model. The technology models of tunnel boring machine’s subsystems are build and the optimization objective of the multidisciplinary design optimization is to minimize the construction period from the system level of the hard rock tunnel boring machine. To further analyze the established multidisciplinary design optimization, the correlation between the design variables and the tunnel boring machine’s performance is also explored. Results indicate that the multidisciplinary design optimization process has significantly improved the performance of the tunnel boring machine. Based on the optimization results, another two excavating processes under different geological conditions are also optimized complementally using the collaborative optimization architecture, and the corresponding optimum performance of the hard rock tunnel boring machine, such as the cost and energy consumption, is compared and analysed. Results demonstrate that the proposed multidisciplinary design optimization method for tunnel boring machine is reliable and flexible while dealing with different geological conditions in practical engineering.

Predicting Outcomes of Business Process Executions Based on LSTM Neural Networks and Attention Mechanism

2021 ◽  
Author(s):  
Jiaojiao Wang ◽  
Dongjin Yu ◽  
Chengfei Liu ◽  
Xiaoxiao Sun
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Sequential Data ◽  
Time Prediction ◽  
Prediction Time ◽  
Highly Sensitive ◽  
Long Short Term Memory ◽  
Early Decision ◽  
Lstm Network ◽  
Hidden Layer

Abstract To effectively predict the outcome of an on-going process instance helps make an early decision, which plays an important role in so-called predictive process monitoring. Existing methods in this field are tailor-made for some empirical operations such as the prefix extraction, clustering, and encoding, leading that their relative accuracy is highly sensitive to the dataset. Moreover, they have limitations in real-time prediction applications due to the lengthy prediction time. Since Long Short-term Memory (LSTM) neural network provides a high precision in the prediction of sequential data in several areas, this paper investigates LSTM and its enhancements and proposes three different approaches to build more effective and efficient models for outcome prediction. The first move on enhancement is that we combine the original LSTM network from two directions, forward and backward, to capture more features from the completed cases. The second move on enhancement is that we add attention mechanism after extracting features in the hidden layer of LSTM network to distinct them from their attention weight. A series of extensive experiments are evaluated on twelve real datasets when comparing with other approaches. The results show that our approaches outperform the state-of-the-art ones in terms of prediction effectiveness and time performance.

scholarly journals Rock Breaking Performance of TBM Disc Cutter Assisted by High-Pressure Water Jet

2020 ◽  
Vol 10 (18) ◽  
pp. 6294
Author(s):  
Fengchao Wang ◽  
Dapeng Zhou ◽  
Xin Zhou ◽  
Nanzhe Xiao ◽  
Chuwen Guo
Keyword(s):  
High Pressure ◽  
Hard Rock ◽  
Rock Breaking ◽  
Water Jet ◽  
Disc Cutter ◽  
Pressure Water ◽  
High Pressure Water Jet ◽  
Full Face ◽  
Rock Tunnel ◽  
Digging Efficiency

A high-pressure water jet can break rock efficiently, which is of great potential to overcome the problems of a tunnel boring machine (TBM) in full-face hard rock tunnel digging, such as low digging efficiency and high disc cutter wear rate. Therefore, this paper presented a new tunneling method that is a TBM coupled with a high-pressure water jet. The rock failure mechanism under the coupled forces of a disc cutter and water jet was analyzed at first. Then, the finite element method (FEM) and smoothed particle hydrodynamics (SPH) method were used to establish a numerical model of rock broken by the disc cutter and water jet. Effects of parameters on rock breaking performance were studied based on the numerical model. Moreover, an experiment of the water jet cutting marble was carried out to verify the reliability of the numerical simulation. Results showed that the high-pressure water jet can increase the TBM digging efficiency and decrease the forces and wear rate of the disc cutter. The optimum nozzle diameter is 1.5 mm, while the optimum jet velocity is 224.5 m/s in this simulation. The results can provide theoretical guidance and data support for designing the most efficient system of a TBM with a water jet for digging a full-face hard rock tunnel.

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