Pavement defect assessment and maintenance effectiveness evaluation using a falling weight accelerometer intelligent inspection vehicle

Refined pavement maintenance requires localized defect identification and quantitative treatment-effect evaluation. However, conventional mechanical response-based methods are often constrained by field operability and testing efficiency for localized assessment. This study developed an FWA-based assessment approach using a Falling Weight Accelerometer (FWA) intelligent inspection vehicle for pavement defect assessment and maintenance effectiveness evaluation. The approach included impact-configuration selection, task-oriented indicator development, and field application of the indicators. The 100 kg hammer with a 0.5 m drop height provided the greatest stress transmission among the tested configurations and was therefore selected for field assessment. Intact free-corner acceleration was approximately 25%–30% lower than slab-center acceleration, and two localized support-loss zones were identified using comparable intact corners as references. FWA acceleration decreased with increasing asphalt crack severity, with the mean deterioration rate increasing from 11.0% in the single-crack area to 27.5% in the network-crack area. In the concrete pavement grouting case, the improvement ratio and restoration proportion reached 21.93% and 88.0%, respectively. For the two asphalt cracks, the corresponding improvement ratios were 12.5% and 5.8%, and the restoration proportions were 81.3% and 20.0%. After concrete pavement microcracking, mean acceleration decreased by 17.05%–19.39%, while the coefficient of variation decreased by 24.07%–38.45%. FWA and Falling Weight Deflectometer (FWD) results showed consistent overall trends but different pointwise sensitivities. Overall, the FWA intelligent inspection vehicle enables automated, high-density assessment and cloud-based data processing for rapid weak-zone screening and treatment-response evaluation.