|
|
Nasrin Sumee
University of New Mexico – Department of
Civil Engineering
CE 547 - GIS in Water Resources
Engineering
Instructor - Dr. Julie Coonrod
|
NECESSITY OF PAVEMENT
DATABASE A
central database including traffic, climate, materials and existing
structures is the key requirement for reliable pavement design. The New
Mexico Department of Transportation (NMDOT) database currently in use is
incomplete, separated and not updated. The figure shows the basic needs of
database for pavement.
|
|
|
||||||||||||||||||||||
|
OBJECTIVE AND SCOPE Design
and develop a database capable of manipulating, storing and processing. Populate
and analyze the database using data from different data sources. |
||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
|
PURPOSE OF GIS ORIENTED
DATABSE GIS
is a computer system capable of storing and using information or data
describing the places of the world. Capable
to support storage and use of geospatial data (i.e. point, line and polygon
data) about road segments and associated features adds important capabilities
to the database. Maintaining
the pavement related data and linking the data to their precise locations. Presenting
numeric visual geographic data layers with traditional data, GIS can answer
many questions all together: location,
where it is; the condition, what it is; the trends, pattern etc. |
||||||||||||||||||||||||
|
|
|
|
||||||||||||||||||||||
|
|
|
PRESENT DATA TYPE
|
||||||||||||||||||||||
|
Projection:
NAD_1983_UTM_Zone_13N Software
Technology: Microsoft Access Software: ArcMap, ArcScene, Microsoft Access |
|
METHODOLOGY Ø Migrating
the Existing Data into the New Geodatabase Ø Establish
Relationships Between Objects Ø Analyze
the Data |
|
|||||||||||||||||||||
|
|
||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
|
·
Accumulated
pavement design data are analyzed for the County of
Bernalillo. The analysis results are presented in Manage the Data. ·
The major two
interstate highways I-25 and I-40 are intersected in Bernalillo County which
is known as Big-I. Therefore, traffic loading on the highways at this county
is relatively high. The annual average daily traffic (AADT), Growth rates are presented in Case Study: Big I. Growth rate is calculated considering the compound growth
from the AADT data. Growth rate for the lane of I-40 west bound to I-25 south
bound and I-40 east bound to I-25 north bound indicate more than 6%, which should be considered in overlay design as critical lanes.
Peak hourly volume (PHV) at Big-I are also analyzed to predict the capacity
at AM and PM rush period. PHV is calculated
considering the 10 % of total AADT. Figure G2 indicates the Peak Hourly
Volume at Big-I. ·
In addition,
traffic entering and leaving from Bernalillo County through I-25 and I-40 are
also analyzed which is presented in Additional Studies. This analysis shows that traffic intensity at Big-I is
approximately 10 and 3 times higher than the leaving and entering traffic at
Bernalillo County for I-40 and I-25, respectively. ·
Structural
database are populated with the layer thickness and
material data. Structure Database indicates the layer profile data, which will be useful in
designing the overlay for rehabilitation program. ·
Data Dictionary: Climate presents the climatic data for Bernalillo County, which will be use
full in selection of asphalt binder and performance analysis. ·
Pavement
performance is evaluated for the subgrade
soil profile. The analysis states that sandy loam soil shows better
performance compared to clayey subgrade soil are presented in Pavement Performance Data and Evaluation Studies. ·
Topographic view
at the Bernalillo County is also analyzed presenting the roadways,
agricultural land, water, range land, forest,
wetland and city area are described in Additional Studies. This analysis will be useful for future expansion with
cut-and-fill. |
||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
|
·
A geo-database for pavement is design and
built which is capable of storing, manipulating and processing the input data
for pavement analysis and design. ·
The database is populated
from three different data sources: LTPP, NCDC and NMDOT. ·
AADT, peak hourly volume, growth factor,
climatic data, structural layer profile and topographic view are analyzed. ·
The critical lanes are also identified on the basis of growth factors. ·
Traffic intensity at Big-I is approximately
10 and 3 times higher than the leaving and entering traffic at Bernalillo
County for I-40 and I-25, respectively. ·
Pavement performance is
also evaluated for the subgrade soil type. ·
Topographic map is also analyzed for future
expansion with cut-and-fill and fixing alignments |
|
Ø Future
works of the study can be identified as follows: Ø Complete
the pavement database for New Mexico. Ø Implement
the database in MEPDG local calibration. Ø Ensure
the accessibility of the user of this database Ø Routinely
update the database for traffic and performance data. |
||||||||||||||||||||||
|
|
||||||||||||||||||||||||
|
REFERENCES 1.
http://rgis.unm.edu/intro.cfm 2.
http://webhelp.esri.com/arcgisdesktop/9.2/pdf/Building_Geodatabases_Tutorial.pdf 3.
National Climatic Data Center (NCDC) (http://www.ncdc.noaa.gov/oa/ncdc.html) 4.
NCHRP (2008), Long Term Pavement Performance (LTPP) Database 5.
NMDOT (2008), New Mexico Department of Transportation Pavement Database 6. Ormbsy, T., Napoleon, E., Burke, R., Grossel, C., and Bowden, L. (2008), Getting to Know ArcGIS desktop, 2nd Edition. |
||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
|
||||||||||||||||||||||||
