A National Database of Subgrade Soil-Water Characteristic Curves and Selected Soil Properties for Use with the MEPDG (2010)

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Research Results Digest 347 August 2010 C O N T E N T S Introduction, 1 Input Parameters for Unbound Materials Required by the EICM, 2 Development of the National Database of Soil Properties from the NRCS Database, 2 Processing NRCS Raw Data, 3 Creating the Master File, 3 Properties and Characteristics of the Master File, 3 Missing Information in the Master File, 3 Preliminary Reduction of Soil Unit Data, 5 Selecting the Proper Component to Represent the Map Unit, 5 Data Sorting by Map Unit, 5 Properties Included in the National Database, 5 Computed Parameters, 15 GIS and Cartographic Process, 18 Software, 18 Data Collection, 18 Data Preparation, 18 User Interface, 19 Using the National Database, 19 Summary, 19 References, 23 INTRODUCTION The Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed in NCHRP Project 1-37A to give pavement de- signers a tool to assess, for a specific design problem, the sensitivity of the critical para- meters that influence pavement performance. Within the MEPDG, the Enhanced Inte- grated Climatic Model (EICM) is the engine that handles the input, collection, character- ization, and analysis of environmental and material properties that determine the stiff- ness or modulus of unbound materials. This stiffness, in turn, significantly influences the pavement distresses predicted by the MEPDG. The EICM requires two main cat- egories of input parameters in order to accu- rately predict the environmental factors: cli- matic information and material properties for unbound (granular base, subbase, and subgrade) materials. The necessary climatic information is readily available to the pave- ment designer in the form of a database within the MEPDG that contains historical weather data from more than 800 stations of the U.S. National Weather Service, with hourly information that includes precipita- tion, temperature, wind speed, cloud cover, and relative humidity. The unbound mate- rial information required by the MEPDG, on the other hand, ranges from routine index properties, which are well known to pave- ment engineering practitioners and research- ers and are used in design Levels 2 and 3, to a specialized set of moisture retention pa- rameters (soil-water characteristic curves, SWCC) that are required for Level 1 designs and are fundamental to predicting the mois- ture content and soil stiffness of the subgrade and unbound pavement layers. SWCC are commonly used by the agricultural sciences and unsaturated soil mechanics communi- ties, but are relatively unfamiliar to the pave- ment community. The objective of NCHRP Project 9-23A was to create a national database of pedo- logic soil families that contains the soil prop- erties for subgrade materials needed as input to the MEPDG. The database focuses upon the parameters describing the SWCC, which are key parameters in the implementation of MEPDG Level 1 environmental analysis, but also includes measured soil index prop- erties needed by the EICM in all three hier- archical levels of pavement design. A NATIONAL DATABASE OF SUBGRADE SOIL-WATER CHARACTERISTIC CURVES AND SELECTED SOIL PROPERTIES FOR USE WITH THE MEPDG This digest summarizes key findings from NCHRP Project 9-23A, “Implement- ing a National Catalog of Subgrade Soil-Water Characteristic Curve (SWCC) Default Inputs for Use with the MEPDG” conducted by Arizona State Univer- sity, Tempe, AZ. The digest was prepared from the project final report au- thored by Claudia E. Zapata, Arizona State University. The final report and Appendices A through D are available on the TRB website (www.trb.org) as NCHRP Web-Only Document 153. The interactive National Database is avail- able on a DVD from NCHRP upon request to eharriga@nas.edu. Responsible Senior Program Officer: E. T. Harrigan NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM

This Research Results Digest (RRD) summarizes the approach taken to create the national database of unbound material properties from soil properties directly measured in the field for agricultural and geotechnical (pavement) engineering purposes down to depths of 100 in. The soil properties are contained in a database available from the U.S. Department of Agriculture’s (USDA) Natural Resources Conserva- tion Service (NRCS, formerly the Soil Conservation Service) that comprises 31,100 soil units distributed in more than 9,800 soil profiles covering the conti- nental United States, Hawaii and Alaska, and Puerto Rico. Data were downloaded from the NRCS data- base and manipulated in both tabular and spatial files. Tabulated data were organized by soil profiles, each of which comprises one or more soil units. The spatial data files were organized in 814 maps covering the entire United States and Puerto Rico that are searchable with a user-interface created in Microsoft Excel; this interface facilitates searching for specific locations within a state, using the maps created in the project. This RRD was prepared from the project final report, which includes four appendices. Appendix A presents a comprehensive user guide to obtain and integrate the contents of the National Database. Appendix B includes 4 volumes presenting the National Atlas of Soil Units. Appendix C includes 10 volumes containing the tabular database. Appen- dix D is a concise summary of the technical fun- damentals related to unsaturated moisture behav- ior and in particular, to the SWCC parameters. The project final report and Appendices A through D are available online as NCHRP Web-Only Document 153 at http://www.trb.org/Main/Blurbs/163721.aspx. The interactive National Database is available on a DVD from NCHRP upon request. INPUT PARAMETERS FOR UNBOUND MATERIALS REQUIRED BY THE EICM The specific types of input parameters required by the EICM in order to accurately predict environ- mental factors are defined by the hierarchical level of MEPDG analysis selected for the pavement design. Generally, as the design level proceeds from Level 3 to Level 1, the number of input parameters and the scope and complexity of testing required to deter- mine them substantially increase. Specifically: • For Level 3, the designer inputs the AASHTO soil classification, grain-size distribution, and Atterberg limits of the unbound material. Though these properties are best measured by the designer, default values, based on AASHTO soil classification, are available within the EICM. • For Level 2, the designer inputs the param- eters required for a Level 3 design plus mass- volume properties including specific gravity of solids, maximum dry density, and optimum moisture content. Default values gathered from the published literature for specific gravity of solids are available within the EICM. In addi- tion, default values based on AASHTO classi- fication are also available for the compaction parameters, optimum moisture content, and maximum dry density. However, these values are derived by correlation with grain size dis- tribution and consistency limits. • For Level 1, the user is expected to input mea- sured results for all the parameters required for Level 2, plus the SWCC parameters. The SWCC parameters are the least standardized and the most complicated set of inputs used in EICM analysis. All three levels of analysis also require the ground- water table depth. This input parameter can be the best estimate of either the annual average depth or the seasonal average depth. For input to Level 1, the groundwater table depth can be determined from profile characterization borings prior to design. For input to Level 3, an estimate of the annual average value or the seasonal averages can be provided. DEVELOPMENT OF THE NATIONAL DATABASE OF SOIL PROPERTIES FROM THE NRCS DATABASE The NRCS has collected, stored, maintained, and distributed soil survey information in the United States since the last decade of the 19th century. It is an excellent source for determining the regional distribution and general characteristics of soils for urban and rural engineering projects. While the NRCS database was obviously intended for agricultural pur- poses, the USDA entered into an agreement with the then Bureau of Public Roads (BPR, predecessor to the Federal Highway Administration [FHWA]) to also measure key soil properties useful for high- way engineering. Thus, the NRCS database con- tains numerous engineering properties of soil deposits needed as input to the MEPDG. 2

The NRCS information is divided among three soil geographic databases, which differ primarily in the scale used for mapping the different soil units. The three soil geographic databases are: 1. The Soil Survey Geographic (SSURGO) data- base, 2. The State Soil Geographic (STATSGO) data- base, and 3. The National Soil Geographic (NATSGO) database. The components of map units in each database are different and correspond to a differing level of detail. The SSURGO database, for example, pro- vides the most detailed level of information, while NATSGO has a somewhat lower level of detail and is primarily used for national and regional resource appraisal, planning, and monitoring. The source of information used in this initial national database is the NATSGO database. Processing NRCS Raw Data The NATSGO database was downloaded from the NRCS website (http://soildatamart.nrcs.usda.gov). The NATSGO database contains spatial and tabular files. The tabular files provide engineering and agri- cultural soil properties in Microsoft Access format, facilitating manipulation of the immense volume of data found in the database. The tables are orga- nized by attribute group according to the techno- logical field; therefore, it is possible to classify and query the database. The spatial files have information necessary to process the graphical expressions of the different soil units. They provide shapefiles, which allow the user to analyze spatial information, edit data, and cre- ate maps in a Geographic Information System (GIS)- based format. The soil data (tabular and spatial) available from NRCS are shown in Figure 1. The database has infor- mation from both private and public sources, as NRCS combines survey information from several organizations including federal agencies. The dark and light green areas in Figure 1 correspond to soil survey areas for which digital data are available. The white areas are land areas where soil surveys either have not been conducted or do not exist in dig- ital form (at the time of this research). Overall, the database provides information from 78,311 map units covering the 50 states and Puerto Rico. The term map unit is defined as an area that rep- resents a group of soil profiles with generally the same or similar characteristics. These map units con- tain information organized according to the schematic diagram shown in Figure 2. Each map unit is identi- fied with a code called a Map Unit Key (Mukey). Each Mukey or map unit consists of several compo- nents, which are soil profiles with slightly different soil properties. The percentage of the area within the map unit that is covered by each component is avail- able. For the purpose of this project, it was assumed that the component with the largest percentage of coverage was representative of the entire map unit. Each profile is typically comprised of 3 to 5 layers, with some profiles containing information from as many as 11 layers. The depth covered by the typical profile averages about 60 in., with some profiles approaching 100 in. CREATING THE MASTER FILE The files downloaded from the NRCS database were processed initially in the Microsoft Access *.mdb format. The master file created with the infor- mation extracted from NRCS contained data for 1,227,117 soils throughout the United States and Puerto Rico. Properties and Characteristics of the Master File Soil properties known to impact the structural engineering behavior of pavement systems were extracted from the NRCS database into a master file; the preliminary selection is shown in Table 1. Some of these properties are not required by the MEPDG software, but they were initially included because they either can be correlated to properties that are required or are needed in the estimation of these properties. Table 2 describes the soil proper- ties that were eventually stored in the master file. Missing Information in the Master File Due to the enormous size of the master file, the information was initially stored in seven Microsoft Excel *.xls files, each capable of storing data for about 60,000 soils. Upon detailed review, 11.6% of the total number of soils mapped in the United States were found to have incomplete information. This percentage is considered remarkably low, given the 3

Figure 1 Soil survey available data.

vast geographic area covered by the project. Table 3 presents the distribution by state of the soil units with missing information. As can be observed, three states have a significant percentage of missing information, viz., North Carolina (99.4%), North Dakota (68.3%), and Montana (57.0%). If these states are removed, the amount of missing information for the remainder of the United States and Puerto Rico is reduced to 5.3%. PRELIMINARY REDUCTION OF SOIL UNIT DATA Each soil type found in the database had informa- tion from several boring logs. In most cases, the infor- mation for each boring log was similar or very similar, allowing for an initial reduction of the master file. This process was carefully performed by choosing the bor- ing log with the most complete information. In some cases, the information collected from two boring logs was complementary and was combined to produce a complete description of the soil. This process resulted in a reduced database that contained 291,216 soils. This reduction also allowed a switch to the use of Microsoft Excel as the primary database tool. SELECTING THE PROPER COMPONENT TO REPRESENT THE MAP UNIT As previously noted, the master file consisted of information for different components within each map unit. It was necessary to further reduce the mas- ter file to reflect only one set of soil properties per Mukey or map unit. For the purposes of this project, it was assumed that the component with the largest percentage of coverage was representative of the entire map unit. After this criterion was applied, the total number of soils was reduced to 31,100. Table 4 shows an example of one map unit with several components. In this case, the map unit with Mukey 677056 is comprised of eight components or soil profiles with coverage ranging from 33% to 2% (see the column labeled comppct–Component per- centage). The typical soil profile selected for this map unit corresponded to the component with greater cov- erage, that is, the one with 33%. DATA SORTING BY MAP UNIT Once each soil had been assigned a unique set of index properties, the final sorting effort consisted of organizing the data by map unit. Each map unit consists of several layers that need to be contained in a unique row. This approach was required in order to link the tabular database with the spatial data, which allows for the creation of the maps. This effort resulted in a final count of 9,827 map units. PROPERTIES INCLUDED IN THE NATIONAL DATABASE Table 5 presents the final selection of soil proper- ties included in the National Database. The database contains the following data necessary for input to the MEPDG at the three design levels: • For Level 3 analysis, – Grain-size distribution including percentage of clay – Plasticity properties (Atterberg limits) – AASHTO soil classification – Groundwater table depth • For Level 2 analysis, – Saturated hydraulic conductivity • For Level 1 analysis, – Data to estimate the Fredlund and Xing SWCC parameters 5 Component A Sterrett Component B Quitman Component B Sunlight Component A Townley Component C Choccolocco Choccolocco Representative Component for Map Unit 657756 Map Unit: 657756 Map Unit: 657755 0 in. 5.9 in. 42.1 in. 59.8 in. Soil Unit m Soil Unit n Soil Unit p Figure 2 Schematic representation of map unit, component, and soil unit.

6Table 1 Initial soil properties selected for the master database Column Label Column Name Map Unit Symbol musym Map Unit Name muname Component Name compname AASHTO Classification aashtocl AASHTO Group Index—Representative Value aashind_r Unified unifiedcl Top Depth—Representative Value hzdept_r Bottom Depth—Representative Value hzdepb_r Thickness—Representative Value hzthk_r #4—Representative Value sieveno4_r #10—Representative Value sieveno10_r #40—Representative Value sieveno40_r #200—Representative Value sieveno200_r Total Clay—Representative Value claytotal_r LL—Representative Value ll_r PI—Representative Value pi_r Db 0.1 bar H2O—Representative Value dbtenthbar_r Db 0.33 bar H2O—Representative Value dbthirdbar_r Db 15 bar H2O—Representative Value dbfifteenbar_r Dp partdensity Ksat—Representative Value ksat_r 0.1 bar H2O—Representative Value wtenthbar_r 0.33 bar H2O—Representative Value wthirdbar_r 15 bar H2O—Representative Value wfifteenbar_r Satiated H2O—Representative Value wsatiated_r LEP—Representative Value lep_r CaCO3—Representative Value caco3_r Gypsum—Representative Value gypsum_r CEC-7—Representative Value cec7_r Water Table Depth—Annual—Minimum wtdepannmin Water Table Depth—April—June—Minimum wtdepaprjunmin Bedrock Depth—Minimum brockdepmin Corrosion Concrete corcon Corrosion Steel corsteel EC—Representative Value ec_r Available Water Storage 0–150 cm aws0150wta SAR—Representative Value sar_r pH H2O—Representative Value ph1to1h2o_r Kw kwfact Kf kffact AWC—Representative Value awc_r Db oven dry—Representative Value dbovendry_r Comp %—Representative Value comppct_r Hydrologic Group hydgrp MAAT—Representative Value airtempa_r Elevation—Representative Value elev_r ENG—Local Roads and Streets englrsdcd Map Unit Key mukey Component Key cokey Chorizon Key chkey Chorizon AASHTO Key chaashtokey Chorizon Unified Key chunifiedkey

Table 2 Soil properties of master database Column Label Description Map Unit Symbol Map Unit Name Component Name AASHTO Classification AASHTO Group Index— Representative Value Unified Top Depth— Representative Value Bottom Depth— Representative Value Thickness— Representative Value #4—Representative Value #10—Representative Value #40—Representative Value #200—Representative Value Total Clay— Representative Value LL—Representative Value PI—Representative Value The symbol used to uniquely identify the soil map unit in the soil survey. Correlated name of the map unit (recommended name or field name for surveys in progress). Name assigned to a component based on its range of properties. A rating based on a system that classifies soils according to those properties that affect roadway construction and maintenance. Soils are classified into seven basic groups plus eight subgroups, for a total of fifteen for mineral soils. Another class for organic soils is used. The groups are based on determinations of particle-size distribution, liquid limit, and plasticity index. The group classification, including group index, is useful in determining the relative quality of the soil material for use in earthwork structures, particularly embankments, subgrades, subbases, and bases. (AASHTO) The empirical group index formula devised for approximately within-group evaluation of the “clayey granular materials” and the “silty-clay materials.” Unified Soil Classification System—A system for classifying mineral and organo-mineral soils for engineering purposes based on particle size characteristics, liquid limit, and plasticity index. The distance from the top of the soil to the upper boundary of the soil horizon. The distance from the top of the soil to the base of the soil horizon. A measurement from the top to bottom of a soil horizon throughout its areal extent. Soil fraction passing a number 4 sieve (4.70-mm-square opening) as a weight percentage of the less than 3 in. (76.4 mm) fraction. Soil fraction passing a number 10 sieve (2.00-mm-square opening) as a weight percentage of the less than 3 in. (76.4 mm) fraction. Soil fraction passing a number 40 sieve (0.42-mm-square opening) as a weight percentage of the less than 3 in. (76.4 mm) fraction. Soil fraction passing a number 200 sieve (0.074-mm-square opening) as a weight percentage of the less than 3 in. (76.4 mm) fraction. Mineral particles less than 0.002 mm in equivalent diameter as a weight percentage of the less than 2.0 mm fraction. The water content of the soil at the change between the liquid and plastic states. The numerical difference between the liquid limit and plastic limit. (continued on next page)

Db 0.1 bar H2O— Representative Value Db 0.33 bar H2O— Representative Value Db 15 bar H2O— Representative Value Dp Ksat—Representative Value 0.1 bar H2O— Representative Value 0.33 bar H2O— Representative Value 15 bar H2O— Representative Value Satiated H2O— Representative Value LEP—Representative Value CaCO3—Representative Value Gypsum—Representative Value CEC-7—Representative Value Water Table Depth— Annual—Minimum Water Table Depth— April—June—Minimum Bedrock Depth—Minimum The oven dried weight of the less than 2 mm soil material per unit volume of soil at a water tension of 1⁄10 bar. The oven dry weight of the less than 2 mm soil material per unit volume of soil at a water tension of 1⁄3 bar. The oven dry weight of the less than 2 mm soil material per unit volume of soil at a water tension of 15 bars. Mass per unit of volume (not including pore space) of the solid soil particle either mineral or organic. Also known as specific gravity. The amount of water that would move vertically through a unit area of saturated soil in unit time under unit hydraulic gradient. The volumetric content of soil water retained at a tension of 1/10 bar (10 kPa), expressed as a percentage of the whole soil. The volumetric content of soil water retained at a tension of 1/3 bar (33 kPa), expressed as a percentage of the whole soil. The volumetric content of soil water retained at a tension of 15 bars (1500 kPa), expressed as a percentage of the whole soil. The estimated volumetric soil water content at or near zero bar tension, expressed as a percentage of the whole soil. The linear expression of the volume difference of natural soil fabric at 1/3 or 1/10 bar water content and oven dryness. The volume change is reported as percent change for the whole soil. The quantity of Carbonate (CO3) in the soil expressed as CaCO3 and as a weight percentage of the less than 2-mm size fraction. The percent by weight of hydrated calcium sulfate in the less than 20 mm fraction of soil. The amount of readily exchangeable cations that can be electrically absorbed to negative charges in the soil, soil constituent, or other material, at pH 7.0, as estimated by the ammonium acetate method. The shallowest depth to a wet soil layer (water table) at any time during the year expressed as centimeters from the soil surface, for components whose composition in the map unit is equal to or exceeds 15%. The shallowest depth to a wet soil layer (water table) during the months of April through June expressed in centimeters from the soil surface for components whose composition in the map unit is equal to or exceeds 15%. The distance from the soil surface to the top of a bedrock layer, expressed as a shallowest depth of components whose composition in the map unit is equal to or exceeds 15%. Table 2 (Continued) Column Label Description

Corrosion Concrete Corrosion Steel EC—Representative Value Available Water Storage 0–150 cm SAR—Representative Value pH H2O—Representative Value Kw Kf AWC—Representative Value Db oven dry— Representative Value Comp %—Representative Value Hydrologic Group MAAT—Representative Value Elevation—Representative Value ENG—Local Roads and Streets Map Unit Key Component Key Chorizon Key Susceptibility of concrete to corrosion when in contact with the soil. Susceptibility of uncoated steel to corrosion when in contact with the soil. The electrical conductivity of an extract from saturated soil paste. Available water storage (AWS). The volume of water that the soil, to a depth of 150 centimeters, can store that is available to plants. It is reported as the weighted average of all components in the map unit, and is expressed as centimeters of water. AWS is calculated from available water capacity (AWC) which is commonly estimated as the difference between the water contents at 1/10 or 1/3 bar (field capacity) and 15 bars (permanent wilting point) tension, and adjusted for salinity and fragments. A measure of the amount of Sodium (Na) relative to Calcium (Ca) and Magnesium (Mg) in the water extract from saturated soil paste. The negative logarithm to the base 10, of the hydrogen ion activity in the soil using the 1:1 soil-water ratio method. A numerical expression of the relative acidity or alkalinity of a soil sample. An erodibility factor which quantifies the susceptibility of soil particles to detachment and movement by water. This factor is adjusted for the effect of rock fragments. An erodibility factor which quantifies the susceptibility of soil particles to detachment by water. The amount of water that an increment of soil depth, inclusive of fragments, can store that is available to plants. AWC is expressed as a volume fraction, and is commonly estimated as the difference between the water contents at 1/10 or 1/3 bar (field capacity) and 15 bars (permanent wilting point) tension, and adjusted for salinity and fragments. The oven dry weight of the less than 2 mm soil material per unit volume of soil exclusive of the desiccation cracks, measured on a coated clod. The percentage of the component of the map unit. A group of soils having similar runoff potential under similar storm and cover conditions. Examples are A and A/D. The arithmetic average of the daily maximum and minimum temperatures for a calendar year taken over the stan- dard “normal” period, 1961 to 1990. The vertical distance from mean sea level to a point on the earth’s surface. The rating of the map unit as a site for local roads and streets, expressed as the dominant rating class for the map unit, based on composition percentage of each map unit component. A non-connotative string of characters used to uniquely identify a record in the map unit table. The unique identifier of a record in the Component table. Use this column to join the Horizon table to the Component table. A non-connotative string of characters used to uniquely identify a record in the Horizon table.

10 Table 3 Soil units with missing information State Name Map Units Map Units without Information 1 Alabama 1394 30 2 Alaska 1818 115 4 Arizona 1229 15 5 Arkansas 1073 38 6 California 4203 182 8 Colorado 2013 121 9 Connecticut 354 10 0 Delaware 43 0 12 Florida 2984 272 13 Georgia 2722 21 0 Hawaii 378 0 16 Idaho 1506 45 17 Illinois 1344 28 18 Indiana 1372 18 19 Iowa 1954 33 20 Kansas 1116 33 21 Kentucky 607 6 22 Louisiana 2259 127 23 Maine 1236 172 24 Maryland 568 79 25 Massachusetts 465 56 26 Michigan 2218 63 27 Minnesota 2400 199 28 Mississippi 1255 20 29 Missouri 904 25 30 Montana 4284 2443 31 Nebraska 2332 419 32 Nevada 2898 24 33 New Hampshire 385 42 34 New Jersey 363 13 35 New Mexico 1985 37 36 New York 3253 314 37 North Carolina 1371 1363 38 North Dakota 2291 1564 39 Ohio 1022 22 40 Oklahoma 1714 74 41 Oregon 1252 76 42 Pennsylvania 1228 25 72 Puerto Rico 217 13 44 Rhode Island 110 4 45 South Carolina 825 101 46 South Dakota 1589 241 47 Tennessee 1402 35 48 Texas 3892 136 49 Utah 1471 61 50 Vermont 273 5 51 Virginia 940 82 53 Washington 2350 83 54 West Virginia 362 8 55 Wisconsin 1220 84 56 Wyoming 1131 49 No State Designation 736 59 78311 9085 % Map Units without Soil Information . . . 11.6

Table 4 Example of component by map unit muname mukey aashtocl layer hzdept hzdepb hzthk comppct sieve10 sieve40 sieve200 ll pi Rogert-Rock outcrop 677056 A-2 1 0 10 10 33 62.5 42.5 30 30 2.5 Rogert-Rock outcrop 677056 A-1 2 10 36 26 33 35 25 12.5 0 Rogert-Rock outcrop 677056 A-2 1 0 8 8 17 62.5 50 30 0 Rogert-Rock outcrop 677056 A-2 2 8 25 17 17 62.5 42.5 35 27.5 7.5 Rogert-Rock outcrop 677056 A-1 3 25 48 23 17 20 14 7.5 0 Rogert-Rock outcrop 677056 A-2-4 1 0 8 8 10 95 70 30 22.5 2.5 Rogert-Rock outcrop 677056 A-6 2 8 30 22 10 80 62.5 45 35 15 Rogert-Rock outcrop 677056 A-6 3 30 51 21 10 65 52.5 35 35 12.5 Rogert-Rock outcrop 677056 A-2-6 4 51 84 33 10 37.5 25 17.5 35 12.5 Rogert-Rock outcrop 677056 A-2 1 0 18 18 6 82.5 55 35 0 Rogert-Rock outcrop 677056 A-2 2 18 25 7 6 62.5 42.5 30 25 10 Rogert-Rock outcrop 677056 A-6 3 25 36 11 6 80 60 45 32.5 12.5 Rogert-Rock outcrop 677056 A-1 1 0 25 25 6 92.5 55 25 0 Rogert-Rock outcrop 677056 A-2 2 25 66 41 6 65 35 27.5 35 12.5 Rogert-Rock outcrop 677056 A-1 1 13 30 17 3 42.5 32.5 25 20 2.5 Rogert-Rock outcrop 677056 A-1 2 30 48 18 3 22.5 17.5 10 20 2.5 Rogert-Rock outcrop 677056 A-1 1 0 25 25 3 62.5 40 20 0 Rogert-Rock outcrop 677056 A-2 2 25 71 46 3 35 27.5 17.5 25 10 Rogert-Rock outcrop 677056 A-4 1 0 8 8 2 95 87.5 65 30 2.5 Rogert-Rock outcrop 677056 A-4 2 8 152 144 2 95 87.5 72.5 30 10

Table 5 Soil Properties Included in the National Database Column Label Parameter Description Units MapChar MapunitSym MapUnitNam_01 Mukey_01 Compname_01 AASHTO_01 GroupIndex_01 TopDepth_01 BottDepth_01 Thickness_01 Pass#4_01 In. In. In. % Key to identify the number of map. The symbol used to uniquely identify the soil map unit in the soil survey. Recommended name or field name for surveys in progress for each map unit. A non-connotative string of characters used to uniquely identify a record in the Map unit table. Name assigned to a component based on its range of properties. Most predominant soil unit within the map unit. A rating based on a system that classifies soils according to those properties that affect roadway construction and maintenance. Soils are classified into seven basic groups plus eight subgroups, for a total of fifteen for mineral soils. The groups are based on determinations of particle-size distribution, liquid limit, and plasticity index. The empirical group index formula devised for approximately within-group evaluation of the “clayey granular materials” and the “silty-clay materials.” The group clas- sification, including group index, is useful in determining the relative quality of the soil material for use in earthwork structures, particularly embankments, sub- grades, subbases, and bases. The distance from the top of the soil to the upper boundary of the soil horizon. The distance from the top of the soil to the base of the soil horizon. A measurement from the top to bottom of a soil horizon throughout its areal extent. Soil fraction passing a number 4 sieve (4.70-mm-square opening) as a weight percentage of the less than 3 in. (76.4 mm) fraction. Map Chart Key Map Unit Symbol Map Unit Name Map Unit Key Component Name AASHTO Classification AASHTO Group Index Top Depth of Layer Bottom Depth of Layer Thickness of the Layer Passing # 4

Pass#10_01 Pass#40_01 Pass#200_01 Clay0.002_01 LL_01 PI_01 Ksat_01 Wsatiated_01 af_01 nf_01 mf_01 hr_01 (continued on next page) Passing # 10 Passing # 40 Passing # 200 Passing Sieve 0.002 mm Liquid Limit Plasticity Index Sat’d Hydraulic Conductivity Satiated H2O A N M Hr % % % % % % ft/hr % Psi Psi Soil fraction passing a number 10 sieve (2.00-mm-square opening) as a weight percentage of the less than 3 in. (76.4 mm) fraction. Soil fraction passing a number 40 sieve (0.42-mm-square opening) as a weight percentage of the less than 3 in. (76.4 mm) fraction. Soil fraction passing a number 200 sieve (0.074-mm-square opening) as a weight percentage of the less than 3 in. (76.4 mm) fraction. Clay as a soil separate consists of mineral soil particles that are less than 0.002 mm in diameter. The estimated clay content of each soil layer is given as a percentage, by weight, of the soil material that is less than 2 mm in diameter. Liquid limit (LL) is one of the standard Atterberg limits used to indicate the plasticity characteristics of a soil. It is the water content, on a percent by weight basis, of the soil (passing #40 sieve) at which the soil changes from a plastic to a liquid. Plasticity index (PI) is one of the standard Atterberg limits used to indicate the plasticity characteristics of a soil. It is defined as the numerical difference between the liquid limit and plastic limit of the soil. The amount of water that would move vertically through a unit area of saturated soil in unit time under unit hydraulic gradient. The estimated volumetric soil water content at or near zero bar tension, expressed as a percentage of the whole soil; also known as saturated volumetric water content or porosity. Soil-Water Characteristic Curve fitting parameter Soil-Water Characteristic Curve fitting parameter Soil-Water Characteristic Curve fitting parameter Soil-Water Characteristic Curve fitting parameter

Table 5 (Continued) Column Label Parameter Description Units CBR_FROM_PI_01 MR_FROM_PI_01 Elev_01 Comppct_01 BedroDepth_01 wtdepannmin_01 wtdepaprjunmin_01 California Bearing Ratio Resilient Modulus Elevation Comp % Bedrock Depth—Minimum Water Table Depth— Annual—Minimum Water Table Depth— April—June—Minimum % Psi M % Ft Ft Ft Measured pressure required to penetrate a soil sample with a plunger of standard area. The measured pressure is then divided by the pressure required to achieve an equal penetration on a standard crushed rock material. CBR data were estimated from simple index properties: It is defined as the ratio between the repeated axial stress to the recoverable axial strain. Resilient modulus was calculated based on CBR values: The vertical distance from mean sea level to a point on the earth’s surface. The percentage of the component of the map unit. The distance from the soil surface to the top of a bedrock layer, expressed as a shallowest depth of components whose composition in the map unit is equal to or exceeds 15%. The shallowest depth to a wet soil layer (water table) at any time during the year ex- pressed as centimeters from the soil surface, for components whose composition in the map unit is equal to or exceeds 15%. The shallowest depth to a wet soil layer (water table) during the months of April through June from the soil surface for components whose composition in the map unit is equal to or exceeds 15%. M psi CBRR ( ) = ( )2555 0 64 . wPI P PI CBR CBR wPI wPI = = = + ( ) > 200 100 75 1 0 728 0 28  . . .09 060 0 358  D wPI( ) =

In addition to the SWCC parameters necessary for the MEPDG, many other relevant MEPDG soil parameters were also computed, checked, and incor- porated in the database. The following additional engineering properties were estimated based on the original database information and then incorporated into the National Database: • Group index • Layer thickness • D60, • Weighted Plasticity Index (wPI) • California Bearing Ratio (CBR), and • Resilient modulus (MR), computed from esti- mated CBR. Table 6 summarizes the percentage of data avail- able for each soil engineering variable considered in the database. For example, there are 31,100 soil units in the database, but only 66% of these have enough information to compute SWCC parameters. The soil properties needed to estimate the SWCC parameters include the volumetric water content at 0.1, 0.33, and 15 bars, and the saturated volumetric water content (i.e., satiated water content or porosity). Computed Parameters AASHTO Group Index The group index is an engineering concept de- veloped by AASHTO that categorizes the probable 15 Table 6 Summary of available engineering data Soil Properties Total Data in Surveys Percentage Map Unit Symbol 31,100 31,100 100 Map Unit Key 31,100 31,100 100 Map Unit Name 31,100 31,100 100 Component Name 31,100 31,100 100 AASHTO Classification 31,100 31,100 100 AASHTO Group Index 31,100 31,100 100 Top Depth of Layer 31,100 31,101 100 Bottom Depth of Layer 31,100 31,101 100 Thickness of the Layer 31,100 31,101 100 Passing # 4 31,100 30,796 99 Passing # 10 31,100 30,796 99 Passing # 40 31,100 30,794 99 Passing # 200 31,100 30,793 99 Passing Sieve 0.002 mm 31,100 1,127 4 Liquid Limit 31,100 27,409 88 Plasticity Index 31,100 30,800 99 Oven dried weight per unit volume at H2O tension of 0.1 bar 31,100 3 0 Oven dried weight per unit volume at H2O tension of 0.33 bar 31,100 29,684 95 Oven dried weight per unit volume at H2O tension of 15 bars 31,100 265 1 Saturated Hydraulic Conductivity (Ksat) 31,100 31,090 100 Volumetric water content at 0.1 bar H2O 31,100 2,151 7 Volumetric water content at 0.33 bar H2O 31,100 20,497 66 Volumetric water content at 15 bars H2O 31,100 20,497 66 Satiated H2O 31,100 20,588 66 SWCC parameter: a 31,100 31,101 100 SWCC parameter: n 31,100 31,100 100 SWCC parameter: m 31,100 31,100 100 SWCC parameter: hr 31,100 31,101 100 California Bearing Ratio (CBR) from Index Properties 31,100 31,101 100 Resilient Modulus from Index Properties 31,100 31,101 100 Elevation 31,100 21,988 71 Comp % 31,100 31,101 100 Water Table Depth—Annual—Minimum 31,100 10,024 32 Water Table Depth—April—June—Minimum 31,100 9,058 29

“service performance” of the soil, particularly when it is used as a highway pavement subgrade. The group index can be calculated by the empirical equation given in AASHTO M145-91, Standard Specification for Classification of Soils and Soil-Aggregate Mix- tures for Highway Construction Purposes: Where: P200 = Passing the No. 200 sieve LL = Liquid Limit PI = Plasticity Index Note that the first term in equation (1) is related to the liquid limit and the second to the plasticity index. The final GI value is based on the following qualifications: • If the GI calculated is negative, it is taken to be zero. • The GI calculated is rounded to the nearest whole number. • There is no upper limit. • The GI for the following soils must be taken as zero: A-1-a, A-1-b, A-2-4, A-2-5, and A-3. • For soils A-2-6 and A-2-7, the GI must be cal- culated by the equation: Soil-Water Characteristic Curve Parameters The SWCC is defined as the relationship between soil water content and soil matric suction (Fredlund, 2006). The water content refers to either the volu- metric water content (ratio of volume of water to the volume of solids) or the degree of saturation (per- centage of voids filled with water), depending upon the intended use of the SWCC relationship. The soil suction corresponds to the matric suction (ua − uw), which is the difference between the pore-air pressure and the pore-water pressure. Several researchers have proposed models to define the SWCC. The model implemented in the MEPDG is that of Fredlund & Xing (Fredlund & Xing, 1994). This model is represented as a sigmoidal function with four fitting parameters. In terms of degree of saturation and English units, the equa- tion reads: GI P PI= −( ) −( )0 01 15 10 2200. ( ) GI P LL P = −( ) + −( )[ ] + −( 200 200 35 0 2 0 005 40 0 01 15 . . . ) −( )PI 10 1( ) Where: S = Degree of saturation, in % h = Soil matric suction, in psi af = Soil fitting parameter which is related to the air entry value of the soil, in psi bf = Soil fitting parameter which is related to the rate of water extraction of the soil after exceeding the air entry value cf = Soil fitting parameter which is related to the residual water content of the soil hr = Soil fitting parameter which is related to the suction value at which the resid- ual water content occurs, in psi This particular equation sets the maximum suc- tion value (zero moisture content) at 1.45×105 psi (1,000,000 kPa), which is very convenient when incorporated in numerical analysis because it elim- inates the possibility of indeterminate results when the moisture content approaches zero. In order to find the four fitting parameters for the 31,100 soils, a macro was created to repeat the com- putation required for each soil. Figure 3 shows an example of the computations needed for one soil. The “Solver” function in Microsoft Excel was used to optimize the nonlinear relationship, by finding the least squared error between the measured and predicted moisture content data available at each suction value. Two or three measured suction val- ues were consistently available in the database. In addition, the saturated volumetric water content (soil porosity) was available, which corresponded to a suction equal to zero. The fourth point was set to one million kPa (145,000 psi) of suction at zero moisture content. California Bearing Ratio (CBR) and Resilient Modulus (MR) The CBR is an empirical soil property that characterizes the strength of subgrade and unbound C h h h r r ψ( ) = − + ⎛ ⎝⎜ ⎞ ⎠⎟ + ×⎛ ⎝⎜ ⎞ ⎠⎟ ⎡ ⎣⎢ 1 1 1 1 45 10 5 ln ln . ⎤⎦⎥ ( )4 S C e h af b cf f = ( ) × + ⎛ ⎝⎜ ⎞ ⎠⎟ ⎡ ⎣⎢ ⎤ ⎦⎥ ⎧⎨⎪⎩⎪ ⎫⎬⎪⎭⎪ ⎡ ⎣ ψ 1 ln ⎢⎢⎢⎢⎢ ⎤ ⎦ ⎥⎥⎥⎥⎥ ( )3 16

materials. The resilient modulus can then be esti- mated from the CBR by using the expression: that is used in the MEPDG (Witczak et al., 2001). CBR values can also be estimated from index soil properties like grain size distribution and Atterberg limits. U.S. Geological Survey (USGS) and AASHTO classification are correlated to estimate typical CBR and MR values. However, another practical way is to use the grain size distribution. First, grain size M psi CBRR ( ) = ×2555 50 64. ( ) distribution parameters as the D60, Passing 200, and Plasticity Index are used to estimate the weighted plasticity index (wPI) by the expression: The value of wPI will depend on the type of soil being considered. For coarse soils, wPI = 0; for soils with more than 12 percent of fines, wPI > 0. For coarse soils (with wPI = 0), the CBR value is related to the grain diameter at which 60% of the wPI P PI= ×200 100 6( ) 17 Suction Suction Dry Density Vol. Water Content Sat Vol. Water Content Sat (Bar) (kPa) (gm/cc) Note (%) (%) 0.1 10 N/A 39.0 0.33 33.33 Not reliable 25.8 66.2 15 1500 N/A 14.2 36.4 SWCC Parameters Final Initial af 1.1972 10 Objective Function bf 1.4156 1 1.05642E-09 cf 0.4969 2 hr 3,000.0 3000 xe Vol. Water Content ye yp Constraints Suction (psi) (%) Sat 0.0001 39.0 100.0 100.0 0.000 4.8309 25.8 66.2 66.2 0.000 217.3913 14.2 36.4 36.4 0.000 Figure 3 Spreadsheet with the Soil-Water Characteristic Curve parameter calculations.

grain size distribution passes (D60), in millimeters. The equation in this case is: This equation has two limitations: for soils with D60 less than 0.01 mm, CBR = 5 is used and for soils with D60 greater than 30 mm, CBR = 95 is used. For fine soils (with wPI > 0), the expression that is used is: It should be recognized that all of these con- ditions are approximations to the real measured laboratory value for either CBR or MR. Their use should be confined to Level 3 applications of the MEPDG. GIS AND CARTOGRAPHIC PROCESS A series of maps was created to allow the user of the MEPDG to visually identify the specific geo- graphic region of interest. Each soil unit depicted in these maps was uniquely identified to allow for a search of the material properties in the National Database. The procedures used to create these maps are described next. Software All GIS and cartographic work was performed using ESRI’s ArcGIS 9.2, Microsoft Excel 2007, and Microsoft Access 2007. Data Collection The first step in the map creation involved col- lecting the necessary elements of the map. The soil unit boundaries and related tabular data were down- loaded from the NRCS NATSGO database. The spa- tial boundaries are in the form of a shapefile, to be used with the GIS software, and the tabular data were stored as Microsoft Access files. Both the spa- tial and tabular files contained data for the 50 U.S. states and Puerto Rico. CBR wPI = + ( ) 75 1 0 728 8 . ( ) CBR D= ( )28 09 760 0 358. ( ). Boundary files for the 50 states and Puerto Rico were downloaded as shapefiles from the U.S. Cen- sus Bureau website. These files, known as TIGER files, are free and readily available to the public. In addition to state boundaries, shapefiles of the main interstate road network were also downloaded from the U.S. Census Bureau. Data Preparation Road Shapefiles After downloading all of the data from the Cen- sus Bureau, some shapefiles needed modification before they could be used. In particular, the road files from the Census Bureau were greatly altered before use. These road files are organized by county, so the first step involved combining the numerous county files by state, using ArcGIS 9.2. Once these files were reduced to one shapefile per state, a query was performed to identify only the interstates in the highway network. The Census Bureau uses Census Feature Class Codes (CFCC) to identify the vari- ous types of roads, and these classification codes are included in the data table for all road shape- files. Using these tables in ArcGIS 9.2, roads with a CFCC of “A1” or “A2,” which indicate interstates and other major highways, were selected. Once se- lected, these roads were exported to a separate file. As a result, 50 new shapefiles were created that held data for the major highways in each state. These smaller files allowed for quicker data processing and mapping. Map Unit Keys and Map Characteristics Like the road files, the map unit boundary shape- file (and its associated data table) required additional analysis before mapping could begin. First, the state boundary shapefile and the map unit boundary shape- file were combined using a spatial join in ArcGIS 9.2 to reveal the number of map units in each state at the level of detail for the NATSGO data. Then this new shapefile was used to draft a state map, which revealed areas of missing data. Furthermore, a summary function was applied to the Mukey column in the data table of the map unit boundary file. The summary function allowed for the creation of a new table that showed the number of times each unique Mukey appeared in the table and the overall number of unique Mukeys. 18

After identifying each unique Mukey, a Map Character was assigned to each one; each Mukey was assigned a three-character alpha-numeric code. Since each Mukey is at least six characters long, use of the Map Character substantially reduced the space required in the database for labeling purposes. Map Characters are the key labels that allow the user of the database to easily search for the unbound material information needed to run the MEPDG. Map Projection and Coordinate System All of the original data were downloaded as un- projected shapefiles within the Geographic Coordi- nate System (GCS) North American 1983 coordinate system. Following common practice, these data were re-projected to a more appropriate projection and coordinate system to reduce distortion. It is impor- tant to note that all two-dimensional maps of the globe contain some distortion, due to the process of representing a three-dimensional surface on a two-dimensional plane. Regardless of the type of projection, some distortion of area, shape, direc- tion, or distance occurs. In order to reduce distor- tion, each state was treated as a separate set of maps, and the State Plane Coordinate System (SPCS) was employed. According to Stem (1989), the SPCS was devel- oped by the U.S. Coast and Geodetic Survey in the 1930s and uses three different projections, depend- ing on the orientation of the state or zone. The SPCS is commonly used to reduce distortion in large-scale maps, generally at the county level or below. For each state, the zone that covers the largest region was used as the projection and coordinate system for that state’s maps, which resulted in an atlas of maps with a minimum of distortion. User Interface To minimize any difficulties in the search of a particular soil profile corresponding to a specific proj- ect location, a simple user interface was developed. This interface is an extraction tool that facilitates the use, analysis, and reporting of the data and maps implemented in the National Database. It contains links to the maps that allow the user to choose the soil unit of interest and extract, in a printable re- port, all the information available for that particu- lar unit. The interface was developed using Microsoft Excel and Adobe Acrobat. The maps are provided as Adobe PDF files, while Excel is used for the data- base and the interface. USING THE NATIONAL DATABASE The steps required to extract the desired subgrade SWCC and soil properties at a selected site using the Microsoft Excel interface follow: 1. Choose the state map. a. The state map is divided with a numbered grid, which allows the user to narrow the search to a smaller region within the state. The map for Louisiana is presented as an example in Figure 4. 2. Select the region number of interest from the state grid map. a. Maps are created for each region within each state. There are 814 maps available. The map of Region 14 in Louisiana is pre- sented as an example in Figure 5. 3. Select the map unit desired to extract the rele- vant soil property data from the region map and note the map unit number (Mapchar number). 4. Input the Mapchar number in the Microsoft Excel file interface. 5. Display and, if desired, print a report of the soil properties for that particular map unit. a. Figure 6 shows an example of a printable report. SUMMARY The National Database developed in this project provides an easy, efficient access to Level 3 unbound material properties required for input to the MEPDG for Level 3 designs. Besides a full set of Level 3 data, the database also provides most information required for Levels 1 and 2 designs, including SWCC parameters and saturated hydraulic conductivity. In addition, the information in the database permits predictions of typical resilient modulus and CBR values based on soil index properties. This infor- mation is organized in GIS Soil Unit Maps for the entire United States and Puerto Rico. The National Database provides transportation agencies with a tool to design better-performing and more cost-effective pavements through the use of 19

28 31 97 5 64 1 1 1 6 1 91 7 1 0 1 8 1 2 1 51 3 1 4 89°0'0"W 89°0'0"W 90°0'0"W 90°0'0"W 91°0'0"W 91°0'0"W 92°0'0"W 92°0'0"W 93°0'0"W 93°0'0"W 94°0'0"W 94°0'0"W 33°0'0"N 33°0'0"N 32°0'0"N 32°0'0"N 31°0'0"N 31°0'0"N 30°0'0"N 30°0'0"N 29°0'0"N 29°0'0"N Created by: Natalie Lopez, Gustavo Torres, and Dr.Claudia Zapata 0 20 40 60 80 Miles This map was produced for the Department of Civil and Environmental Engineering at Arizona State University. State boundaries and roads courtesy of the US Census. Figure 4 State of Louisiana map with region grid. 20

21 212 EZ8 EU9 FC0 FB5 FA4 FA5 FC9 FB4 EW5 FC9 EZ8 EW5 FD2 FC3 FB3 FA4 FB4 FA8 EX0 EX3 FB4 FD1 FA7 FB3 212 212 FA4 FB3 FJ9 FA5 FD2 FC7 FB4 FC1 FA5 FA4 FD2 FC2 EZ8 FA8 212 FC0 FB7 EX0 FA2 FC1 FD4 FC0 EZ6 FB4 FA5 EW5 FD4 EY8 FD1 FC6 FC5 FC6 FC6 FA4 FC5 FB3 FB3 EY9 EZ8 FD1 EW4 EU9 FB6 EX0 FC5 FB5 FD1 EU9 EY8 EZ9 FC5 FC9 FA3 EY9 FA4 EY9 EY8 FC0 ET9 FA5 EZ8 FB7 EZ6 FD2 EZ1 EX9 FC5 FD1 FA2 EY9 FA8 EY9 FD2 FD4 EX0 FD8 EZ7 EZ6 FD1 EY8 FD2 EW5 FC2 FB8 FD8 EZ6 EX0 EZ6 FD6 EZ4 FC7 FC5 FD8 FB8 212 FD7 FD1 FD0 FA7 EX8 EX0 FA4 FC5 EY6 FC5 FA5 FD1 FC0 FA4 FC9 FA7 FA5 FD1 EX7 I-12 I-10 I-55 I-310 I-610 I-12 I-10 90°0'0"W 90°0'0"W 90°10'0"W 90°10'0"W 90°20'0"W 90°20'0"W 90°30'0"W 90°30'0"W 90°40'0"W 90°40'0"W 90°50'0"W 90°50'0"W 91°0'0"W 30°30'0"N 30°30'0"N 30°20'0"N 30°20'0"N 30°10'0"N 30°10'0"N 30°0'0"N 30°0'0"N 29°50'0"N 29°50'0"N 2 8 31 97 5 64 11 16 1917 10 18 12 1513 14 Created by: Natalie Lopez Data by: Gustavo Torres, Claudia Zapata 0 5 10 15 20 Miles Date: 8/11/09 Projected Coordinate System: NAD 1983, State Plane, Louisiana North, FIPS 1701 Projection: Lambert Conformal Conic This map was produced for the Department of Civil and Environmental Engineering at Arizona State University. Soil unit data was downloaded from the USDA NRCS. State boundaries and roads courtesy of the US Census. Figure 5 Region 14 soil unit map for the state of Louisiana.

National Catalogue of Natural Subgrade Properties Needed for the MEPDG Input Map Char FA5 Mapunit Key 667683 Mapunit Name Springfield-Natalbany-Encrow-Colyell (s2864) Component Name Colyell Top Layer Layer 2 Layer 3 Layer 4 Layer 5 Layer 6 Layer 7 Layer 8 Layer 9 AASHTO Classification A-4 A-4 A-7-6 A-7-6 AASHTO Group Index 6 5 32 19 Top Depth (in.) Bottom Depth (in.) 3.1 11.8 39.0 59.8 Thickness (in.) 3.1 8.7 27.2 20.9 % Component 25 25 25 25 Water Table Depth - Annual Min (ft) 0.8 0.8 0.8 0.8 Depth to Bedrock (ft) N/A STRENGTH PROPERTIES CBR from Index Properties Resilient Modulus from Index Properties (psi) 11,621 12,908 5,730 7,558 INDEX PROPERTIES Passing #4 (%) Passing #10 (%) Passing #40 (%) Passing #200 (%) Passing 0.002 mm (%) Liquid Limit (%) Plasticity Index (%) Saturated Volumetric Water CONTENT (%) Saturated Hydraulic Conductivity Ksat (ft/hr) SOIL-WATER CHARACTERISTIC CURVE PARAMETERS Parameter af (psi) Parameter bf Parameter cf Parameter hr (psi) 0.0 3.1 11.8 39.0 11 13 4 5 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 97.5 97.5 97.5 97.5 7.0 12.5 47.0 29.0 22.0 23.5 55.0 41.0 8.5 7.0 28.5 18.0 43.0 44.0 44.0 40.0 0.11 0.11 0.01 0.03 9.5142 8.8036 0.5313 10.7499 0.9430 0.9069 1.0255 1.0082 1.0789 1.0745 0.2021 0.4163 3000.00 3000.00 3000.20 2999.99 Figure 6 Example of a printable report displayed in the Excel Interface. 22

measured materials properties rather than empirical relationships. The database will greatly assist pavement design- ers using the MEPDG and other pavement design methods. The SWCC parameters contained in the database represent the largest collection of this type of information available in the world. This database will also allow further analyses to estimate better default parameters for Level 3 designs. Parameters such as the group index, the complete soil gradation, and the Atterberg limits can be used to further subdivide soil classifications and improve the default param- eters used as MEPDG inputs. Finally, and as im- portantly, researchers can use the database to revise and update the SWCC models currently available in the MEPDG. REFERENCES Fredlund, D. G. (2006). “Unsaturated Soil Mechanics in Engineering Practice,” Journal of Geotechnical and Geoenvironmental Engineering, Vol. 132, No. 3, pp. 286–321. Fredlund, D. G. and Xing, A. (l994). “Equations for the soil-water characteristic curve,” Canadian Geotech- nical Journal, Vol. 31, pp. 521–532. Stem, J. E. (1989). “State Plane Coordinate System of 1983.” NOAA Manual NOS NGS 5. U.S. Govern- ment Printing Office, Washington, D.C. Witczak, M. W., Houston, W. N., and Zapata, C. E. (2001). Correlation of CBR Values with Soil Index Properties. Development of the 2002 Guide for the Design of New and Rehabilitated Pavement Structures—Technical Report. NCHRP Project 1-37A, Transportation Research Board of the National Academies. 23

Transportation Research Board 500 Fifth Street, NW Washington, DC 20001 These digests are issued in order to increase awareness of research results emanating from projects in the Cooperative Research Programs (CRP). Persons wanting to pursue the project subject matter in greater depth should contact the CRP Staff, Transportation Research Board of the National Academies, 500 Fifth Street, NW, Washington, DC 20001. COPYRIGHT INFORMATION Authors herein are responsible for the authenticity of their materials and for obtaining written permissions from publishers or persons who own the copyright to any previously published or copyrighted material used herein. Cooperative Research Programs (CRP) grants permission to reproduce material in this publication for classroom and not-for-profit purposes. Permission is given with the understanding that none of the material will be used to imply TRB, AASHTO, FAA, FHWA, FMCSA, FTA, or Transit Development Corporation endorsement of a particular product, method, or practice. It is expected that those reproducing the material in this document for educational and not-for-profit uses will give appropriate acknowledgment of the source of any reprinted or reproduced material. For other uses of the material, request permission from CRP. Subscriber Categories: Highways • Design • Geotechnology ISBN 978-0-309-15494-9 9 780309 154949 9 0 0 0 0