Research on rock drill ability prediction based on PCA-LM-BP neural network

Abstract:

Predicting the drill ability level of rocks can provide effective assistance for the development of drilling engineering projects， and selecting reasonable processes， methods， and technologies based on the drillability level of rocks can provide technical support and assistance for the project. In this paper， considering the impact of complex environmental factors on rocks in underground space， five factors affecting rock drill ability grade are selected from geophysical exploration data， mechanical properties and physical properties of rocks. Principal component analysis （PCA） is used to explain the correlation and contribution rate between each influencing factor， and the correlation between the five factors is eliminated. Three principal components with low correlation were selected to replace the data samples for prediction evaluation. The LM-BP algorithm was compiled， reasonably set the parameter values of the prediction model， and based on the data samples after principal component analysis， establish a rock drill ability level prediction model. Analyze and compare the prediction results with the measured results of indoor experimental methods. It was found through analysis that the PCA-LM-BP prediction model has the characteristics of high prediction accuracy and short prediction time in rock drill ability level prediction， it can be applied to rock drill ability analysis in drilling engineering.