In this study, to address the overlying strata control issue for the isolated working face of a super-thick coal seam confined by surrounding goafs and open-pit boundaries, we focus on mining via upward slicing strip paste filling in a Ningxia coal mine. We adopt strip coal pillar stability theory for safety factor analysis and conduct FLAC3D three-dimensional numerical simulations to quantitatively reveal overlying strata displacement, stress redistribution, plastic zone evolution, and surface subsidence response. Our theoretical calculations show that, when the mining width is 5 m, the safety factors of retained coal pillars with widths of 5, 10, and 15 m are <1, 1.3, and 1.8, respectively. Our numerical results indicate that the overlying strata of the first slice are dominated by continuous bending subsidence, with a maximum vertical displacement of 21.4 cm, increasing to 55.5 cm after four slices without through damage. High stress is mainly controlled by mined-out area boundaries and inter-face coal pillars, with the maximum principal stress of the fourth slice reaching 18.9 MPa. The surface subsidence center stably corresponds to the underlying backfill goaf, with a maximum value of 11.4 cm. Our research demonstrates that slicing strip filling can suppress deformation and stress concentration risks by reconstructing load transfer and realizing synergistic bearing, as reflected by the limited surface subsidence of 11.4 cm, the controlled maximum principal stress of 18.9 MPa, and the improved coal pillar safety factor from <1 to 1.3–1.8 under wider retained pillars, providing a basis for optimizing strip pillar width and mining–filling parameters.

Carbonation in CO₂-storage solid-waste backfill materials couples CO₂ transport, mineral reaction, and strength evolution, making carbonation-front prediction and transport–reaction inference important for evaluating sequestration performance. This study proposes an evidence-ranked, physics-guided inverse-learning framework for carbonation-front prediction, auxiliary strength reconstruction, PDE-residual assessment, and practical-identifiability analysis. The framework represents carbonation using group-conditioned latent fields of effective CO₂ concentration and remaining reactive capacity, maps latent carbonation degree to measured depth through a differentiable front operator, and reconstructs unconfined compressive strength through a supervised auxiliary head. Empirical front laws and reaction–diffusion physics-informed neural-network variants were evaluated using held-out ranking, repeated stratified splits, residual-weight sweeps, front-operator threshold and smoothing-coefficient sensitivity checks, profile-likelihood and Fisher-information diagnostics, and controlled synthetic tests. Results show that the grouped Weibull front law achieved the best short-range carbonation-depth interpolation, while the retained constant-diffusion PINN was used as a diagnostic formulation within the physics-guided family to improve auxiliary strength reconstruction and to evaluate residual consistency, front-threshold selection, parameter sharing, and inverse-parameter behavior rather than to replace the empirical depth regressor. Increasing the PDE-residual weight substantially reduced residual magnitudes, but profile-likelihood and Fisher-information diagnostics indicated strong parameter trade-offs; the fitted diffusion, reaction, depletion, and diffusion–decay quantities are therefore interpreted as effective, observation-conditional parameters rather than unique material constants. The proposed framework provides a prediction-first and attribution-aware approach for analyzing carbonation evolution in solid-waste backfill materials and supports coordinated assessment of front advancement, strength response, and transport–reaction behavior, while explicitly delimiting the generalization and physical interpretation that can be supported by sparse literature-derived observations.