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Intelligent Asphalt Compaction Analyzer ASSISTANCE AGREEMENT DTFH61-08-G-0002

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. Destructive. Coring. Quality Control. Spot Testing.

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Intelligent Asphalt Compaction Analyzer ASSISTANCE AGREEMENT DTFH61-08-G-0002

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  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.

  2. Destructive Coring Quality Control Spot Testing 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. 2/6

  4. 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. 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. University of OklahomaIntelligent Asphalt Compaction Analyzer (IACA) 3/6

  5. Principle of Operation of the IACA Roller Vibrations Extract Salient Features from Vibrations Collect vibration data Use training data to classify the features Maps the Neural Network Output to Density Mat Properties Measure of Compaction (density/stiffness) Sensor Module Feature Extractor Neural Network Compaction Analyzer 4/6

  6. N 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. 5/6

  7. PQI 301 IACA 94 Estimated Density (%) 91 Comparison of IACA and PQI 301 density Measurements 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. 6/6

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