Long-term compressive load on the lumbar intervertebral disc (IVD) might lead to lumbar IVD herniation. Exploring the material parameters of normal and degenerative enucleated IVDs is the basis for studying their mechanical behavior. According to the inverse analysis principle of the parameter estimation, an optimization method was proposed to identify the parameters of the porous material of the lumbar IVD based on finite element inverse analysis. The poroelastic finite element models were established in line with the compression creep experiment. The material parameters were combined by Box–Behnken design (BBD), and the response surface (RS) models were constructed using a quadratic polynomial with cross terms and optimized by genetic algorithm (GA). The results showed that the simulation result of the best material parameter combination had a good agreement with the experiment. Compared with the normal lumbar IVD, the elastic modulus and permeability decreased, and Poisson's ratio increased for the enucleated disk, resulting in a significant difference in mechanical properties. The algorithm used in this study can reduce the parameter identification error compared with only the RS method, and decrease the number of finite element simulations compared with only the GA.

腰椎间盘（IVD）上的长期压缩负荷可能导致腰椎IVD突出。探索正常去核IVDs的材料参数是研究其力学行为的基础。根据参数估计的反分析原理，提出了一种基于有限元反分析的腰椎IVD多孔材料参数识别的优化方法。根据压缩蠕变实验建立了多孔弹性有限元模型。通过Box–Behnken设计（BBD）组合材料参数，并使用带有交叉项的二次多项式构造响应面（RS）模型，并通过遗传算法（GA）对其进行优化。结果表明，最佳材料参数组合的仿真结果与实验吻合良好。与正常腰椎IVD相比，无核椎间盘的弹性模量和渗透率降低，泊松比增加，从而导致机械性能的显着差异。与仅使用RS方法相比，本研究中使用的算法可以减少参数识别误差，而仅使用GA可以减少有限元仿真的次数。