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NONDESTRUCTIVE RESEARCH
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Nondestructive
Pavement Evaluation Using ILLI-PAVE Based Artificial Neural Network
Models
Evaluating structural condition of existing, in-service pavements
is a part of the routine maintenance and rehabilitation activities
undertaken at the Illinois Department of Transportation (IDOT).
In the field, the pavement deflection profiles (or basins) gathered
from the nondestructive Falling Weight Deflectometer (FWD) test
data are typically used to evaluate pavement structural conditions.
This kind of evaluation requires the use of backcalculation type
structural analysis to determine pavement layer stiffnesses and
as a result estimate pavement remaining life. According to IDOT’s
mechanistic based pavement analysis and design procedures, recent
use of artificial neural network (ANN) models trained with ILLI-PAVE
finite element solutions has proved to give much better results
than the statistical algorithms currently in use. This applied
research advances IDOT’s engineering practices of in the
area of backcalculation of flexible pavement layer properties
from FWD field data. |
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Investigation of Aggregate Shape Effects on Hot Mix Performance
Using An Image Analysis Approach 
This research utilizes advanced imaging technology in the selection
of proper shaped and textured aggregates to build more durable
and longer lasting asphalt concrete pavements. The University
of Illinois Aggregate Image Analyzer is used to automate determination
of coarse aggregate size and shape properties, such as the gradation,
angularity, flatness and elongation, surface texture, and the
surface area. The impact of these imaging based shape and size
indices on the performances of asphalt concrete mixes is being
investigated for field and laboratory rutting performances.
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Characterization of Fractured Concrete Surface Roughness
In order to improve shear resistance of aggregate interlock joints
in concrete pavements, the surface roughness at the joint face
must be better characterized in terms of the concrete constituents.
The monotonic and cyclic shear behavior of concrete joints were
quantified through the use of the joint's surface roughness. The
surface roughness of the concrete joint/crack was characterized
by a 2-D laser profilometer, which represented the 3-D contours
of the joint surface. A scale invariant parameter, called the
Power Spectral Area Parameter (PSAP), was developed to relate
the large-scale concrete surface roughness to the joint performance.
The concrete’s fracture energy based on the wedge split
test was also found to represent both the concrete’s fractured
surface characteristics and shear load transfer properties for
several coarse aggregate types (limestone, gravel, and trap rock)
and sizes (19 and 38 mm).
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Ground Penetrating Radar Signal Analysis and Modeling
One of the major problems in using Ground Penetrating Radar (GPR)
for estimating pavement layer thickness is the uncertainty associated
with the dielectric properties of the materials. A method to determine
the dielectric constant, and therefore the thickness, of the hot-mix
asphalt (HMA) and other pavement layers of an existing pavement
using GPR is developed. The developed analytical method uses a
modified common midpoint technique to estimate the dielectric
constant, based on the reflections from a common point at the
bottom of the layer. Algorithms were also developed to measure
railroad ballast thickness and rebar cover depth in bridge decks,
as well as detecting internal flaws.
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