The Pistoia criterion (PC) is widely used to estimate the failure load of distal radius segments based on linear micro Finite Element (μFE) analyses. The advantage of the PC is that a simple strain-threshold and a tissue volume fraction can be used to predict failure properties. In this study, the PC is compared to materially nonlinear μFE analyses, where the bone tissue is modelled as an elastic, damageable material. The goal was to investigate for which outcomes the PC is sufficient and when a nonlinear (NL) simulation is required. Three types of simulation results were compared: (1) prediction of the failure load, (2) load sharing of cortical and trabecular regions, and (3) distribution of local damaged/overstrained tissue at the maximum sustainable load. The failure load obtained experimentally could be predicted well with both the PC and the NL simulations using linear regression. Although the PC strongly overestimated the failure load, it was sufficient to predict adequately normalized apparent results. An optimised PC (oPC) was proposed which uses experimental data to calibrate the individual volume of overstrained tissue. The main areas of local over-straining predicted by the oPC were the same as estimated by the NL simulation, although the oPC predicted more diffuse regions. However, the oPC relied on an individual calibration requiring the experimental failure load while the NL simulation required no a priori knowledge of the experimental failure load.

皮斯托亚标准（PC）被广泛用于基于线性微有限元（μFE）分析来估计radius骨远端段的破坏载荷。PC的优点是简单的应变阈值和组织体积分数可用于预测失效特性。在这项研究中，将PC与材料非线性μFE分析进行了比较，在分析中，骨组织被建模为一种弹性的，易损坏的材料。目的是研究PC对于哪些结果是足够的以及何时需要非线性（NL）仿真。比较了三种类型的仿真结果：（1）预测失败载荷；（2）皮质和小梁区域的载荷分配；（3）在最大可持续载荷下局部受损/过度劳累的组织的分布。使用线性回归通过PC和NL仿真可以很好地预测实验获得的失效载荷。尽管PC强烈高估了故障负荷，但足以预测足够标准化的表观结果。提出了一种优化的PC（oPC），该PC使用实验数据来校准过度劳累的组织的各个体积。尽管oPC预测更多的扩散区域，但oPC预测的局部过度应变的主要区域与NL模拟所估计的区域相同。但是，oPC依赖于需要实验失败载荷的单独校准，而NL模拟则不需要先验知识就可以知道实验失败载荷。足以预测足够标准化的表观结果。提出了一种优化的PC（oPC），该PC使用实验数据来校准过度劳累的组织的各个体积。尽管oPC预测更多的扩散区域，但oPC预测的局部过度应变的主要区域与NL模拟所估计的区域相同。但是，oPC依赖于需要实验失败载荷的单独校准，而NL模拟则不需要先验知识就可以知道实验失败载荷。足以预测足够标准化的表观结果。提出了一种优化的PC（oPC），该PC使用实验数据来校准过度劳累的组织的各个体积。尽管oPC预测更多的扩散区域，但oPC预测的局部过度应变的主要区域与NL模拟所估计的区域相同。但是，oPC依赖于需要实验失败载荷的单独校准，而NL模拟则不需要先验知识就可以知道实验失败载荷。