Machine Learning Inversion of Runway Base-Course Parameters Using Multi-Domain Vibration Features Under Moving Aircraft Loads

Abstract

Accurate identification of runway base-course parameters is an important basis for structural condition assessment, yet separating base-course elastic modulus, thickness, and local damage from runway responses under moving aircraft loads remains largely unexplored. This study proposes a machine learning inversion method based on multi-domain vibration features. Dynamic responses are generated with a 2.5-dimensional finite-element–boundary-element model, from which peak strain, dominant frequency, low-frequency energy ratio, strain attenuation gradient, and time–frequency entropy are extracted; random forest (RF) and support vector machine (SVM) models then invert three classes of base-course parameters. The extracted features exhibited distinct sensitivities to base-course modulus, thickness, and local damage. RF and SVM showed different advantages depending on the evaluation metric, while SVM performed better in the unseen interpolation cases. The modulus-identification performance of both models remained comparatively stable after noise was added. The results indicate that multi-domain vibration features provide complementary information for the numerical inversion of runway base-course parameters, thereby establishing a methodological basis for their rapid assessment.