This study reports the development of an artificial neural network computation model to predict the accumulation of crack density and crack length in cancellous bone under a cyclic load. The model was then applied to conduct a parametric investigation into the effects of load level on fatigue crack accumulation in cancellous bone. The method was built in three steps: (1) conducting finite element simulations to predict fatigue growth of different three-dimensional micro-computed tomography cancellous bone specimens considering input combinations based on a factorial experimental design; (2) performing a training stage of an artificial neural network based on the results of step 1; and (3) applying the trained artificial neural network to rapidly predict the crack density and the crack length growth for cancellous bone under a cyclic loading for a given applied apparent strain, cycle frequency, bone volume fraction, bone density and apparent elastic modulus.

中文翻译：

---

基于人工神经网络计算的载荷水平对小梁骨疲劳裂纹扩展影响的参数研究。

这项研究报告了人工神经网络计算模型的发展，该模型可预测循环载荷下松质骨中裂纹密度和裂纹长度的累积。然后将该模型应用于负荷水平对松质骨疲劳裂纹累积影响的参数研究。该方法分为三个步骤：（1）进行有限元模拟，以基于阶乘实验设计，考虑输入组合，预测不同的三维显微计算机断层扫描松质骨标本的疲劳增长；（2）根据步骤1的结果进行人工神经网络的训练。