Prediction Method of Dynamic Normal Stress on the Silo Wall Assisted with Tree Model
It is difficult to accurately predict the normal stress on the silo wall during discharge.To address this issue,a prediction model is proposed for the dynamic normal stress on the silo wall,utilizing a tree model.Firstly,the main influencing factors of the dynamic normal stress on the silo wall were identified as the structural size of the silo,the physical parameters of the storage material,and the position of the measuring point.A data set for the machine learning prediction model was then constructed using the collected 496 groups of dynamic normal stress data on the silo wall.Subsequently,based on the tree model,a DT(decision tree)prediction model for the dynamic normal stress on the wall was established.On this foundation,the RF(random forest)prediction model for dynamic normal stress and the GBDT(gradient boosting decision tree)prediction model were established,employing the Bagging algorithm and the Boosting algorithm respectively.By comparing the MSE(mean square error),determination coefficient,and relative error of the three prediction models in the test set,it is shown that the GBDT prediction model exhibits the best generalization performance.Furthermore,the GBDT prediction model has been verified through model testing and numerical simulation,with satisfactory fitting results.Additionally,according to the branching principle of the tree model,the importance of the influencing factors for the dynamic lateral pressure of the silo is judged,indicating that the density of the storage material and the size of the discharge port rank first for the storage materials and silo structure respectively.Therefore,when designing a silo,it is recommended to prioritize the density of the bulk materials within the silo and the size of the discharge port.
storing silodynamic normal stress on the silo walltree modelparameter optimizationpredicting model
万方数据
维普
