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Numerical sample and random forest-based TBM jamming risk prediction method

A technology of risk prediction and random forest, which is applied in special data processing applications, instruments, calculation models, etc., can solve the problems of difficult and fast and real-time prediction of machine jam risk, and achieve the problem of overcoming imbalance, overcoming the small number of monitoring samples, and fast prediction Effect

Active Publication Date: 2021-05-07
TSINGHUA UNIV
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Problems solved by technology

[0006] In order to overcome the problem that the jamming risk in the current TBM tunnel construction is difficult to predict quickly and in real time, the present invention proposes a TBM jamming risk prediction method based on numerical samples and random forests

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  • Numerical sample and random forest-based TBM jamming risk prediction method
  • Numerical sample and random forest-based TBM jamming risk prediction method
  • Numerical sample and random forest-based TBM jamming risk prediction method

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[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0056] Such as Figure 1 to Figure 6 As shown, the present invention provides a kind of TBM stuck machine risk prediction method based on numerical sample and random forest, its implementation process is as follows figure 1 shown, including the following steps:

[0057] Step S1, establishing a refined numerical simulation model, simulating the time-dependent deformation of the surrounding rock based on the creep damage model, and realizing the simulation of the TBM construction process;

[0058] Taking a tunnel project as the background, based on FLAC 3D (Fast Lagrangian Analysis of Continua 3D, fast Lagrangian ...

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Abstract

The invention discloses a numerical sample and random forest-based TBM jamming risk prediction method. The method comprises the steps: building a refined numerical simulation model, simulating the aging deformation of surrounding rock based on a creep damage model, and achieving the simulation of a TBM construction process; setting values of different card machine influence factors in the numerical simulation model, and calculating numerical samples containing different working conditions; establishing a jamming risk discrimination index, and calibrating the jamming risk level of the sample; and establishing a random forest model, performing model training based on the numerical samples, and predicting the jamming risk level of the actual construction section by using the trained random forest model. According to the method, the jamming numerical value sample library is constructed based on refined numerical value simulation, and the problems of few monitoring samples and imbalance existing in application of machine learning in engineering are solved; by using the trained random forest model, the jamming risk level of the actual construction section can be quickly predicted, so that early prevention and control of disasters are guided, and safe and efficient construction of the TBM is ensured.

Description

technical field [0001] The invention relates to the technical field of TBM (Tunnel Boring Machine, tunnel boring machine) excavation construction, in particular to a method for predicting the risk of TBM jamming based on numerical samples and random forests. Background technique [0002] With the rapid development of my country's economy, it has led to the increase of infrastructure construction. In recent years, key projects and projects in the fields of water conservancy, transportation, and mining have all involved the construction of deep-buried long tunnels. Compared with the drilling and blasting method, TBM has the advantages of fast construction speed, less disturbance to the surrounding rock and less disturbance to the ecology, and is more and more widely used in tunnel engineering. Deep tunnel engineering often faces the problems of high ground stress and strong excavation disturbance. The deep rock mass has obvious time-dependent deformation characteristics after...

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Application Information

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IPC IPC(8): G06F30/20G06N3/00
CPCG06F30/20G06N3/006
Inventor 刘耀儒侯少康庄文宇张凯
Owner TSINGHUA UNIV
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