Long bones are composite materials possessing nonhomogeneous and anisotropic properties. They repair themselves (self-repairing) and adapt to changing mechanical demands by altering their shape and mechanical properties (self-adapting). Such exceptional features make long bones intriguing materials to comprehend properly. This also expands our knowledge of engineering materials and motivates researchers to employ novel techniques where conventional approaches may present limitations. This paper delves into the use of artificial neural network (ANN) expert systems to predict load-displacement curves of a long bone. Thirteen hydrated third metacarpal (MC3) bones from thoroughbred horses aged from twelve hours to three years were loaded in compression in an MTS machine. Strain readings from one three-gauge rosette and three distinct single-element gauges at different locations on the MC3 midshaft, displacement, load, load exposure time, horse age and bone side (left or right limb) were recorded for each bone. This information shaped ANNs input variables. Two in-series feedforward back-propagation ANNs were employed where the experimental recordings except for load were fed into the first ANN to predict load. Then, the predicted load along with the rest of experimental recordings were fed into the second ANN to predict displacement. Cyclic load-displacement and stiffness predicted by ANNs were plotted versus experimental counterparts. ANNs regression analyses showed R > 0.95 for training and testing datasets. To confirm their accuracy, ANNs were used to predict responses of specific bone samples that were not used in ANNs training. The ANNs trained using 17,718 experimental data points from twelve bones predicted the load (R = 0.997, RMSE = 2.44 kN), displacement (R = 0.948, RMSE = 0.321 mm), and stiffness (R = 0.982, RMSE = 1.197 kN/mm) of the thirteenth bone. The encouraging outcomes exhibit the exceptional ability of artificial neural networks in capturing the mechanical characteristics of complex structures.

长骨是具有非均质和各向异性特性的复合材料。它们可以自我修复（自我修复），并通过改变其形状和机械性能来适应不断变化的机械需求（自适应）。这些出色的功能使长骨头吸引人的材料得以正确理解。这也扩展了我们对工程材料的知识，并激发了研究人员在传统方法可能存在局限性的地方采用新技术。本文深入研究了使用人工神经网络（ANN）专家系统预测长骨的载荷-位移曲线的方法。将来自十二岁至三年的纯种马的13个水合第三掌骨（MC3）骨头在MTS机器中加压加载。记录每个骨骼在MC3中轴上不同位置的一个三号玫瑰花结和三个不同的单元素量规的应变读数，位移，负荷，负荷暴露时间，马龄和骨骼侧面（左或右肢）。这些信息塑造了人工神经网络的输入变量。使用两个串联前馈反向传播ANN，将除负载以外的实验记录输入第一个ANN中以预测负载。然后，将预测的载荷与其余的实验记录一起输入到第二个ANN中，以预测位移。绘制了人工神经网络预测的循环载荷-位移和刚度与实验值的关系图。人工神经网络回归分析表明 记录每个骨骼的马龄和骨骼侧面（左或右肢）。这些信息塑造了人工神经网络的输入变量。使用两个串联前馈反向传播ANN，将除负载以外的实验记录输入第一个ANN中以预测负载。然后，将预测的载荷与其余的实验记录一起输入到第二个ANN中，以预测位移。绘制了人工神经网络预测的循环载荷-位移和刚度与实验值的关系图。人工神经网络回归分析表明 记录每个骨骼的马龄和骨骼侧面（左或右肢）。这些信息塑造了人工神经网络的输入变量。使用两个串联前馈反向传播ANN，将除负载以外的实验记录输入第一个ANN中以预测负载。然后，将预测的载荷与其余的实验记录一起输入到第二个ANN中，以预测位移。绘制了由人工神经网络预测的循环载荷-位移和刚度与实验值的关系图。人工神经网络回归分析表明 预测的载荷与其余的实验记录一起被输入到第二个ANN中，以预测位移。绘制了人工神经网络预测的循环载荷-位移和刚度与实验值的关系图。人工神经网络回归分析表明 预测的载荷与其余的实验记录一起被输入到第二个ANN中，以预测位移。绘制了人工神经网络预测的循环载荷-位移和刚度与实验值的关系图。人工神经网络回归分析表明 对于训练和测试数据集，R > 0.95。为了确认其准确性，人工神经网络用于预测未在人工神经网络训练中使用的特定骨骼样本的反应。使用来自十二个骨骼的17,718个实验数据点训练的ANN预测了载荷（R  = 0.997，RMSE = 2.44 kN），位移（R  = 0.948，RMSE = 0.321 mm）和刚度（R  = 0.982，RMSE = 1.197 kN / mm ）的第十三骨。令人鼓舞的结果表明，人工神经网络在捕获复杂结构的机械特性方面具有非凡的能力。