In the small-deformation scenario of high-speed railway (HSR) rigid pile–raft composite foundations, the coupled effects of non-equidistant and non-stationary (NENS) characteristics in observational data significantly affect the system response of prediction models. Existing research has predominantly focused on single evaluation metrics or large-deformation scenarios, lacking a comprehensive evaluation system for the multidimensional performance of these models under small-deformation conditions. NENS time-series data were generated via Monte Carlo simulation. The coupled effect was quantified through a process involving “theoretical curve extraction–non-equidistant sampling–random disturbance injection”. Parameters such as pile length, displacement ratio, and pile–soil modulus ratio were normalized using the composite modulus (CMA) method to uniformly characterize the influence of foundation stiffness on time-varying settlement characteristics. A robust entropy-weighted method was then used to construct a comprehensive evaluation index (CEI), which integrates goodness-of-fit (36%), prediction accuracy (26%), and stability (38%) to systematically assess four empirical models: the hyperbolic method, exponential curve method, Asaoka method, and Hoshino method. The results indicate that when CMA ≤ 100 MPa, settlement curves exhibit nonlinearity, and the Hoshino and hyperbolic methods perform optimally. Between 100 and 1000 MPa, pile–soil interaction intensifies, highlighting the Hoshino method’s superior stability. When CMA ≥ 1000 MPa, pile–soil interaction becomes load-dominated, with the Hoshino method remaining optimal while the hyperbolic and exponential curve methods exhibit significantly increased errors. The proposed NENS time-series simulation–multi-criteria coupling evaluation framework resolves model selection challenges in small-deformation scenarios and provides robust decision support for HSR settlement prediction.
Liu et al. (Wed,) studied this question.