Evidence-Gated Symbolic Correction for Data-Limited Geomechanical Models

Geomechanical models often retain systematic errors after calibration, but sparse observations may support several competing corrections. We present an evidence-gated symbolic regression framework that retains an existing baseline and evaluates explicit corrections using coefficient fitting, grouped validation, and physical checks. Evolutionary search and optional large language model proposals supply candidates to a common evaluator. Additional observations are requested only when admissible candidates make sufficiently different predictions; unresolved corrections remain provisional. Across 32 cases covering four nonlinear response families and eight previously unused data seeds, predictive-disagreement sampling reduced mean normalized held-out RMSE from 0.193 without acquisition to 0.117. A fixed-budget comparison showed that gating saved 28.1% of space-filling observations at an increase of 0.028 in normalized RMSE. Controlled weak-plane tests verified constitutive-law recovery and FLAC3D material-point implementation. In a separate numerical demonstration of staged cavern excavation, a transferred cohesion-decay correction reduced multi-response RMSE against a prescribed reference from 2.24 to 0.694 mm. Grouped evaluation of four published rock datasets supported two corrections and retained two as provisional. The framework assesses predictive improvement, physical admissibility, and evidence sufficiency without equating a good fit with recovery of the governing equation.