Physics-data coupling-driven method to predict the penetration depth into concrete targets

Abstract

• Inspired by the task of predicting the penetration depth into concrete targets, a new intelligent paradigm (anomaly detection-empirical algorithm evaluation-domain knowledge fusion) has been proposed for a wider range of tasks of damage effect. • A new idea using neural network models to evaluate empirical algorithms has been proposed to exhibit the optimal one in each parameter interval. • Domain prior knowledge is integrated into three levels called data level, feature level and decision level. • A decision-level fusion module SFM based on self-attention mechanism has been proposed, effectively improving the performance of the predictive model. The projectile penetration process into concrete target is a nonlinear complex problem. With the increase of experiment data, the data-driven paradigm has exhibited a new feasible method to solve such complex problem. However, due to poor quality of experimental data, the traditional machine learning (ML) methods, which are driven only by experimental data, have poor generalization capabilities and limited prediction accuracy. Therefore, this study intends to exhibit a ML method fusing the prior knowledge with experiment data. The new ML method can constrain the fitting to experimental data, improve the generalization ability and the prediction accuracy. Experimental results show that integrating domain prior knowledge can effectively improve the performance of the prediction model for penetration depth into concrete targets.

Topics & Concepts