Predicting early-age temperature evolution in mass concrete is crucial for controlling thermal cracks. This process involves two distinct physical stages: an initial, hydration-driven heating stage (Stage I) and a subsequent, environment-dominated cooling stage (Stage II). To address this challenge, we propose a novel two-stage hybrid modeling strategy that decouples the underlying physical processes. This approach was developed and validated using a 450-h on-site monitoring dataset. For the deterministic heating phase (Stage I), we employed polynomial regression. For the subsequent stochastic cooling phase (Stage II), a Random Forest algorithm was used to model the complex environmental interactions. The proposed hybrid model was benchmarked against several alternatives, including a physics-based finite element model (FEM) and a single Random Forest model. During the critical cooling stage, our approach demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of $0.24°\mathrm{C}$. This represents a 17.2% improvement over the best-performing single model. Furthermore, cumulative error analysis indicated that the hybrid model maintained a stable and unbiased prediction trend throughout the monitoring period. This addresses a key weakness in single-stage models, where underlying phase-specific errors can accumulate and lead to long-term drift. The proposed framework offers an accurate, robust, and transferable paradigm for modeling other complex engineering processes that exhibit distinct multi-stage characteristics.

Equivalent thermal calculations describe mean heat transfer, but their accuracy may not extend to local thermal-bridge responses in 3D-printed ribbed walls. We compared the ISO 6946 path-layer method with two-dimensional finite-volume solutions for 2700 dry, steady, insulation-filled periodic cells. Four geometry and conductivity parameters were varied at fixed wall thickness, matrix conductivity, and surface resistances. Numerical benchmarks, mesh checks and ANSYS APDL cross-checks of five selected cases (resistance differences within 0.14%) assessed implementation. Experimental validation was not performed. Resistance error was within 5% for 72.2% of the full grid but for only 6.7% of a 75-case subset selected using literature-reported geometric and conductivity ranges. These proportions characterize the selected domains, not constructed-wall populations. The conductivity contrast at which the resistance error crossed 5% varied with geometry. Resistance errors also changed with wall scale and surface resistance. Sensitivity calculations with air cores used prescribed equivalent resistances without resolving cavity convection or radiation; ISO cavity-geometry compliance was not confirmed. Within the full-grid 5% band, the peak-to-mean heat-flux ratio reached 2.72, and the equivalent model overpredicted minimum internal surface temperature by up to 3.21 K. Global resistance accuracy is therefore not a proxy for local thermal accuracy: conditional error maps can screen resistance, but minimum surface temperature and peak heat flux need explicit-field assessment.