Evaluating subsurface cavities detection using innovative laser dynamic deflectometer for efficient and large-scale urban road network inspections

Abstract

Pavement collapses caused by subsurface cavities present a considerable challenge in urban areas. Conventional inspection techniques like ground-penetrating radar (GPR) are effective but expensive and time-consuming. Alternatively, laser dynamic deflectometer (LDD) has emerged as a cost-efficient tool capable of real-time measurements; however, its reliability for detecting subsurface defects in urban roadways remains unclear. This research investigates the feasibility and efficacy of LDD for the rapid identification of subsurface cavities within urban road networks. A case study was conducted in Longgang District, involving comprehensive inspections using both LDD and GPR, with cavities delineated based on GPR data. Chi-square tests revealed a positive correlation between deflection anomalies detected by LDD and the subsurface cavities identified by GPR, with a stronger correlation on main roads. Deflection values significantly increased as the distance between the top of the cavity and road surface decreased, suggesting that LDD measurements can effectively predict the condition of underlying cavities. A moderate-to-strong correlation was found between LDD deflection anomalies and GPR reflection intensity, validating the integration of these technologies for reliable cavity inspections. This research demonstrates that the LDD is a feasible and effective method for large-scale urban road inspections, offering the potential for timely and early warning of ground collapse.