Presented at NJDOT Quarterly Meeting, January 9, 2015.

LASER SCANNING AGGREGATES FOR REAL-TIME PROPERTY IDENTIFICATION

Andrew Branin, Bless Ann Varghese, Dr. Michael Lim, Dr. Ravi Ramachandran and

Dr. Beena Sukumaran

Presented at NJDOT Quarterly Meeting, January 9, 2015

Background

Use of unacceptable aggregates such as mica schist and carbonate rocks can reduce the quality and

life of roadway pavement.

Currently used analysis techniques such as XRF analysis for

determination of mineralogy do not provide real-time data.

Objectives of the Study Obtain laser spectra models for various

aggregate sources from New Jersey Calibrate laser-spectra models to identify real-

time aggregate properties such as mineralogy Determine the feasibility of laser technology as

a portable tool for identification of real time aggregate properties such as mineralogy and particle morphology

Determine the feasibility and affordability of adapting laboratory based laser technology applications for field use

Approach

LIBS

Experimental Procedure

Spectrum Preprocessing

PLS Analysis

Determine Chemical

Composition

Approach

LIBS


PLS Analysis


Determine Chemical

Composition

Overview of LIBS(Laser-Induced Breakdown Spectroscopy)

http://www.arl.army.mil/www/default.cfm?page=247http://industrial-lasers.net/yag.html LIBS Handbook

Previous Geological Applications

NY State DOT used LIBS to:1. Determine Acid

Insoluble Residue (AIR) in an aggregate sample.

2. Determine the percent noncarbonated stone in an aggregate blend.

PLS Analysis technique employed for data analysis

(Chesner 2012)

Previous Geological Applications KSDOT

LIBS was used to:1. Predict D-cracking

likelihood: pass/fail.2. Determine the

aggregate source. PCA and PLS

analysis technique used

(Chesner 2012)

Previous Geological Applications TXDOT

LIBS was used to:1. Determine the

amount of chert.2. Predict result of

state testing.3. Differentiate

cherts. PCA and PLS

Analyses(Chesner 2012)

Other Noteworthy Applications

Quality control for: Cement powder composition Concrete repair by modeling chloride and

sulphur contamination at varying depth Recycling demolished concrete

Analysis of micro-cracks in surfaces

Approach

LIBS



PLS Analysis

Determine Chemical

Composition

Current Experimental Setup

1. Nd: YAG Laser (Brilliant B)2. Control Pad (flash lamp, Q-switch)3. Sample Chamber4. Applied Spectra Spectrometer5. Control Unit6. Laptop

1. Nd: YAG Laser (Brilliant B)2. Mirror3. Focusing Lens4. Beam Splitter (not currently used)

2 1

3

5

4

6

1

2

3 4

1. Adjustable Sample Stage2. Spectral Emission Redirection Mirror3. Sample Tray4. Focal Point Indicator

3

2

1

4

Current Experimental Setup

Additional Notes

The beam splitter is no longer used. System timing has been adjusted so that

more laser energy is used with a shorter spectrometer delay.

A fresh battery of tests were performed following these adjustments.

Comments on Preliminary Tests

Relative light intensities were generally relatively consistent as long as sufficient pulse energy was provided.

Measured on cooler plasma – it was assumed that only neutral atoms were present.

Testing had suggested that a 50 mJ laser pulse was optimal (nearly 100 mJ are presently used).

The addition of the beam splitter and use of the higher pulse energy reduced the frequency of low-emission shots, but resulted in a less focused beam.

Current Model Testing

Models were calibrated using new data from 5 locations per sample, for each of 10 samples, for each of 10 stone types.

Each spectrum was the sum of the emissions from 100 shots to mitigate shot to shot variation.

Approach

LIBS



PLS Analysis

Determine Chemical

Composition

Analysis

Data Pre-processing: Subtract baseline (done automatically by software) Remove negative intensity values Normalize to total light emission (where applicable) Various other techniques were used (detailed in Results

section) Base model Y-scaling Averaged calibration set Amplitude scaling Spectral Derivatives Split training

Number of PLS components was optimized for each model via a built-in cross validation function.

Approach

LIBS



PLS Analysis

Determine Chemical

Composition

PLS Regression Analysis Partial Least Squares Regression Analysis (PLS) analysis has been

used to develop models to predict concentrations of compounds within stone samples.

PLS Analysis can be used to generate predictive models based on single values corresponding to an entire spectrum

Predictions are made in a manner similar to Multiple Linear Regression, but coefficients are determined by maximizing covariance between X data and known Y values rather than by minimizing square error.

PLS can also be used to differentiate different types of samples, but only for linear relationships (PCA is generally used for classification)

Chesner 2012

Approach

LIBS


PLS Analysis


Determine Chemical

Composition

Previous Testing

Initial Testing: Metal samples to confirm that LIBS could be used

to qualitatively identify elements in samples Mica and limestone samples to observe output

spectra of two of the target minerals Various coins of known composition to develop a

predictive model for a simplified case Preliminary Testing and Models:

Obtaining output spectra from aggregate samples Calibrating and testing initial predictive models

Preliminary Predictive Models Early models were very inaccurate, and

were calibrated using 27 PLS components, which was later determined to be excessive.

Models may have been weak due to a lack of variation in calibration data, flaws in system timing, sub-optimal data pre-processing, or a combination of factors.

Notes on Accepted Values Testing

DOT XRF results were expected to be, and were found to be more reproducible due to tests being performed on powdered samples.

DOT results continued to be used as accepted values.

Prior to the most recent testing, the experimental setup was adjusted, cleaned, and realigned to produce more reliable spectrum data.

Includes Discussion of Data Pre-Processing Techniques

Current Test Results

Base Model

Negative values were removed, but center clipping was not otherwise applied.

Spectra were normalized to a metric of total light emission prior to calibration and testing.

No other adjustments were made. All other models were compared to this

baseline model.

Y-Scaling, ratio:1

Identical to Base Model, but Y variables (known concentrations) were scaled by dividing the values for each compound by the maximum in the calibration set.

The reverse adjustment is applied to predicted results to produce a prediction.

This forces PLS Regression to consider all compounds with equal priority.

Y-Scaling, 0:1

Identical to the other Y-Scaling method, however the minimum value of each compound is first subtracted from each, before each value is divided by the range for the compound’s accepted values.

The reverse adjustments are again applied to predicted values.

This is simply an alternative method of the previous principle.

Y-Scaling Results

SiO2 Al2O3 Fe2O3 CaO MgO0

10

20

30

40

50

60

70

Carbonate Dolomite

XRFBase Model UpperBase Model Lowerratio:1 Upperratio:1 Lower0:1 Upper0:1 Lower

% C

om

posit

ion


10

20

30

40

50

60

70

80

Woodboro Carbonate


% C

om

posit

ion

Y-Scaling Results

SiO2 Al2O3 Fe2O3 CaO MgO

-20

-10

0

10

20

30

40

50

60

70

Gneiss


% C

om

posit

ion


-20

-10

0

10

20

30

40

50

60

Argillite


% C

om

posit

ion

Averaged Calibration Set

Identical to the Base Model, however the spectra for each type of stone are first averaged together, resulting in a single resultant spectrum for each stone type.

Testing uses individual spectra or averaged testing data.

Averaged Calibration Results


10

20

30

40

50

60

70

Carbonate Dolomite

XRFBase Model UpperBase Model LowerAveraged UpperAveraged LowerAveraged Test

% C

om

posit

ion


10

20

30

40

50

60

70

80

Woodboro Carbonate


% C

om

posit

ion

Averaged Calibration Results

SiO2 Al2O3 Fe2O3 CaO MgO-10

0

10

20

30

40

50

60

70

Gneiss


% C

om

posit

ion


0

10

20

30

40

50

60

70

Argillite


% C

om

posit

ion

Amplitude Scaling

As the Base Model, but spectra are not normalized, and instead each spectrum for a certain type of stone is scaled in amplitude relative to the average light emission for that stone type.

This was done in an attempt to normalize spectra without removing information on varying overall amplitude.

Amplitude Scaling Results


-100

-50

0

50

100

150

Carbonate Dolomite

XRFBase Model UpperBase Model LowerAmp Scale UpperAmp Scale LowerScaled Test UpperScaled Test Lower

% C

om

posit

ion


-100

-50

0

50

100

150

200

Woodboro Carbonate


% C

om

posit

ion

Amplitude Scaling Results


0

10

20

30

40

50

60

70

Gneiss


% C

om

posit

ion


10

20

30

40

50

60

Argillite


% C

om

posit

ion

Spectral Derivatives

An approximation of the derivative of each unadjusted spectrum is used for calibration and testing in place of the spectra themselves.

This was done in an attempt to consider the slope trends in the spectra rather than amplitudes.

Spectral Derivative Results


0

10

20

30

40

50

60

70

80

90

Carbonate Dolomite

XRFBase Model UpperBase Model LowerDerivative UpperDerivative Lower

% C

om

posit

ion


10

20

30

40

50

60

70

80

Woodboro Carbonate


% C

om

posit

ion

Spectral Derivative Results


-40

-20

0

20

40

60

80

Gneiss


% C

om

posit

ion


10

20

30

40

50

60

Argillite


% C

om

posit

ion

**Split Training Sets**

Training Data was divided into carbonate and non-carbonate rocks.

Separate models were generated for each subset to narrow the range of expected values within each model.

In a finished product, a broad-base model would be used as a preliminary estimate before a more specialized model would be used for actual prediction.

Split Training Set Results - Carbonate


10

20

30

40

50

60

70

Carbonate Dolomite

XRFBase Model UpperBase Model LowerSplit Training UpperSplit Training Lower

% C

om

posit

ion


10

20

30

40

50

60

70

80

Woodboro Carbonate


% C

om

posit

ion

Split Training Set Results Non-Carbonate


0

10

20

30

40

50

60

70

Gneiss


% C

om

posit

ion


10

20

30

40

50

60

Argillite


% C

om

posit

ion

Portable LIBS laser and laser for morphology characterization

Equipment Acquisition

Portable Units

A Quantel ULTRA U1064E100R020LN compact laser was purchased following comparison of comparable systems.

This laser will be installed and results compared to previous tests to ensure accuracy with the portable system.

Morphology Characterization Two techniques considered:

Pulsed Digital Holography

Optical Coherence Tomography

Pulsed Digital Holography

Using a pulsed laser as the source of coherent light.

Three-dimensional size and shape information is encoded in the interference pattern produced.

Computer encodes images from interference pattern.

Strength: simplicity and robustness of the hardware implementation.

Limitation: image reconstruction is computationally intensive.

Experimental setup for Pulsed Digital Holography

Optical Coherence Tomography

Based on interferometry principle Interferometer output is analyzed with a

grating spectrometer. Strength:

high-resolution depth information for the scattering surface.

commercial system is available. Team is currently discussing with Thorlabs

regarding the technical capabilities of several OCT systems available for purchase.

Conceptual Depiction of OCT method

Conclusions and Future Work

Previous research has shown that LIBS is feasible as a means to quantify chemical composition of aggregate.

By dividing the training set into carbonate and non-carbonate stones, prediction accuracy and reliability improved considerably.

Future testing will include further refining of predictive models; building on the split training set strategy.

Future testing will also expand the training set and optimize the testing set size, before moving on to testing unknown samples and field tests.

The model can be calibrated to predict other sample traits. Research has begun into measuring particle morphology,

equipment will be selected, and a standard procedure developed.

References

Chesner, Warren and McMillan, Nancy. (2012). “Automated Laser Spectrographic Pattern Matching For Aggregate Identification.” Highway IDEA Program.

Cremers, D. A. and Radziemski, L. J. (2006). “Handbook of Laser-Induced BreakdownSpectroscopy.” John Wiley & Sons, Ltd.

Mansoori, A. et. al. (2011) “Quantitative analysis of cement powder by laser induced breakdown spectroscopy.” Optics and Lasers in Engineering. Vol. 49, Issue 3, 318-323.

Nelson, Stephen. (2013). “Mineral Chemistry.” Tulane University. <http://www.tulane.edu/~sanelson/eens211/mineral_chemistry.htm> Oct. 25, 2013.

Pasquini, Celio et. al. (2007). “Laser Induced Breakdown Spectroscopy.” J. Braz. Chem. Soc., Vol. 18, No. 3, 463-512.

Taffe, A. et. al. (2009) “Development of a portable LIBS-device for quality assurance in concrete repair.” Concrete Repair, Rehabilitation and Retrofitting II. Taylor and Francis Group, London.

Tucker, J.M. et al. (2010) “Optimization of laser-induced breakdown spectroscopy for rapid geochemical analysis.” Chemical Geology. Vol. 277, Issues 1-2. 137-148.

Wessel, W. et. al. (2010) “Use of femtosecond laser-induced breakdown spectroscopy for micro-crack analysis on the surface.” Engineering Fracture Mechanics. Vol. 77, 1874-1883.

Xia, H. and M.C.M. Bakker. “Online Sensor System Based on Laser Induced Breakdown Spectroscopy in Quality Inspection of Demolition Concrete.” Delft University of Technology, the Netherlands.

Acknowledgments

Jr/Sr clinic students Eric Seckinger Saima Mahmud Christine Neppel Joshua Edwards

Questions

Presented at NJDOT Quarterly Meeting, January 9, 2015.

Documents

Transcript of Presented at NJDOT Quarterly Meeting, January 9, 2015.