presented by amy l. simpson, ph.d., p.e. amec environment ... · conclusions • hpms summary...
TRANSCRIPT
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Presented By
Amy L. Simpson, Ph.D., P.E.
AMEC Environment & Infrastructure, Inc.
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Project Objectives
• Define consistent and reliable
method of assessing infrastructure
health on the IHS
• Develop tools to provide FHWA and
State DOTs ready access to key
information
• Focus on pavements and bridges
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Goal of Paper
• Process and findings related to
establishing condition through
pavement surface characteristics
• Pilot study performed to test options
for categorizing pavement condition
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Goals of the Pilot Study
• How difficult is it to obtain the data?
• Is there temporal consistency
between data sets?
• How do data sets compare?
• Which data set has potential use for
National condition metric?
• What are the needed improvements?
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Pilot Study Corridor
• 874 miles
• AADT from
5,000 to
90,000
• Urban and
Rural
• Variety of
surface
types
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National Data
• HPMS in 2010+ format
• Intervals range from 0.002 km to over 15 km
• Rutting
• Roughness
• Faulting
• Percent and length of cracking
• 2009 data from MN and WI, 2010 data from SD
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State Data
• Documentation
• Inventory - Roadway geometry
- Maintenance / Rehabilitation
- Traffic
• Pavement management data - Structure
- Performance
- FWD
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Field Data Collection
• Summer of 2011
• Collected in eastbound direction
• Summarized to 0.2-km intervals
- Roughness
- Rutting
- Faulting
- Percent and length of cracking
• Estimated PCI for 240 km
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Data Collection Summary
National State Field
HPMS PMS Condition RWD
MN 2009 2010
2011
(No RWD for WI) SD 2010 2010
WI 2009 2010
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IRI
AC
PCC
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Correlation of IRI
HPMS State
Outliers No Outliers Outliers No Outliers
Field AC 34% 79% 77% 75%
HPMS AC 29% 84%
Field PCC 22% 63% 36% 84%
HPMS PCC 54% 60%
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Cracking
• Percent cracking
- AC, percentage of area of wheelpaths
- JCP, percentage of cracked slabs
- CRC, percentage of punchouts
• Length of cracking
- Length of transverse or reflective
cracking on AC pavements, only
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Percent Cracking on AC
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Percent Cracking on PCC
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Cracking Length
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Correlation
Data Set Correlation between Field and HPMS
Asphalt surface, % cracking 6%
Asphalt surface, crack length 27%
PCC surface, % cracking 64%
• Temporal differences in data
• Different vendors
• HPMS are sampled, field are continuous
• Studies show high variability in crack detection
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Rutting Data
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Rutting Correlations
HPMS Rut State Rut
Outliers No Outliers Outliers No Outliers
Field Rut 58% 86% 87% 87%
HPMS Rut 57% 92%
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Faulting Data
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Conclusions
• HPMS summary lengths set to fixed length
• Processing time results in data that are 1.5
years old used to evaluate condition
• Cracking / faulting not feasible for use
• States would need to collect similar data
• Need good information about maintenance
and rehabilitation activities
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Conclusions, cont.
• IRI is currently most feasible for use
• Rut algorithm should be codified to promote consistency
• Cracking data collection needs further definition and QA/QC procedures
• Rutting could be used as a flag
• Significant work required for cracking and faulting