Prediction of tbm oil temperature by improved camel algorithm assisted random forest model (mt)

In order to obtain the classification prediction performance of TBM oil temperature, a camel walking resistance and walking endurance strategy based on natural weather phenomena was proposed to improve the camel algorithm to optimize the prediction model of random forest. Firstly, the traditional camel algorithm was improved by using the proposed strategies. The results showed that the improved camel algorithm has good convergence speed and convergence accuracy. Secondly, the improved camel algorithm was used to optimize the parameters of the TBM oil temperature prediction model established by the random forest to obtain the best model. Finally, on this basis, the classification prediction research and analywis of the test data set was carried out. The experimental results show that the prediction accuracy of the proposed model reaches 97.71%, compared with the traditional random forest model, the accuracy is mproved 6.38%, which can achieve the purpose of avoiding the failure of the shield machine caused by high oil temperature. It provides the basis for the whoce machine material-structure-control future multi-disciplinary cooperative optimization design and performance prediction.