Abstract:

In order to ensure the safety of coal mining engineering, a high-resilience safety assessment method based on AdaBoost was proposed. Firstly, the tunnel design parameters, geological condition parameters, and construction parameters were taken as the input of the model, and the mine settlement value was taken as the output. The input parameter data collected at the construction site were divided into a training data set and a test data set. Secondly, multiple data sets were extracted from the data set to establish multiple sub-models respectively. Then, the errors and weights of the sub-models were calculated respectively, and the neural network was used as the benchmark model. All the training data sets were divided into normal data and abnormal data, and the weights were updated. Finally, according to the weighted data set, the AdaBoost prediction model was built, and case analysis was carried out. The effectiveness of the method was verified. The results show that the high-resilience safety assessment method using AdaBoost established based on normal data and abnormal data has higher accuracy than the results obtained by directly using the AdaBoost algorithm, which proves that the proposed method is scientific and effective.

Key words: coal mining engineering, AdaBoost, high resilience, safety assessment, abnormal data, normal data