BP neural network-based mine shaft engineering TBM control method

Abstract

The invention discloses a BP neural network-based mine shaft engineering TBM control method. The method comprises the following steps: establishing a relationship model; selecting a training sample and an inspection sample; training the relationship model by using the training sample; inputting the test sample into the trained relation model to obtain a grading prediction result; and regulating torque and thrust through the prediction result, and controlling TBM tunneling equipment to operate. According to the invention, the BP neural network is adopted; the method takes a parameter corresponding to a development law of rock mass grading prediction as an input; the torque and the thrust required by the TBM equipment during tunneling are regarded as output; the relation model is established, after rock mass grading prediction is carried out through the relation model, the torque and thrust needed by TBM equipment during tunneling are automatically regulated and controlled through the prediction result, and therefore the purpose of automatically controlling the TBM tunneling equipment to operate is achieved, and the method has the advantages of being simple in algorithm and high in prediction precision.

Description

technical field [0001] The invention relates to the field of mine excavation engineering, in particular to a mine shaft engineering TBM control method based on BP neural network. Background technique [0002] Tunnel boring machine (TBM) has the advantages of economy and high efficiency, and is widely used in the construction of long tunnels. Among the many parameters that affect the performance of the roadheader, the working torque and thrust have the greatest impact on the performance of the roadheader. TBMs are susceptible to rock mass conditions during mining, and unfamiliar rock mass information may lead to inappropriate operational restrictions and may also result in reduced efficiency and safety during mining. In addition, due to the diversity of the environment and the limitation of space, it is difficult to achieve the purpose of rock mass restriction by field test or direct observation. [0003] Since the total cost of the roadheader is the lowest and the excavatio...

AI Technical Summary

Problems solved by technology

On the whole, the domestic TBM method construction units still control the tunneling parameters based on the analysis of the rock mass stress field, the analysis of relevant monitoring data, and the construction experience. However, the technology of construction units varies, and it is difficult to share control experience. Ineffective

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