Implementation of the trending mechanistic-empirical pavement design guide for resilient modulus ($M_r$) estimation, often requires the use of models. The present study proposes a multimodel approach for model performance assessment amongst various stress-based models, in order to identify the existence of a confidence set of models. The multimodel approach involves the use of Akaike information criterion (AIC) and Schwartz Bayesian criterion (SBC), which are respectively based on Akaike’s information theory and Bayes factor approximation. The analysis was performed on $M_r$ data from long-term pavement performance database for fine and coarse-grained soils, including a parametric study of the effect of spatial variability of the soil. The results show that the use of AIC and SBC is robust for handling data, even in the presence of heteroscedasticity. Furthermore, a confidence model set can be proffered using the proposed multimodel approach, irrespective of spatial variability for the fine-grained soil. The proposed approach uses the AIC/SBC differences, 95% Kullback-Leibler confidence set for the Akaike weight and posterior probability, and stability of the model ranking.

弹性模量趋势机械经验路面设计指南的实施（${米}_r$) 估计，通常需要使用模型。本研究提出了一种在各种基于压力的模型中进行模型性能评估的多模型方法，以识别模型置信度集的存在。多模型方法涉及使用 Akaike 信息准则 (AIC) 和 Schwartz Bayesian 准则 (SBC)，它们分别基于 Akaike 的信息理论和贝叶斯因子近似。分析是在${米}_r$来自细粒和粗粒土壤的长期路面性能数据库的数据，包括对土壤空间变异性影响的参数研究。结果表明，即使存在异方差性，使用 AIC 和 SBC 也能很好地处理数据。此外，无论细粒土壤的空间变异性如何，都可以使用所提出的多模型方法提供置信模型集。所提出的方法使用 AIC/SBC 差异、Akaike 权重和后验概率的 95% Kullback-Leibler 置信度集以及模型排名的稳定性。