The two critical strains of pavement design, under the asphalt layer and on the subgrade, are conventionally obtained by backcalculating layer moduli from a falling weight deflectometer (FWD) basin and propagating them through layered-elastic analysis. That route is ill-posed and confined to the office. We propose the deflection-basin fusion transformer (DBFT), a 0.22M-parameter attention model predicting both strains in one step from the basin and the layer thicknesses, read as a sequence indexed by offset. One objective spans 14,174 layered-elastic solutions and 7,651 field measurements from 96 Thai highway sections, three routes withheld entirely. On exact theory DBFT reaches R^2 >= 0.9998 at RMSE <= 1.24 microstrain, an order of magnitude better than published index equations. Trained on that factorial alone it fails on measured basins, R^2 <= 0.14: the field domain inverts the sign of the outer-basin correlation. The combined objective restores 0.964 and 0.879 on the withheld routes, holding R^2 >= 0.993 on theory. Attention and SHAP recover recognised mechanics, including reliance on the 1200-1800 mm range routine indices never reach. Agreement is with backcalculation-consistent mechanics, not gauge-measured strain. Both strains return in 1.6 ms on one CPU thread.

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