01620174

Component

https://doi.org/10.3141/2639-04

A statistical classifier was developed to interpret falling weight deflectometer data for the detection of voids under jointed concrete pavement slabs. The classifier was trained with the Seasonal Monitoring Program sections in the Long-Term Pavement Performance (LTPP) database and data from the Minnesota Road Research Facility. A two-level cross-validation process was used to assess the performance of existing void detection methods, according to a threshold of a single variable, and the least absolute shrinkage and selection operator (LASSO) classifier, which is based on several variables. Simple void detection methods based on the normalized 9,000-lb deflection were found to perform better than void detection methods based on variable deflection analysis. The LASSO classifier outperformed any of the existing void detection techniques. The LASSO classifier was validated through two field trials in Pennsylvania and an LTPP general pavement section in which significant faulting had developed.

01632579

17-05073

English

pp 28–38

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309441636

Digital/other

Figures (9) ; References (16)

Air voids; Concrete pavements; Curling; Detection and identification; Falling weight deflectometers; Field tests; Statistical analysis; Validation; Warpage

Long Term Pavement Performance (LTPP) Program

Pennsylvania

Highways; Maintenance and Preservation; Pavements

PRP, TRIS, TRB, ATRI

Dec 8 2016 11:58AM

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