1. Highways for LIFE – Technology Partnerships Program
Intelligent Asphalt Compaction Analyzer
ASSISTANCE AGREEMENT DTFH61-08-G-0002
Project Team
Haskell Lemon Construction Company
University of Oklahoma
Volvo Road Machinery
EST Inc.
Highways for LIFE – Technology Partnerships Program

2. Destructive Quality Control Time Consuming
Coring
Spot Testing
Destructive
Time
Consuming
Conventional Quality Control Techniques do not accurately reflect the overall quality of the Asphalt Pavement.

3. Need For Intelligent Compaction
Ruts, potholes, cracks and other forms of defects reduce the useful life of a pavement.
Improper / inadequate compaction is the most common cause for early degradation of asphalt pavements.
Anticipated Advantages
Instantaneous and complete evaluation of the pavement being compacted
Higher efficiency and increased productivity through the
- reduction in labor and fuel costs,
- reduction in the number of conventional spot tests.
Higher adaptability of the compaction process to suit thin/thick lifts, soft/stiff
subgrades etc.
Better quality resulting from uniform and optimum compaction.
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4. University of Oklahoma Intelligent Asphalt Compaction Analyzer (IACA)
The IACA display (above right) provides real-time information on the density and the mat temperature to the roller operator at each location on the pavement during each roller pass.
The Intelligent Asphalt Compaction Analyzer is a roller-mounted device that continuously senses the vibrations of the compactor and estimates the level of compaction (density) achieved during the construction of an asphalt pavement.
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5. Principle of Operation of the IACA
Roller Vibrations
Collect vibration data
Extract Salient Features from Vibrations
Use training data to classify the features
Mat Properties
Maps the Neural Network Output to Density
Measure of Compaction
(density/stiffness)
Sensor Module
Feature Extractor
Neural Network
Compaction
Analyzer
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6. IACA ‘As-Built’ Density Map
Density information from the final pass of the roller can be used to compile the final density of the entire pavement. The color coded map ( 90%, 92%, 94%) portrays the uniformity of compaction that was achieved.
Areas in green (in the figure above) indicate inadequate
compaction or uncompacted areas resulting from relief features
on the pavement surface (concrete manhole covers,
in this case).
Uneven compaction can be investigated to determine
the cause. In this case, soft subgrade had resulted in uneven compaction on the North Lane.
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7. Comparison of IACA and PQI 301 density Measurements
Estimated Density (%)
PQI 301
IACA 91 94
Distance (feet)
The density estimates of the IACA are comparable to those obtained by point-wise measurements using a PQI 301 gauge.
The density estimates obtained using the IACA comparable very favorably with density measured from roadway cores.
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