发表机构
King Mongkut’s University of Technology Thonburi (KMUTT)（泰国皇家理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出0.22M参数的弯沉盆融合Transformer，一步预测路面关键应变，结合理论解与现场数据训练，精度远超传统方法，速度快至1.6毫秒。
AI 中文摘要
路面设计的两个关键应变，即沥青层下和路基上的应变，通常通过从落锤式弯沉仪（FWD）弯沉盆反算层模量，并通过层状弹性分析传播这些模量来获得。该路径是不适定的，且局限于办公室环境。我们提出了弯沉盆融合Transformer（DBFT），一个0.22M参数的注意力模型，从弯沉盆和层厚中一步预测两种应变，这些数据按偏移量索引作为序列读取。一个目标函数涵盖14,174个层状弹性解和来自96个泰国公路路段的7,651个现场测量，其中三条路线完全保留。在精确理论上，DBFT达到R^2 >= 0.9998，RMSE <= 1.24微应变，比已发表的指数方程好一个数量级。仅在该因子组合上训练时，它在实测弯沉盆上失败，R^2 <= 0.14：现场域反转了弯沉盆外部的相关性符号。组合目标在保留路线上恢复了0.964和0.879，同时在理论上保持R^2 >= 0.993。注意力和SHAP恢复了公认的力学机制，包括对常规指数从未达到的1200-1800毫米范围的依赖。一致性是与反算一致的力学，而非与仪表测量的应变。两种应变在单个CPU线程上1.6毫秒内返回。
英文摘要
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.
