A study was undertaken to backcalculate the asphalt concrete (AC) and subgrade moduli from Falling Weight Deflectometer (FWD) data acquired at National Airport Pavement Test Facility (NAPTF) during the full-scale traffic testing using an Artifi cial Neural Network (ANN) based approach. Validated ANN models trained over a comprehensive database of 28,500 datasets were employed for pavement layer moduli predictions. The ILLI-PAVE finite element synthetic solutions database, considering the non-linear, stress-dependent unbound granular layer and subgrade soil properties, were used for training the ANN. The ANN models successfully predicted the pavement layer moduli from the ILLI-PAVE finite element solutions. The best-performing ANN models were used to evaluate the FWD data acquired at the NAPTF during full-scale traffi c testing with a dual-tandem Boeing 747 (B747) gear and dual-tridem Boeing 777 (B777) gear. The variations in pavement layer moduli properties during the course of traffic testing were studied using the ANN predictions and the results were compared with those obtained using ILLI-PAVE based regression algorithms. The results indicate the potential of using 40-kN (9,000-lb) FWD test data for routine airport pavement structural evaluation as long as they generate sufficient deflections for reliable data acquisition. The rapid prediction ability of the ANN backcalculation models makes them promising evaluation tools for analyzing FWD defl ection data in real-time for both project-specific and network-level FWD testing of airport pavement systems.
Structural characterization of flexible airfield test pavement under repeated traffic loading
Strukturelle Charakterisierung flexibler Luftfahrt-Testbeläge unter wiederholter Verkehrsbelastung
2006
8 Seiten, 3 Bilder, 21 Quellen
Conference paper
Storage medium
English
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