Validation of Source Approval of HMA Surface Mix Aggregate

Details

• Creators:
  Wilson, Frederick K. ; Owolabi, Oludare ; Willoughby, Arthur ; Whitney, III, James
• Corporate Creators:
  Morgan State University
• Corporate Contributors:
  Maryland. State Highway Administration. Office of Policy & Research
• Subject/TRT Terms:
  Aggregates Aggregate Sources Chemometrics Electromagnetic Spectrum HMA Hot Mix Asphalt Neural Network Neural Networks Quarries Spectroradiometer
• Publication/ Report Number:
  MD-16-SHA/MSU/4-2
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final report
• Corporate Publisher:
  Maryland. State Highway Administration. Research Division
• 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.
  
  
• Format:
  PDF
• Funding:
  SHA/MSU/4-2
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:95b44a4c3f5cac11a5b6107d900938cfb879b11be4c4eec96c430ea00c68d977
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31050/dot_31050_DS1.pdf
• File Type:
  [PDF - 3.53 MB ]