The present study focuses on exploring the relationship between various properties of tire tread composites and filler system using machine learning. Four different types of machine learning algorithms, such as multiple linear regression (MLR), artificial neural network (ANN), support vector machine regression (SVR), and classification and regression tree, are used for predicting 0 °C tanδ, 60 °C tanδ, tensile strength, and Shore A hardness of natural rubber nanocomposites from carbon nanotubes dosage, silica dosage, and total filler equivalent. The results showed that the introduction of interaction terms and square terms into the inputs evidently improved the prediction capability of MLR, ANN and SVR, and MLR possessed the smallest prediction errors (<5%). The established MLR models are further used to design tire tread composites with high 0 °C tanδ, low 60 °C tanδ, and appropriate Shore A hardness and tensile strength. The predicted values are in good agreement with the experimental results, indicating that the established MLR models can be used for properties prediction and design of tire tread composites effectively. Moreover, k‐fold cross‐validation is proved to be a reliable technique to evaluate the predictive capability of the MLR models.
In order to investigate the progressive collapse performance of steel open-web sandwich plate structure, the sensitivity index and the importance coefficient of the bars are analyzed by the alternate path method. The condition that the model has perimeter supports with different parameters shows the result that: the redundancy index of structure increases at the structural edge, and the redundancy index will be reduced to changing degrees at the middle structure, when the stiffness of higher ribs increases. The redundancy index has little change, when the stiffness of lower ribs or shear keys increases. The sensitivity index of the shear keys dropped significantly, but the sensitivity index of the higher ribs and lower ribs increase, when the span to depth ratio increases. The sensitivity index of the higher ribs in L1 line increases significantly, when the span to depth ratio declines. So it is advisable to strengthen the higher ribs to avoid excessive sensitivity of ribs, when the span to depth ratio declines.
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