Bone is a nonlinear, inhomogeneous and anisotropic material. To predict the behavior of bones expert systems are employed to reduce the computational cost and to enhance the accuracy of simulations. In this study, an artificial neural network (ANN) was used for the prediction of displacement in long bones followed by ex-vivo experiments. Three hydrated third metacarpal bones (MC3) from 3 thoroughbred horses were used in the experiments. A set of strain gauges were distributed around the midshaft of the bones. These bones were then loaded in compression in an MTS machine. The recordings of strains, load, load exposure time, and displacement were used as ANN input parameters. The ANN which was trained using 3,250 experimental data points from two bones predicted the displacement of the third bone (R² ≥ 0.98). It was suggested that the ANN should be trained using noisy data points. The proposed modification in the training algorithm makes the ANN very robust against noisy inputs measurements. The performance of the ANN was evaluated in response to changes in the number of input data points and then by assuming a lack of strain data. A finite element analysis (FEA) was conducted to replicate one cycle of force-displacement experimental data (to gain the same accuracy produced by the ANN). The comparison of FEA and ANN displacement predictions indicates that the ANN produced a satisfactory outcome within a couple of seconds, while FEA required more than 160 times as long to solve the same model (CPU time: 5 h and 30 min).

骨骼是一种非线性，不均匀且各向异性的材料。为了预测骨骼的行为，采用专家系统来减少计算成本并提高模拟的准确性。在这项研究中，人工神经网络（ANN）用于预测长骨的位移，然后进行离体实验。实验中使用了来自3匹纯种马的3个水合的第三掌骨（MC3）。一组应变仪分布在骨骼的中轴周围。然后将这些骨骼压缩加载到MTS机器中。应变，载荷，载荷暴露时间和位移的记录用作ANN输入参数。使用来自两个骨骼的3,250个实验数据点训练的ANN预测了第三骨骼的位移（R ² ≥0.98）。有人建议应使用嘈杂的数据点对人工神经网络进行训练。训练算法中的拟议修改使ANN能够很好地抵抗嘈杂的输入测量。响应输入数据点数量的变化，然后通过假设缺少应变数据来评估ANN的性能。进行了有限元分析（FEA），以复制一个周期的力-位移实验数据（以获得与ANN相同的精度）。FEA和ANN位移预测的比较表明，ANN在几秒钟内产生了令人满意的结果，而FEA求解相同模型所需的时间是160倍以上（CPU时间：5小时30分钟）。