A Multi-Scale Computational Framework for Durability-State Reconstruction and Sequential Updating of Environmentally Aged Textile-Reinforced Mortar

Durability assessments of textile-reinforced mortar (TRM) are often fragmented across exposure conditions, test scales, and observation times. An existing durability campaign on glass- and basalt-textile TRM was reorganized into a hierarchical, condition-specific state comprising matrix, textile pull-out, and single-lap bond responses, preserving the different pull-out definitions the two textile systems require. A frozen, target-blind linear transition rule was assessed by withheld 3000 h reconstruction across 43 prediction records corresponding to 31 unique physical targets, the difference arising because matrix observations are shared by both textile branches. Single-lap retention was reconstructed with the lowest errors, at median absolute percentage errors of 3.2% for glass and 6.8% for basalt, whereas the basalt pull-out coordinates reached 84.7% and 175.9% and produced four physically inadmissible negative extrapolations, indicating structural mismatch with non-monotonic bond trajectories. Benchmarked against persistence baselines using identical information, the rule was approximately unbiased but did not achieve a lower absolute error than carrying the 5000 h state backwards. The 5000 h extrapolation was shown to be algebraically determined by the reconstruction residuals, the signed errors differing by an exact factor of minus two, so the two temporal exercises are not independent confirmation. Cross-fitted sequential updating did not improve the withheld single-lap estimate under either training design, the fitted coefficient was indistinguishable from zero in every fold, and propagated uncertainty increased throughout. One of fifteen cross-scale associations survived multiplicity correction. The contribution is therefore architectural and evidential, identifying where state-transition assumptions are informative, mixed, or structurally unsupported, rather than evidence of external predictive validity.