United States Department of Transportation
i
Validation of Source Approval of HMA Surface Mix Aggregate
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2016-04-01
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[PDF-3.53 MB]
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Edition:Final report
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Abstract:The main focus of this research project was to develop methodologies for the validation of source approval of hot
mix asphalt surface mix aggregate. In order to further enhance the validation process, a secondary focus was also to
create a spectral library. Two methodologies were developed as part of this project: Chemometrics, using GRAM/IQ
software and Neural Network (NN). Spectra of aggregate samples from 19 quarries were extracted, using the
portable ASD FieldSpec 4 spectroradiometer (FS4). Because the aggregate samples when placed in a petri dish form
heterogeneous surfaces, it was necessary to utilize specialized equipment (ASD FS4 Turntable) which converts the
surface of the aggregate samples in the petri dish to a homogeneous surface and enhances the spectra extraction
process. Ten spectra were extracted from each aggregate sample and then averaged to produce one spectrum. In all,
three spectra were extracted per aggregate sample, which ensures representativeness of the aggregate samples. All
the spectra were collected in reflectance unit and the data was exported into ASCII for further analyses. Results for
both methodologies were encouraging. The Chemometrics was also able to discriminate limestone based on the
quarry locations. It also correctly classified 100% of aggregate samples that were not from the aggregate sample
population and explained the frictional and physical variability of aggregates within a given quarry over a period of
time, making it useful as a diagnostic tool to validate aggregate source. The NN, which relies heavily on the
variance of the training set, was able to provide outstanding parameter estimations. Thus the NN is a viable solution
for providing quick parameter estimations, based solely on the optical spectrographic measurement of the aggregate
sample.
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