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
  • Electromagnetic spectrum
  • Hot mix asphalt
  • Neural networks
  • Quarries
  • HMA
  • aggregate sources
  • spectroradiometer
  • chemometrics
  • neural network
• 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.
  More +
• Format:
  PDF
• Funding:
  SHA/MSU/4-2
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31050/dot_31050_DS1.pdf
• File Type:
  [PDF - 3.53 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:95b44a4c3f5cac11a5b6107d900938cfb879b11be4c4eec96c430ea00c68d977