00757483

Component

https://doi.org/10.3141/1639-04

http://scholar.google.com...than&publication_year=1998

Most of the deflection analysis programs used today to analyze falling weight deflectometer (FWD) data are based on static analysis, which often underestimates the subgrade strength. Unfortunately, dynamic analysis usually involves complex calculations and requires significant computation time, thus making it impractical for routine applications. A methodology based on deflection basin parameters and artificial neural networks (ANNs) for processing dynamic FWD measurements to estimate layer strengths is presented in this paper. Two-dimensional, dynamic finite element analysis using the ABAQUS program was employed to develop the deflection information for this study. Unlike the majority of the existing backcalculation programs that iteratively adjust the layer moduli to match the measured deflections, the proposed method first determines the subgrade modulus by means of two deflection basin parameters - Base Damage Index and Shape Factor F2 - and then applies the estimated subgrade modulus and other parameters as input variables to a trained ANN to estimate the upper layers' moduli. In contrast to other programs that require the input of seed values for layer moduli, this method does not require initial estimates as input. A set of field FWD measurements were analyzed both by this method and by the MODULUS program. Results reveal that the proposed method is able to better predict the asphalt concrete layer modulus while taking into account the dynamic effects of the FWD test. This method is also shown to be computationally efficient, which makes it applicable for routine tasks and field use.

This paper appears in Transportation Research Record No. 1639, Recent Pavement Research Issues.

English

p. 36-42

1998

Transportation Research Record

0309065119

Figures (7) ; References (16) ; Tables (2)

Accuracy; Coefficient of subgrade reaction; Deflection; Economic efficiency; Falling weight deflectometers; Finite element method; Flexible pavements; Forecasting; Modulus of elasticity; Neural networks; Pavements

Efficiency

Design; Economics; Highways; Pavements; I22: Design of Pavements, Railways and Guideways; I23: Properties of Road Surfaces

TRIS, TRB, ATRI

Dec 7 1998 12:00AM

• ANALYSIS OF CONCRETE PAVEMENT RESPONSES TO TEMPERATURE AND WHEEL LOADS MEASURED FROM INSTRUMENTED SLABS
• CONTINUOUS PROFILING OF RUNWAY AND TAXIWAY PAVEMENTS WITH ROLLING DYNAMIC DEFLECTOMETER AT DALLAS-FORT WORTH INTERNATIONAL AIRPORT
• CORRECTION OF FIELD SKID MEASUREMENTS FOR SEASONAL VARIATIONS IN TEXAS
• CRASH EXPERIENCE ON TINED AND CONTINUOUSLY GROUND PORTLAND CEMENT CONCRETE PAVEMENTS
• EFFECT OF ALLOWABLE THICKNESS VARIATION ON BACKCALCULATED MODULI (WITH DISCUSSION AND CLOSURE)
• EVALUATION OF SUPERLIGHT PAVEMENTS UNDER ACCELERATED TRAFFIC
• EXPERIMENTAL VERIFICATION OF RESILIENT DEFORMATION FOR GRANULAR SUBGRADES
• FATIGUE BEHAVIOR OF RUBBER-MODIFIED PAVEMENTS
• FIELD STUDY OF IN SITU SUBGRADE SOIL RESPONSE UNDER FLEXIBLE PAVEMENTS
• FRICTION AND SURFACE TEXTURE CHARACTERIZATION OF 14 PAVEMENT TEST SECTIONS IN GREENVILLE, NORTH CAROLINA
• IN SITU EVALUATION OF LAYER STIFFNESSES OF AIRPORT PAVEMENTS BY CROSSHOLE SEISMIC TESTS
• MECHANISTIC-EMPIRICAL APPROACH TO ASSESSING RELATIVE PAVEMENT DAMAGE
• PAVEMENT DISTRESS UNDER ACCELERATED TRAFFICKING
• PREDICTION OF LAYER MODULI FROM FALLING WEIGHT DEFLECTOMETER AND SURFACE WAVE MEASUREMENTS USING ARTIFICIAL NEURAL NETWORK
• RELATING LABORATORY AND FIELD MODULI OF TEXAS BASE MATERIALS
• TIME-DEPENDENT PERMANENT DEFORMATION MODELS FOR ASPHALTIC MIXTURES