Intelligent prediction method for TBM rock breaking efficiency based on rock slag size
A technology of rock-breaking efficiency and intelligent prediction, applied in the fields of instruments, artificial life, computing, etc., can solve the problem of insufficient research content of intelligent prediction of rock-breaking efficiency, and achieve the effect of improving prediction accuracy.
Pending Publication Date: 2022-03-18
CHONGQING COMM CONSTR GRP +2
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Problems solved by technology
[0003] Most of the existing research is about the prediction of TBM tunneling performance and rolling wear through artificial intelligence methods, and there is very little research on the intelligent prediction of rock breaking efficiency
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Embodiment 1
[0032] Embodiment 1, with reference to Figure 1-2 , an intelligent prediction method for TBM rock-breaking efficiency based on rock slag size, including the following steps:
[0033] Step 1: Install a rock slag shooting device on the TBM slag discharge belt to collect real-time rock slag image information during TBM excavation. The rock slag image capture device is as follows: figure 2 As shown, (1) is an industrial camera, (2) is an LED fill light, (3) is a data storage center, the model of the industrial camera is Canon N10-W02, the number of effective pixels is 1920×1080, and the focal length is 4.3mm. On both sides and above of the TBM slag belt, real-time photos of rock slag are taken. The LED model is nijia200 professional supplementary light, with a maximum illuminance of 70400 lux / 0.5m and a maximum luminous flux of 5800lm. It is used for the slag belt. rock residue for light compensation. The data storage center can transfer the rock slag images obtained by taking...
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The invention discloses a TBM rock breaking efficiency intelligent prediction method based on the rock slag size, and the method comprises the following steps: installing an industrial camera on a TBM slag discharging belt, and collecting the rock slag image information in the TBM tunneling process in real time; carrying out automatic picture identification on the obtained rock slag image information to obtain the shape and geometrical characteristics of rock slag; collecting on-site tunneling parameters, rock slag geometric information characteristics and rock breaking efficiency data of the TBM, and forming a sample data set; and establishing a gradient lifting regression tree model optimized by a particle swarm algorithm by using the sample data set, and realizing intelligent prediction of the TBM rock breaking efficiency. According to the method, the particle swarm optimization is used for optimizing the gradient boosting regression tree model, so that the optimal parameters of the gradient boosting regression tree model are found, the prediction precision of the model is improved, and accurate prediction of the rock breaking efficiency can effectively provide reference for TBM tunneling parameter setting and efficient rock breaking.
Description
technical field [0001] The invention relates to the technical field of TBM rock-breaking efficiency prediction technology, in particular to an intelligent prediction method for TBM rock-breaking efficiency based on rock slag size. Background technique [0002] With the continuous development of underground space, the difficulty of excavation and the level of mechanization of tunnel engineering are constantly improving, and the full-face tunnel boring machine (Tunnel Boring Machine, TBM) has been rapidly developed and applied. Compared with the traditional drill and blast method, which has poor safety and complicated construction procedures, TBM has the following advantages: ① It ensures the safety of construction; ② The excavation speed is 3-10 times that of the traditional drill and blast method; ③ The tunnel forming effect is good, realizing Assembly integration; ④Small noise disturbance and little impact on the surrounding environment. At present, TBM has been widely use...
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Login to View More IPC IPC(8): G06V20/10G06V10/141G06V10/22G06V10/42G06V10/764G06K9/62G06N3/00
CPCG06N3/006G06F18/24323
Inventor 刘国强张庆明汪平刘彬陈勇杨海清王曙光刘勃龙李进汪岳健
Owner CHONGQING COMM CONSTR GRP