Superelastic shape memory alloys (SMAs) are capable of recovering large inelastic deformation and dissipating energy under loading reversals, and therefore present promising application in vibration control of engineering structures subjected to dynamic loads. Properties of SMAs are often optimised based on experimental results and treated as deterministic for dynamic analysis of structures with SMA-based devices. This study applies the Metropolis–Hasting algorithm to characterise the uncertainties within an SMA material model and to provide insight into its parameter uncertainty. Cyclic tests of SMA bars are first conducted and the experimental data are analysed using the Markov chain Monte Carlo method. The statistical properties of SMA model parameters are calculated based on posterior parameter distributions. The influence of SMA model parameter uncertainty is further explored on energy dissipation capacity under cyclic tests. The first four L-moment method is applied to transform the posterior parameter distributions into standard normal distribution to further evaluate the influence of model parameter uncertainty on energy dissipation in dynamic analysis.

超弹性形状记忆合金（SMAs）能够在逆转载荷下恢复较大的非弹性变形并耗散能量，因此在动载荷作用下的工程结构振动控制中具有广阔的应用前景。SMA的属性通常基于实验结果进行优化，并被视为基于SMA的设备进行结构动态分析的确定性。这项研究应用了Metropolis-Hasting算法来表征SMA材料模型中的不确定性，并深入了解其参数不确定性。首先进行SMA棒的循环测试，并使用Markov链蒙特卡洛方法分析实验数据。基于后验参数分布来计算SMA模型参数的统计特性。在循环测试下，进一步探讨了SMA模型参数不确定性对能量耗散能力的影响。应用前四个L矩方法将后验参数分布转换为标准正态分布，以进一步评估模型参数不确定性对动态分析中能量耗散的影响。