Research on intelligent design of coal mine roadway support scheme

Abstract

Currently, the design of coal mine roadway support schemes is still mainly based on manual design, engineering analogy, and FLAC model simulation, which has problems such as strong subjectivity, low universality, and insufficient utilization of coal mine support big data. The design method based on expert systems has cumbersome rule setting procedures, large engineering quantities, and low intelligence. Case based reasoning (CBR) and deep learning techniques are introduced into the field of roadway support scheme design. Based on text big data such as coal mine support regulations, support standards, and coal mine roadway geological reports, an intelligent design method for coal mine roadway support scheme is proposed. The method obtains 346 sets of roadway support data from different coal mines, extracts structured data and divides it into input and output parameters, and optimizes the input and output parameters through constant attribute variable filtering and high correlation filtering methods. The method establishes a CBR model and imports the extracted structured data into the CBR model to form a case library of support scheme comparison and selection. The method calculates the similarity between the new roadway support scheme and the historical scheme, and outputs the three historical schemes with the highest similarity for comparison, achieving similar case comparison. BP neural network and long short term memory (LSTM) network are respectively used to establish automatic generation models for coal mine roadway support schemes. By comparing the prediction indicators, it is determined to use the combination of LSTM model and CBR model to establish an intelligent design system for coal mine roadway support scheme. The system is used for the design of auxiliary transportation roadway support scheme in the F6226 working face of Buliangou Coal Mine excavation. Through experiments, it is verified that the deformation of the two sides of the roadway and the maximum displacement of the roof under the system generated scheme are smaller than those under the manual design scheme. The integrity of the roadway roof and two sides is good, the bearing capacity of the surrounding rock is enhanced, and the support effect is significant.

Keywords

• coal mine roadway support
• support scheme design
• case based reasoning
• case comparison and selection
• deep learning
• long short term memory network