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Geotechnical Aspects of Pavements Reference Manual
Chapter 5.0 Geotechnical Inputs For Pavement Design (continued)
5.5 Thermo-Hydraulic Properties
Thermo-hydraulic material properties are required to evaluate the temperature and moisture conditions in a pavement system and their effects on the material behavior. Temperature has significant effects on the stiffness of asphalt concrete, and temperature gradients can induce thermal curling and stresses in rigid pavement slabs. Moisture content influences the stiffness and strength of unbound materials, and moisture gradients can induce warping of rigid pavement slabs. Combined temperature and moisture effects can cause detrimental freeze/thaw cycles in unbound materials.5
The empirical 1993 AASHTO Design Guide and the mechanistic-empirical NCHRP 1-37A design procedure have drastically different input requirements for thermo-hydraulic properties. The thermo-hydraulic design inputs in the 1993 AASHTO Guide are largely empirical coefficients grouped in the following categories:
- Drainage coefficients (for unbound layers)
- Swelling parameters (for expansive subgrade soils)
- Frost heave parameters (for frost-susceptible subgrade soils)
These empirical properties in the 1993 Guide often mix material property and climate factors. For example, drainage coefficients are functions of both climate-determined moisture conditions and material-related drainage quality.
The thermo-hydraulic properties required as input to the NCHRP 1-37A Design Guide tend to be more fundamental material properties. These include:
- Groundwater table depth
- Infiltration and drainage properties
- Physical properties
- Soil water characteristic curve
- Hydraulic conductivity (permeability)
- Thermal conductivity
- Heat capacity
These thermo-hydraulic properties are used in the mechanistic Enhanced Integrated Climate Model (EICM) along with climate inputs (discussed separately in Section 5.6) to predict temperature and moisture distributions in the pavement as functions of depth and time. Appendix D provides details on algorithms embedded in the EICM.
Because of the substantial differences in these thermo-hydraulic inputs to the two design methods, each design method is discussed separately in the following subsections.
5.5.1 1993 AASHTO Guide
The environment-related aspects in the 1993 AASHTO Design Guide are grouped into two general categories: drainage and subgrade swelling/frost heave. As described in Section 3.5.2 in Chapter 3, drainage is incorporated via adjustment to the unbound structural layer coefficients for flexible pavements or via a drainage factor in the design equation for rigid pavements. Swelling and/or frost heave, on the other hand, is incorporated via a partitioning of the total allowable serviceability loss ΔPSI; part of ΔPSI is allocated to environment-induced deterioration due to swelling and/or frost heave, and the remainder of ΔPSI is allocated to traffic-induced deterioration.
The 1993 AASHTO Guide provides guidance for the design of subsurface drainage systems and modifications to the flexible and rigid pavement design procedure to take advantage of improvements in performance due to good drainage. For flexible pavements, the benefits of drainage are incorporated into the structural number via empirical drainage coefficients:(5.40)
SN = a1D1 + a2D2m2 + a3D3m3
in which m2 and m3 are the drainage coefficients for the base and subbase layers, respectively, and all other terms are as defined previously. Table 5-49 summarizes the recommended values for mi in the 1993 AASHTO Guide as functions of qualitative descriptions of drainage quality and climate conditions.
For rigid pavements, the benefits of drainage are incorporated via an empirical drainage coefficient Cd in the rigid pavement design equation. Table 5-50 summarizes the recommended values for Cd in the 1993 AASHTO Guide as a function of qualitative descriptions of drainage quality and climate conditions.
|Quality of Drainage||Water Removed Within||Percent of Time Pavement is Exposed to Moisture Levels Approaching Saturation|
|< 1%||1-5%||5-25%||> 25%|
|Very Poor||no drainage||0.95-0.75||0.95-0.75||0.75-0.40||0.40|
|Quality of Drainage||Water Removed Within||Percent of Time Pavement is Exposed to Moisture Levels Approaching Saturation|
|< 1%||1-5%||5-25%||> 25%|
|Very Poor||no drainage||1.00-0.90||0.90-0.80||0.80-0.70||0.70|
The 1993 AASHTO Guide includes three empirical parameters for estimating potential serviceability loss due to swelling:
- Swell rate constant θ
- Potential vertical rise VR
- Swell probability PS
The swell rate constant θ is used to estimate the rate at which swelling will take place. It varies between 0.04 and 0.20, with higher values appropriate for soils exposed to a large moisture supply either due to high rainfall, poor drainage, or some other source. Figure 5-33 provides a nomograph for subjectively estimating the rate of subgrade soil swelling based upon qualitative descriptions of moisture supply and soil fabric. Little guidance beyond that in Figure 5-33 is provided in the 1993 Guide for estimating the values for moisture supply and soil fabric.
The potential vertical rise VR is a measure of the vertical expansion that may occur in the subgrade soil under extreme swell conditions. Although it is possible to measure VR from laboratory swell tests, this is not commonly done in practice. Instead, VR is estimated using the chart in Figure 5-34 based on the soil's plasticity index, moisture condition, and overall thickness of the layer. The moisture condition is a subjective estimate of the difference between the in-situ moisture conditions during construction and moisture conditions at a later date.
The swell probability (PS) is a measure of the proportion (percent) of the project length that is subject to swell. The probability of swelling at a given location is assumed to be 100% if the subgrade soil plasticity index is greater than 30 and the layer thickness is greater than 2 feet (or if VR is greater than 0.20 inches). These criteria can be used to separate the project length into swelling and nonswelling sections, from which a length-averaged estimate of PS can be determined.
These three swelling parameters are used in a nomograph (see Appendix C) along with the design life to determine the expected serviceability loss due to swelling ΔPSISW. However, it should be clear from the empirical and highly subjective procedures used to determine the input parameters that the predicted ΔPSISW will be only a very approximate estimate.
Frost Heave Parameters
The 1993 AASHTO Guide includes three empirical parameters for estimating potential serviceability loss due to frost heave:
- Frost heave rate φ
- Maximum potential serviceability loss ΔPSIMAX
- Frost heave probability PF
The frost heave rate φ is a measure of the rate of increase of frost heave in millimeters per day. The rate of frost heave depends on the type of subgrade material, in particular the percentage of fine-grained material. Figure 5-35 can be used to estimate the rate of frost heave based on the USCS class for the subgrade and the percentage of material finer than 0.02 mm.
The maximum potential serviceability loss ΔPSIMAX due to frost heave is dependent on the quality of drainage and the depth of frost penetration. Figure 5-36 can be used to estimate the maximum potential serviceability loss due to these two factors. The drainage quality parameter in Figure 5-36 is the same as that used to define the drainage coefficients in Table 5-49 and Table 5-50. See Yoder and Witczak (1975) for methods for determining the depth of frost penetration.
The frost heave probability PF is the designer's estimate of the percentage length of the project that will experience frost heave. This estimate will depend upon the extent of frost-susceptible subgrade material, moisture availability, drainage quality, number of freeze-thaw cycles during the year, and the depth of frost penetration. Past experience is valuable here, as there is no clear method for approximating the frost heave probability.
These three frost heave parameters are used in a nomograph (see Appendix C) along with the design life to determine the expected serviceability loss due to frost heave ΔPSIFH. However, it should be clear from the empirical and highly subjective procedures used to determine the input parameters that the predicted ΔPSIFH will be only a very approximate estimate.
Figure 5-35. Chart for estimating frost heave rate for subgrade soil (AASHTO, 1993).
Figure 5-36. Graph for estimating maximum serviceability loss due to frost heave (AASHTO, 1993).
5.5.2 NCHRP 1-37A Design Guide
The thermo-hydraulic properties required as input to the NCHRP 1-37A Design Guide can be grouped into the following categories:
- Groundwater depth
- Infiltration and drainage properties
- Physical/index properties
- Soil water characteristic curve
- Hydraulic conductivity (permeability)
- Thermal conductivity
- Heat capacity
Methods for determining the design inputs in each of these categories are described in the following subsections. In some cases, the design inputs are determined by direct measurement in the laboratory or the field. However, other design inputs (e.g., soil water characteristic curve) are much less commonly measured in geotechnical practice. Recognizing this, the NCHRP 1-37A project team expended substantial effort to develop robust correlations between these properties and other more conventional soil properties (e.g., gradation and plasticity). These correlations are also detailed in the following subsections as appropriate.
The groundwater depth plays a significant role in the NCHRP 1-37A Design Guide predictions of moisture content distributions in the unbound pavement materials and thus on the seasonal resilient modulus values. The input value is intended to be the best estimate of the annual average groundwater depth. Groundwater depth can be determined from profile characterization borings during design (see Section 4.7.1) or estimated. The county soil reports produced by the National Resources Conservation Service can often be used to develop estimates of groundwater depth.
Infiltration and Drainage
Three input parameters related to infiltration and drainage are required in the NCHRP 1-37A design methodology:
- Amount of infiltration
- Pavement cross slope
- Drainage path length
Amount of Infiltration
The amount of infiltration will be a function of rainfall intensity and duration (determined from the climate inputs, see Section 5.6), pavement condition, shoulder type, and drainage features. The NCHRP 1-37A Design Guide qualitatively divides infiltration into four categories, as summarized in Table 5-51. These categories are used at all hierarchical input levels. The infiltration category is based upon shoulder type, generally the largest single source of moisture entry into the pavement structure, and edge drains, since these shorten the drainage path and provide a positive drainage outlet. Note that if a drainage layer is present in addition to edge drains, its influence is automatically accounted for within the EICM moisture calculations.
|Infiltration Category||Conditions||% Precipitation Entering Pavement|
|Minor||This option is valid when tied and sealed concrete shoulders (rigid pavements), widened PCC lanes, or full-width AC paving (monolithic main lane and shoulder) are used or when an aggressive policy is pursued to keep the lane-shoulder joint sealed. This option is also applicable when edge drains are used.||10|
|Moderate||This option is valid for all other shoulder types, PCC restoration, and AC overlays over old and cracked existing pavements where reflection cracking will likely occur.||50|
|Extreme||Generally not used for new or reconstructed pavement levels.||100|
Most designs and maintenance activities, especially for higher functional class pavements, should strive to achieve zero infiltration or reduce it to a minimum value. This can be done by proper design of surface drainage elements (cross slopes, side ditches, etc.), adopting construction practices that reduce infiltration (e.g., eliminating cold lane/shoulder joints, use of tied joints for PCC pavements, etc.), proactive routine maintenance activities (e.g., crack and joint sealing, surface treatments, etc.), and providing adequate subsurface drainage (e.g., drainage layers, edge drains). Chapter 7 provides more information on pavement drainage systems.
Pavement Cross Slope
The pavement cross slope is the slope of the surface perpendicular to the direction of traffic. This input is used in computing the drainage path length, as described in the next subsection.
Drainage Path Length
The drainage path length is the resultant of the cross and longitudinal slopes of the pavement. It is measured from the highest point in the pavement cross section to the drainage outlet. This input is used in the EICM's infiltration and drainage model to compute the time required to drain an unbound base or subbase layer from an initially wet condition.
The DRIP computer program (Mallela et al., 2002) can be used to compute the drainage path length based on pavement cross and longitudinal slopes, lane widths, edge drain trench widths (if applicable, and cross section crown and superelevation). The DRIP program is provided as part of the NCHRP 1-37A Design Guide software.
Several physical properties are required for the internal calculations in the EICM. For unbound materials, these are:
- Specific gravity of solids Gs (see Table 5-10)
- Maximum dry unit weight γd max (see Table 5-13)
- Optimum gravimetric moisture content wopt (see Table 5-13)
Table 5-52 describes the procedures to obtain these physical property inputs for hierarchical input levels 1 and 2 (level 3 inputs are not applicable for this input category). From these properties, all other necessary weight and volume properties required in the EICM can be computed. These include:
- Degree of saturation at optimum compaction (Sopt)
- Optimum volumetric moisture content (θopt)
- Saturated volumetric water content (θsat)
For rehabilitation designs only, the equilibrium or in-situ gravimetric water content is also a required input. NCHRP 1-37A recommends that this value be estimated from direct testing of bulk samples retrieved from the site, or through other appropriate means.
Although the material properties of the lower natural subgrade layers are important to the overall response of the pavement, a lower level of effort is generally sufficient to characterize these deeper layers as compared to the overlying compacted materials. Level 1 inputs are thus generally not necessary for in-situ subgrade materials. NCHRP 1-37A recommends that only gradation properties and Atterberg limits be measured for the in-situ subgrade materials.
|Material Property||Input Level||Description|
|Specific gravity, Gs||1||A direct measurement using AASHTO T100 (performed in conjunction with consolidation tests - T180 for bases or T 99 for other layers).|
See Table 5-10.
|2||Determined from P2001 and PI2 of the layer as below:|
|Optimum gravimetric water content, wopt, and maximum dry unit weight of solids, (γd)max||1||Typically, AASHTO T180 compaction test for base layers and AASHTO T99 compaction test for other layers. See Table 5-13.|
|2||Estimated from D601, P2001 and PI2 of the layer following these steps:
- P200 and D60 can be obtained from a grain-size distribution test (AASHTO T 27)-see Table 5-19.
- PI can be determined from an Atterberg limit test (AASHTO T 90)-see Table 5-21.
Soil Water Characteristic Curve
The soil water characteristic curve (SWCC) defines the relationship between water content and matric suction h for a given soil. Matric suction is defined as the difference between the pore air pressure ua and pore water pressure uw in a partially saturated soil:(5.51)
h = ( ua - uw )
This relationship is usually plotted as the variation of water content (gravimetric w, volumetric θ, or degree of saturation S) vs. soil suction (Figure 5-37). The SWCC is one of the primary material inputs used in the EICM to compute moisture distributions with depth and time. Although the SWCC can be measured in the laboratory (e.g., see Fredlund and Rahardjo, 1993), this is quite uncommon and rather difficult. Instead, empirical models are used to express the SWCC in terms of other, more easily measurable parameters. The EICM algorithms in the NCHRP 1-37A analysis procedure are based on a SWCC model proposed by Fredlund and Xing (1994):(5.52)
|θw = C ( h ) ×||θsat|
|ln||EXP ( 1 ) +||h||bf||cf|
|C ( h ) =||1 -||ln||1 +||h|
|ln||1 +||1.45 × 105|
|h||=||matric suction (units of stress)|
|θsat||=||volumetric moisture content at saturation|
|af, bf, cf, and hr||=||model parameters (af, hr in units of stress)|
Figure 5-37. Soil water characteristic curves (NCHRP 1-37A, 2004).
Table 5-53 summarizes the NCHRP 1-37A recommended approach for estimating the parameters of the SWCC at each of the three hierarchical input levels.
|Input Level||Procedure to Determine SWCC parameters||Required Testing|
||Pressure plate, filter paper, and/or Tempe cell testing.
AASHTO T180 or T99 for γd max (see Table 5-13).
AASHTO T100 for Gs (see Table 5-10).
|AASHTO T180 or T99 for γd max (see Table 5-13).
T100 for Gs (see Table 5-10).
AASHTO T88 for P200 and D60 (see Table 5-19).
AASTHO T90 for PI (see Table 5-21).
|3||Direct measurement and input of P200, PI, and D60, after which the EICM uses correlations with P200PI and D60 to automatically generate the SWCC parameters for each soil as follows:
||AASHTO T88 for P200 and D60 (see Table 5-19).
AASHTO T90 for PI (see Table 5-21).
Hydraulic Conductivity (Permeability)
Hydraulic conductivity (or permeability) k describes the ability of a material to conduct fluid (water). It is defined as the quantity of fluid flow through a unit area of soil under a unit pressure gradient. Hydraulic conductivity is one of the primary material inputs to the environment model in the NCHRP 1-37A analysis procedure, where it is used to determine the transient moisture profiles in unbound materials and to estimate their drainage characteristics.
The unsaturated flow algorithms in the EICM require a complete specification of the unsaturated hydraulic conductivity as a function of matric suction h. Although the unsaturated hydraulic conductivity vs. matric suction relationship can be measured in the laboratory (e.g., see Fredlund and Rahardjo, 1993), this is uncommon and difficult. At best, only the saturated hydraulic conductivity ksat is measured in practice. Consequently, within the EICM an empirical model proposed by Fredlund et al. (1994) is used to express the unsaturated hydraulic conductivity k(h) vs. matric suction h relationship in terms of the Fredlund and Xing (1994) SWCC model, Eqs. (5.52) and (5.53). The k(h) model is expressed in terms of a relative hydraulic conductivity:(5.54)
kr ( h ) = k ( h ) / ksat
Recommendations from NCHRP 1-37A for determining the ksat value needed in Eq. (5.54) are summarized in Table 5-54. The Fredlund et al. (1994) model for kr(h) is then expressed in integral form as:(5.66)
|kr ( h ) =||h||∫||hr||θ ( x ) - θ ( h )||θ′ ( x ) dx|
|have||∫||hr||θ ( x ) - θs||θ′ ( x ) dx|
|θ(h)||=||volumetric water content as a function of matric suction, from the SWCC Eqs. (5.52) and (5.53)|
|θs||=||saturated volumetric water content|
|θ′(h)||=||derivative of the SWCC|
|x||=||dummy integration variable corresponding to water content|
|hr||=||matric suction corresponding to the residual water content (i.e., the water content below which a large increase in suction is required to remove additional water)|
|have||=||the air-entry matric suction (i.e., the suction where air starts to enter the largest pores in the soil)|
The procedures described in Table 5-53 are used in the EICM to determine the SWCC via Eqs. (5.52) and (5.53), which in turn is then used to determine the unsaturated hydraulic conductivity via Eq. (5.66). These calculations are performed internally within the EICM software.
|Material Property||Input Level||Description|
|1||Direct measurement using a permeability test (AASHTO T215) - see Table 5-55.|
|2||Determined from P2001, D601, and PI2 of the layer as below:
- P200 and D60 can be obtained from a grain-size distribution test (Table 5-19)
- PI can be determined from an Atterberg limit test (Table 5-21).
|Description||Quantity of fluid flow through a unit area of soil under a unit pressure gradient.|
|Uses in Pavements||Used in the EICM for predicting distributions of moisture with depth and time in the NCHRP 1-37A Design Guide.|
|Laboratory Determination||AASHTO T 215; ASTM D 2434 (Granular Soils), ASTM D 5084 (All Soils). There are two basic standard types of test procedures to directly determine permeability: (1) the constant head test, normally used for coarse materials (Figure 5-38); and (2) the falling head test, normally used for clays (Figure 5-39). Undisturbed, remolded, or compacted samples can be used in both procedures.|
|Field Measurement||Pumping tests can be used to measure hydraulic conductivity in-situ.|
|Commentary||Both test procedures determine permeability of soils under specified conditions. The geotechnical engineer must establish which test conditions are representative of the problem under consideration. As with all other laboratory tests, the geotechnical engineer has to be aware of the limitations of this test. The process is sensitive to the presence of air or gases in the voids and in the permeant or water. Prior to the test, distilled, de aired water should be run through the specimen to remove as much of the air and gas as practical. It is a good practice to use de aired or distilled water at temperatures slightly higher than the temperature of the specimen. As the water permeates through the voids and cools, it will have a tendency to dissolve the air and some of the gases, thus removing them during this process. The result will be a more representative, albeit idealized, permeability value.|
The type of permeameter, (i.e., flexible wall ASTM D 5084 versus rigid ASTM D 2434 and AASHTO T215) may also affect the final results. For testing of fine grained low permeability soils, the use of flexible wall permeameters is recommended, which are essentially very similar to the triaxial test apparatus. When rigid wall units are used, the permeant may find a route at the sample permeameter interface. This will produce erroneous results. It should be emphasized that permeability is sensitive to viscosity. In computing permeability, correction factors for viscosity and temperatures must be applied. The temperature of the permeant and the laboratory should be kept constant during testing.
|Typical Values||See Table 5-56 and Table 5-57. Saturated hydraulic conductivity for loose clean sands can also be estimated using the Hazen relationship:
ksat = C * D102
Figure 5-38. Schematic of a constant head permeameter (Coduto, 1999).
Figure 5-39. Schematic of a falling head permeameter (Coduto, 1999).
|Soil Description||Hydraulic Conductivity k|
|Clean gravel||1 - 100||3x10-2 - 3|
|Sand-gravel mixtures||10-2 - 10||3x10-4 - 0.3|
|Clean coarse sand||10-2 - 1||3x10-4 - 3x10-2|
|Fine sand||10-3 - 10-1||3x10-5 - 3x10-3|
|Silty sand||10-3 - 10-2||3x10-5 - 3x10-4|
|Clayey sand||10-4 - 10-2||3x10-6 - 3x10-4|
|Silt||10-8 - 10-3||3x10-10 - 3x10-5|
|Clay||10-10 - 10-6||3x10-12 - 3x10-8|
|Material||Hydraulic Conductivity k (m/s)*|
|Uniformly graded coarse aggregate||0.4 - 4x10-3|
|Well-graded aggregate without fines||4x10-3 - 4x10-5|
|Concrete sand, low dust content||7x10-4 - 7x10-6|
|Concrete sand, high dust content||7x10-6 - 7x10-8|
|Silty and clayey sands||10-7 - 10-9|
|Compacted silt||7x10-8 - 7x10-10|
|Compacted clay||< 10-9|
|Bituminous concrete**||4x10-5 - 4x10-8|
|Portland cement concrete||< 10-10|
- *1 m/s = 3.25 ft/s
- **New pavements; values as low as 10-10 have been reported for sealed, traffic-compacted highway pavements.
Thermal conductivity K is defined as the ability of a material to conduct heat. Typical units are BTU/ft-hr-°F or W/m-°K. Thermal conductivity is used in the EICM algorithms for the computation of temperature distributions with depth and time in the NCHRP 1-37A analysis methodology.
Table 5-58 outlines the NCHRP 1-37A recommended approach for characterizing the dry thermal conductivity K for unbound materials. Note that thermal conductivity is not commonly measured for unbound pavement materials, and consequently the level 3 inputs will be used for nearly all designs. The EICM automatically adjusts the dry thermal conductivity for the influence of moisture during the calculations.
Heat capacity Q is defined as the amount of heat required to raise by one degree the temperature of a unit mass of soil. Typical units are BTU/lb-°F or J/kg-°K. Heat capacity is used in the EICM algorithms for the computation of temperature distributions with depth and time in the NCHRP 1-37A analysis methodology.
Table 5-58 outlines the NCHRP 1-37A recommended approached for characterizing the dry heat capacity Q for unbound materials. Note that heat capacity is not commonly measured for unbound pavement materials, and consequently the level 3 inputs will be used for nearly all designs. The EICM automatically adjusts the dry heat capacity for the influence of moisture content during the calculations.
|Material Property||Input Level||Description|
|Dry Thermal Conductivity, K||1||Direct measurement (ASTM E 1952).|
Additional typical values are given in Table 5-59.
|Dry Heat Capacity, Q||1||Direct measurement (ASTM D 2766).|
|3||Typical values range from 0.17 to 0.20 BTU/lb-°F. Additional typical values are given in Table 5-59.|
- *1 BTU/ft-hr-°F = 1.73 W/m-°K; 1 BTU/lb-°F = 4187 J/kg-°K
|Soil Type||Thermal Conductivity|
|Clay with high clay content||0.85 - 1.1||1700 - 2050|
|Silty clay/silt||1.1 - 1.5||1650 - 1900|
|Silt||1.2 - 2.4||1400 - 1900|
|Sand, gravel below GWT||1.5 - 2.6|
(1.6 - 2.0)
|1450 - 1850|
|Sand, gravel above GWT||0.4 - 1.1|
(0.7 - 0.9)
|700 - 1000|
|Till below GWT||1.5 - 2.5||1350 - 1700|
|Sandy till above GWT||0.6 - 1.8||750 - 1100|
|Peat below GWT||0.6||2300|
|Peat above GWT||0.2 - 0.5||400 - 1850|
- *1 W/m-°K = 0.578 BTU/ft-hr-°F; 1 J/kg-°K = 2.388E-4 BTU/lb-°F
5.6 Environment/Climate Inputs
5.6.1 1993 AASHTO Guide
There are only four environmental inputs in the 1993 AASHTO Guide:
- Estimated seasonal variation of the subgrade resilient modulus MR (Section 5.4.3)
- The category for the percentage of time that the unbound pavement materials are exposed to moisture conditions near saturation (Section 5.5.1)
- The qualitative description of moisture supply for expansive subgrades (Section 5.5.1)
- The depth of frost penetration (Section 5.5.1)
These environmental factors are intertwined with their associated material property inputs and have already been described in this chapter in the sections noted above.
5.6.2 NCHRP 1-37A Design Guide
Three sets of environmental inputs are required in the NCHRP 1-37A design methodology:
- Climate, defined in terms of histories of key weather parameters
- Groundwater depth
- Surface shortwave absorptivity
These parameters are the inputs/boundary conditions for the calculation of climate-specific temperature and moisture distributions with depth and time in the EICM (see Appendix D). These distributions, in turn, are used to determine seasonal moisture contents and freeze-thaw cycles for the unbound pavement materials.
The seasonal damage and distress accumulation algorithms in the NCHRP 1-37A design methodology require hourly history data for five weather parameters:
- Air temperature
- Wind speed
- Percentage sunshine (used to define cloud cover)
- Relative humidity.
The NCHRP 1-37A Design Guide recommends that the weather inputs be obtained from weather stations located near the project site. At least 24 months of actual weather station data are required for the computations. The Design Guide software includes a database of appropriate weather histories from nearly 800 weather stations throughout the United States. This database is accessed by specifying the latitude, longitude, and elevation of the project site. The Design Guide software locates the six closest weather stations to the site; the user selects a subset of these to create a virtual project weather station via interpolation of the climatic data from the selected physical weather stations.
Specification of the weather inputs is identical at all the three hierarchical input levels in the NCHRP 1-37A Design Guide.
The groundwater table depth is intended to be the best estimate of the annual average depth. Level 1 inputs are based on soil borings, while level 3 inputs are simple estimates of the annual or seasonal average values. A potential source for level 3 groundwater depth estimates is the county soil reports produced by the National Resources Conservation Service. There is no level 2 approach for this design input.
It is important to recognize that groundwater depth can play a significant role in the overall accuracy of the foundation/pavement moisture contents and, hence, the seasonal modulus values. This is explored further in Chapter 6. Every attempt should be made to characterize groundwater depth as accurately as possible.
Surface Shortwave Absorptivity
This last environmental input is a property of the AC or PCC surface layer. The dimensionless surface short wave absorptivity defines the fraction of available solar energy that is absorbed by the pavement surface. It depends on the composition, color, and texture of the surface layer. Generally speaking, lighter and more reflective surfaces tend to have lower short wave absorptivity.
The NCHRP 1-37A recommendations for estimating surface shortwave absorptivity at each hierarchical input level are as follows:
- Level 1-Determined via laboratory testing. However, although laboratory procedures exist for measuring shortwave absorptivity, there currently are no AASHTO protocols for this for paving materials.
- Level 2-Not applicable.
- Level 3-Default values as follows:
- Weathered asphalt (gray) 0.80 - 0.90
- Fresh asphalt (black) 0.90 - 0.98
- Aged PCC layer 0.70 - 0.90
Given the lack of suitable laboratory testing standards, level 3 values will typically be used for this design input.
5.7 Development Of Design Inputs
Myth has it that an unknown structural engineer offered the following definition of his profession (Coduto, 2001):
"Structural engineering is the art and science of molding materials we do not fully understand into shapes we cannot precisely analyze to resist forces we cannot accurately predict, all in such a way that the society at large is given no reason to suspect the extent of our ignorance."
This definition applies even more emphatically to pavement engineering. In spite of our many technical advances, there are still great gaps in our understanding. Often the greatest uncertainties in an individual project are with site conditions and materials-the types and conditions of materials encountered along the highway alignment, their spatial, temporal, and inherent variability, and their complex behavior under repeated traffic loading and environmental cycles.
Site investigation and testing programs often generate large amounts of data that can be difficult to synthesize. Real soil profiles are nearly always very complex, so borings often do not correlate and results from different tests may differ enormously. The development of a simplified representation of the soils and geotechnical conditions at a project site requires much interpolation and extrapolation of data, combined with sound engineering judgment. But what is engineering judgment? Ralph Peck suggested several alternative definitions (Dunnicliff and Deere, 1984):
"To the engineering student, judgment often appears to be an ingredient said to be necessary for the solution of engineering problems, but one that the student can acquire only later in his career by some undefined process of absorption from his experience and his colleagues.
"To the engineering scientist, engineering judgment may appear to be a crutch used by practicing engineers as a poor substitute for sophisticated analytical procedures.
"To the practicing engineer, engineering judgment may too often be an impressive name for guessing rather than for rational thinking."
Perhaps Webster's New Collegiate Dictionary offers the definitive statement:
Judgment: The operation of the mind, involving comparison and discrimination, by which knowledge of values and relations is mentally formulated.
But when confronted with voluminous quantities of inconsistent-and often contradictory-information, how does the pavement engineer compare and discriminate? What tools (or tricks) of the trade are available? This is a difficult process to describe. However, some common techniques for determining design values from site exploration and other geotechnical data are as follows:
- Find and remove any obvious outliers in the data. Although there are statistical techniques for doing this (e.g., McCuen, 1993), in practice, detailed knowledge of the data plus engineering reasoning is usually sufficient for removing data outliers for cause. Table 5-60 summarizes some typical ranges of variability for pavement design inputs; additional information on measured variability of geotechnical parameters can be found in Baecher and Christian (2003). However, it is important that outliers (e.g., a single low stiffness value) not be arbitrarily removed without fully evaluating the data for an explanation. A local anomaly may exist in the field, for example, that requires remediation.
- Examine spatial (and in some cases, temporal) trends in the data. Look at both the subsurface stratagraphic profiles and plan view "map" of subsurface conditions. Refer to the 1993 AASHTO Design Guide for resolving spatial variations in pavement design data by defining homogeneous analysis units based on a "cumulative difference" approach (Figure 5-40). A separate set of design inputs can then be developed for each homogeneous analysis unit, reducing the variability of measured vs. design input values within each unit.
- Check whether the magnitudes and trends in the data pass the test of "engineering reasonableness" - e.g., are the values of the right order of magnitude? Are the trends in the data in the intuitively correct directions?
- Examine the internal consistency of the data - e.g., are the phase relationships by volume consistent with the phase relationships by weight?
- Use correlations among different types of data to strengthen data interpretation - e.g., statistical correlations between resilient modulus and CBR can be used to supplement a limited set of measured MR values (although today, laboratory resilient modulus tests can often be performed more quickly and less expensively than laboratory CBR tests-see Table 5-61).
- Be clear on what is needed for a design value. The value of a material property used for specification purposes may be different from the value of that same material property when used for design. For example, a conservative value (mean plus one or two standard deviations) may be specified for the minimum compressive strength of a lime stabilized subgrade for construction quality control specifications; the mean value would be more appropriate for design applications where overall reliability (e.g., factor of safety) is considered explicitly, as is the case in both the AASHTO and NCHRP design procedures.
- Evaluate the sensitivity of the design to the inputs! This is perhaps the most important-and often the most overlooked-aspect of design. Evaluating sensitivity to design inputs can have several benefits. First, it will categorize which inputs are most important and which are less important to the design. There is no need to expend large effort determining the precise design values for inputs that have little impact on the final outcome. More resources can then be allocated to determining the inputs that have significant impact on the outcome once they have been identified. Second, design sensitivity analyses can indicate the potential consequences of incorrect judgments of the design inputs. For example, if the subgrade resilient modulus is underestimated by 50%, will this reduce the expected useful life of the pavement by 1 year or 10 years? How does the increased cost of reduced pavement life compare with the cost of additional exploration in order to establish the subgrade resilient modulus value more robustly?
- When in doubt run more tests (a single test is often worth a thousand guesses).
Figure 5-40. Variation of pavement response variable versus distance for given project (NCHRP 1-37A, 2004).
|Portland cement concrete||0.1||0.3||0.5|
|Cement treated base||0.5||0.6||0.7|
|Subgrade (4 - 7)||0.5||1.0||2.0|
|Subgrade (7 - 13)||1.0||1.5||2.5|
|Subgrade (13 - 20)||2.5||4.0||6.0|
|Granular subbase (20 - 30)||5.0||8.0||12.0|
|Granular base (80+)||10.0||15.0||30.0|
|Percent compaction (%)|
|Portland cement concrete properties|
|Air content (%)||0.6||1.0||1.5|
|28-day compressive strength (psi)||400||600||800|
|Asphalt concrete properties|
|3/4 or 1/2 inch||1.5||3.0||4.5|
|No. 40 or No. 50||1.3||1.5||1.7|
|Asphalt content (%)||0.1||0.25||0.4|
|Percent compaction (%)||0.75||1.0||1.5|
|Marshall mix properties|
|Air voids (%)||0.8||1.0||1.4|
|Pen @ 77°F||2||10||18|
|Viscosity @ 149°F (kilopoise)||2||25||100|
|Coefficient of Variation (%)|
|Sample size required||60 lbs (27 kg)||5 lbs (2.3 kg)|
|Turnaround time||10 days||4 days|
|In-situ testing||Field test||Shelby tube - lab|
Depending upon the number of groups in the class, one or more of the following exercises may be assigned.
5.8.1 1993 AASHTO Design Guide-Flexible Pavements
Small group exercise: Given the pavement information for the Main Highway in Appendix B, estimate appropriate material property inputs for the unbound materials in a flexible pavement structure as required for the 1993 AASHTO Design Guide. (A worksheet will be distributed to guide this exercise.)
5.8.2 1993 AASHTO Design Guide-Rigid Pavements
Small group exercise: Given the pavement information for the Main Highway in Appendix B, estimate appropriate material property inputs for the unbound materials in a rigid pavement structure as required by the 1993 AASHTO Design Guide. (A worksheet will be distributed to guide this exercise.)
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- Moisture and freeze/thaw are also important factors behind stripping of asphalt concrete, but this material phenomenon is beyond our scope. Return to Text
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