The National Academies

NCHRP IDEA 20-30/IDEA 150 [Completed (IDEA)]

Automated Laser Spectrographic Pattern Matching for Aggregate Identification
[ NCHRP 20-30 (NCHRP-IDEA) ]

  Project Data
Staff Responsibility: Dr. Inam Jawed
Fiscal Year: 2009

This project evaluated the feasibility of using Laser Induced Breakdown Spectroscopy (LIBS) as a technology that can be used as a quality control tool to scan in real-time aggregates used in highway construction applications. The approach involves targeting aggregates with a high-powered laser and using multivariate statistical modeling techniques to determine whether aggregates of interest exhibit definable spectral patterns that could be correlated with selected engineering properties of the target samples. Aggregates were supplied by three state DOTs (New York, Kansas, and Texas) for evaluation. The NYSDOT effort focused on identifying friction properties of aggregates based on percent insoluble residue in target samples. The research team developed an effective calibration curve model capable of predicting the insoluble residue content of unknown samples. The KSDOT effort focused on identifying D-cracking susceptible aggregates by correlating laser-induced spectral data with the results of three KSDOT tests (freeze-thaw, expansion, and durability tests), and examining whether selected beds within a Kansas quarry could be identified using laser scanning techniques. Results to-date have shown a high correlation between laser scanning and modeling predictions and KSDOT D-cracking test results, and have also resulted in successful modeling of the quarry beds. The TXDOT effort focused on identifying a reactive chert that the DOT had found to contribute to alkali-silica reactivity. A calibration curve model that enabled predicting the quantity of chert in target samples. The initial successes have accelerated the development of a Sampling and Laser Targeting System concept for bulk material sampling and analysis in field operations.  The contractor's final report is available.

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