S44 Pavement Management LTC2013

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    By:

    E-mail:

    Quality Assurance/Quality Control

    and

    Quality Acceptance

    Pavement Management Systems

    John Ashley Horne

    [email protected]

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    Various Data Collected for PMS

    Automatic Road Analyzer (ARAN)

    Data Collected by the ARAN

    Data Collection Vendors Quality Assurance/Quality Control

    Pavement Management Systems Quality Acceptance

    Pavement Management Systems QA/QC

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    Overhead Clearances(Collected in 2007)

    Clearance Clearance

    Data Collected for PMScontd

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    Ramps(Collected 2000 and 2007)

    Example: Ramp off of I-0010, District 61

    Data Collected for PMScontd

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    Geometric Information

    Data Collected for PMScontd

    Roadway Geometric Data

    Cross slope Road shoulder drop-off

    Horizontal Curves

    Vertical Curves

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    (1995 Statewide)

    (2009 District 05,remaining Districts over next 4 years)

    Data Collected for PMScontd

    Ground Penetrating Radar

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    Friction Testing

    Data Collected for PMScontd

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    Rolling Wheel Deflectometer (RWD)(collected 2009)

    Data Collected for PMScontd

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    Data Collected for PMScontd

    Falling Weight Deflectometer (FWD)

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    Automatic Road Analyzer

    (ARAN)

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    Data Collected by ARAN

    Right of Way Images Center view

    Right view

    Pavement Images Electronic Data

    Rutting

    Faulting International Roughness Index (IRI)

    Global Positioning System (GPS) Coordinates

    Macrotexture

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    Data Collected by ARANcontd

    Right of way images

    Captured using two high definition cameras (1920 x

    1080 pixel resolution)

    The images are recorded every 0.004 miles

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    Data Collected by ARANcontd

    Pavement Images Captured using two cameras

    100% of the driven lane is captured

    Images stored for post processing

    Strobe lights minimize shadows

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    Rutting data is collected by the use

    of two scanning laser transverse

    profilers

    Roughness and faulting data iscollected by two lasers, one in each

    wheel path

    GPS collected with corrections from

    LSUs Center for GeoInformatics Texture collection utilizes high

    frequency lasers to measure the

    mean profile depth of road surface

    macrotexture

    Data Collected by ARANcontd

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    QA/QC - Calibration Site Test

    Tested three to five times

    Electronic sensor data, fullRight-Of-Way (ROW) and

    pavement images

    The collected and evaluated data are compared

    with the original approved benchmark

    measurements

    Data Collection Vendors QA/QC

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    QA/QC - Calibration Site Test

    Data Collection Vendors QA/QC contd

    Tolerance for Acceptance

    Roughness should not deviate more than 10%

    Rutting and Faulting should not deviate more than 3

    mm (0.1 inches)

    Pavement distresses are critiqued on a project level

    basis

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    Weekly verification site

    used ensure the sensorsemployed by the ARAN

    stay within tolerance

    Monitor systems in real time during collection Compare current data to previous years data

    Regular verification of DMI calibration

    Inter-rater consistency is maintained

    Data Collection Vendors QA/QC

    QA/QC Maintaining Calibration

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    WiseCrax

    Longitudinal cracking

    Transverse cracking

    Fatigue cracking

    DRate

    Patching

    Distresses on concrete pavements

    QA/QC Pavement Distress Ratings

    Data Collection Vendors QA/QC contd

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    QA/QC Pavement Distress Ratings

    Data Collection Vendors QA/QC contd

    WiseCrax

    Used on asphalt surfaces

    Distresses rated by computer Longitudinal cracking

    Transverse cracking

    Fatigue cracking

    Results are visually inspected and

    compared to previous years results

    Valid lane widths

    Duplicate records

    Correct rating scheme

    Distresses not detected

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    QA/QC Pavement Distress Ratings

    Data Collection Vendors QA/QC contd

    WiseCrax

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    QA/QC Pavement Distress Ratings

    Data Collection Vendors QA/QC contd

    DRate

    Used mainly on concrete surfaces Distresses rated manually

    Longitudinal cracking

    Transverse cracking

    Patching (all pavement surfaces)

    Sections randomly sampled

    Results are compared to previous years collection

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    QA/QC Pavement Distress Ratings

    Data Collection Vendors QA/QC contd

    DRate

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    QA/QC Pavement Distress Ratings

    Data Collection Vendors QA/QC contd

    Visualization in Visidata

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    QA/QC Right of Way and Pavement Images

    Data Collection Vendors QA/QC contd

    Image quality

    Brightness Clarity

    Missing images

    Monitored in real time

    ROW and pavement images should be synchronized Able to see cracking in ROW/pavement views

    Control section verification (right of way images)

    Proper image stitching (pavement images)

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    LADOTD Quality Acceptance

    Semi-automated search for missing images

    Verify collection of control sections Check beginning/ending

    Images play in correct order

    Sample images throughout control section Control section collected in correct direction

    Collected both directions

    Pavement surface should be dry

    Quality Acceptance Right of Way Images

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    LADOTD Quality Acceptancecontd

    Quality Acceptance Pavement Images

    Images quality Clarity

    Brightness

    Missing images

    Pavement should synchronize with ROW

    All distresses should be visible

    Look for missed distresses

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    LADOTD Quality Acceptancecontd

    Quality Acceptance Pavement Distress Data

    Distresses correctly identified/quantified

    Type Severity

    Extent

    Protocols were followed

    Thoroughly investigate issues

    Check database prior to import into Pavement

    Managements software

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    LADOTD Quality Acceptancecontd

    Quality Acceptance Findings Reported to Vendor

    Deficiencies are summarized and

    reported to data collection vendor

    for corrective action

    Issues are resolved at no additional

    cost

    Some issues may require recollection Missing images

    Erroneous electronic data

    All corrections are resubmitted for review

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    Vision

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    iVision

    Pavement Management Systems

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    By:

    E-mail:

    Quality Assurance/Quality Control

    and

    Quality Acceptance

    Pavement Management Systems

    John Ashley Horne

    [email protected]

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    PM Web Page and DTIMS

    Dash Board

    Chris Fillastre : Pavement Management EngineerE-mail : [email protected]

    Phone Number : 3-4577 or (225) 242-4577

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    Deighton Water ModelThe level of water in each can represents the %

    of roads in that condition

    The taps represent the process of roads

    deteriorating

    The pumps represent the act of fixing a road

    Electricity represents the cost of fixing a road;

    the more height to pump the water the more

    electricity

    Problem:

    For a fixed amount of electricity (budget) how

    should you distribute the electricity to the

    pumps so that you maximize the level of water

    in the top cans?

    Pump

    Pump

    Pump

    Excellent

    Good

    Fair

    Poor

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    My Water ModelThe level of water in each can represents the % of roads

    at that Treatment Level

    Th