针对弹体对混凝土材料侵彻深度问题，通过量纲分析和神经网络理论，建立了弹体侵彻深度h网络输出量与弹体长度lp、弹的长径比 lp/d、弹体形状系数ψ、弹体与混凝土的比强度σyt/σyp、弹体与混凝土的密度比ρp/ρt等13个网络输入量之间的非线性映射关系。并采用 RBF网络模型，通过Forrestal等文献的试验样本对网络模型训练，获得了弹体对混凝土材料侵彻深度的网络模型，输出结果满意。

In this article,  nonlinear mapping relation between input of 13 variables of lp and σyt/σyp etc. , and output of penetration depth is established by dimensional analysis and theory of artificial neural networks for problem of penetration depth of projectiles into concrete.  Moreover,  a satisfied output about penetration depth from RBF neural network is gotten by a group of input sets and corresponding output sets,  which comes from M.  J.  Forrestal 's document.