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1. Gartner Research Best Practices for Shifting Testing Left to Deliver Higher Quality Software Faster. This newsletter includes Gartner’s recommendations for adopting a shift-left approach to testing to support DevOps initiatives, along with a few of QASymphony’s top resources on the subject.
2. The third edition of a bestseller, Software Testing and Continuous Quality Improvement, Third Edition provides a continuous quality framework for the software testing process within traditionally structured and unstructured environments. This framework aids in creating meaningful test cases for systems with evolving requirements.
3. Laboratory Testing of Suction and Deformation Properties of Base Course Aggregates Article in Transportation Research Record Journal of the Transportation Research Board 1787(1):83-89 January.
4. AASHTOWare is a unique and powerful enterprise software suite designed by transportation professionals for transportation professionals. No other software matches its effectiveness for transportation project design and management.

• Both the down stroke and the up stroke are highlighted in different colors.
• Measurement of the keyboard repeat and depress times.
• Display of BIOS keyboard code and Windows scan codes.
• Language independent testing by using BIOS scan codes.
• Allows creation of your own custom keyboard layouts.
• Support for up to 100 keyboards.
• Downloadable keyboard layouts (see the layout download page)
• Testing of compound keys, like a '.COM' or '.WWW' key
• Batch mode testing (with the /b command line parameter)
• Logging of keyboard serial numbers, operator ID and pass / fail results to disk (in batch mode).
• Display options for testing row and column short circuits (in batch mode).
• Ability to test for under or over-responsive keys by specifying a number of required keystrokes (in batch mode)
• Display options for mouse buttons. Both for mice embedded in the keyboard and external mice.
• Support for all connector types, (PS/2, Wireless & USB keyboards).
• A function to flash the three keyboard LED, Num Lock, Caps lock and Scroll lock.
• Measurement of the delay between key presses.

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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

5INTRODUCTIONThis chapter briefly describes the MEPDG (AASHTO 2008), the AASHTO Guide for the Local Calibration of the Mechanistic-Empirical Pavement Design Guide (MEPDG Local Calibration Guide) (AASHTO 2010), and the AASHTO- Ware Pavement ME Design™ software, with the intent to summarize the key points, rather than duplicate the informa-tion provided in existing AASHTO documentation.MECHANISTIC-EMPIRICAL PAVEMENT DESIGN GUIDE, A MANUAL OF PRACTICEMechanistic-based pavement design procedures incorporate factors that directly relate to pavement performance, such as traffic loadings, climatic effects, material properties, and exist-ing soil conditions. Since the late 1950s, pavement design has evolved from empirical-based methods, such as that developed at AASHO Road Test (HRB 1961), to ME-based procedures, as contained within the MEPDG.As described in the summary of survey results, the major-ity of U.S. highway and Canadian provincial and territorial transportation agencies (39) utilize the AASHTO Guide for the Design of Pavement Structures (AASHTO 1993). Although the AASHTO 1993 Guide has served the pavement design commu-nity reasonably well, there are a number of limitations with this Guide’s design procedure (e.g., limited material types, truck configurations that are no longer used, one climate zone) that can be overcome using an ME-based design procedure.Having recognized the need for a nationally developed and calibrated ME pavement design procedure, the AASHTO Joint Technical Committee on Pavements proposed a research effort to develop such a design procedure that would be based on current state-of-the-practice pavement design methods (AASHTO 2008). This proposal lead to the initiation of NCHRP Project 1-37, Development of the 2002 Guide for the Design of New and Rehabilitated Pavement Structures, and subsequently, NCHRP Project 1-37A, Guide for the Design of New and Rehabilitated Pavement Structures, and NCHRP Project 1-40, Facilitating the Implementation of the Guide for the Design of New and Rehabilitated Pavement Structures. The products of these projects included an ME pavement design guide, rudimentary software, and a performance pre-diction model calibration guide.The MEPDG can be used to analyze a broad range of pave-ment design types, materials, traffic loadings, and climate regions. A summary of MEPDG features includes:• Traffic. Truck traffic is characterized according to the distribution of axle loads for a specific axle type (i.e., axle-load spectra), hourly and monthly distribution fac-tors, and distribution of truck classifications (i.e., the number of truck applications by FHWA vehicle class). Truck traffic classification groups have been developed to provide default values for normalized axle-load spectra and truck volume distribution by functional classifica-tion. The MEPDG also provides the ability to analyze special axle configurations.• Materials. Materials property characterization includes asphalt, concrete, cementitious and unbound granular materials, and subgrade soils. Laboratory and field testing are in accordance with AASHTO and ASTM test proto-cols and standards. The key layer property for all pave-ment layers is modulus (dynamic modulus for asphalt layers, elastic modulus for all concrete and chemi-cally stabilized layers, and resilient modulus for unbound layers and subgrade soils).• Climate. Consideration of climate effects on material properties using the Integrated Climatic Model. This is used to model the effects of temperature, moisture, wind speed, cloud cover, and relative humidity in each pave-ment layer. These effects, for example, include aging in asphalt layers, curling and warping in concrete pave-ments, and moisture susceptibility of unbound materials and subgrade soils.• Performance prediction. The MEPDG includes transfer functions and regression equations to predict pavement distress and smoothness, characterized by the Inter-national Roughness Index (IRI).Another integral aspect of the MEPDG is the incor-poration of input hierarchical levels. Although the analysis method is independent of the input level (i.e., regardless of the input level, the same analysis is conducted), the idea of including a hierarchical level for inputs is based on the concept that not all agencies will have detailed input data or that every pavement needs to be designed with a high level of input accuracy. For example, an agency would not necessarily use the same level of inputs for pavements on farm-to-market roads as they would for an urban interstate. chapter twoMECHANISTIC-EMPIRICAL PAVEMENT DESIGN GUIDE AND AASHTOWare PAVEMENT ME DESIGN™ SOFTWARE OVERVIEW

6 The inputs levels included in the MEPDG are as follows (AASHTO 2008):Level 1. Inputs are based on measured parameters (e.g., laboratory testing of materials, deflection testing) and site-specific traffic information. This level represents the greatest input parameter knowledge, but requires the highest investment of time, resources, and cost to obtain.Level 2. Inputs are calculated from other site-specific data or parameters using correlation or regression equa-tions. This level may also represent measured regional (non-site-specific) values.Level 3. Inputs are based on expert opinion, and global or regional averages.The MEPDG recommends that the pavement designer use as high a level of input as available. Selecting the same hierar-chical level for all inputs, however, is not required (AASHTO 2008). Each agency is expected to determine the input level related to roadway importance, and data collection effort costs and time.The MEPDG (AASHTO 2008) provides recommended input levels for site conditions and factors (Chapter 9), rehabilitation design (Chapter 10), and material properties (Chapter 11).National calibration of the pavement prediction models used in the MEPDG are based on the data included as part of the Long-Term Pavement Performance (LTPP) research program, and recent research studies from the Minnesota pavement test track (MnROAD) and the FHWA accelerated loading facility. Table 1 provides a list of pavement types that are included in the MEPDG.At this time, a number of pavement, treatment, and material types had not been incorporated in the MEPDG (AASHTO 2008) or the performance prediction models had not been nationally calibrated for use in the MEPDG and AASHTOWare Pavement ME Design™ software (AASHTO 2008). For example, these include:• Performance prediction models for asphalt-treated per-meable base under asphalt pavements have not been nationally calibrated.• Semi-rigid pavements cannot currently be modeled using the MEPDG and AASHTOWare Pavement ME Design™ software.• Pavement preservation treatments (e.g., seal coats, micro surfacing, thin asphalt overlays, hot in-place recycling, cold in-place recycling), except for mill and asphalt overlay, are not accounted for in the MEPDG and AASHTOWare Pavement ME Design™ software. However, pavement preservation and maintenance may be accounted for indirectly during the local calibration process (AASHTO 2010).• Jointed reinforced concrete pavements cannot be mod-eled using the MEPDG and AASHTOWare Pavement ME Design™ software.Performance prediction models included in the MEPDG are provided in Table 2.Since the release of the NCHRP 1-37A final report in 2004, a number of additional study efforts have been completed or are currently on-going to improve the MEPDG performance model prediction. These include:• Reflective cracking model—NCHRP Report 669: Mod-els for Predicting Reflection Cracking of Hot-Mix Asphalt Overlays (Lytton et al. 2010).• Rutting models—NCHRP Report 719: Calibration of Rutting Models for Structural and Mix Design (Von Quintus et al. 2012).Asphalt Pavements Concrete Pavements Conventional 2 to 6 in. asphalt layer over unbound aggregate and soil-aggregate layers. Deep strength thick asphalt layer(s) over an aggregate layer. Full-depth asphalt layer(s) over stabilized layer or embankment and foundation soil. Semi-rigid asphalt layer(s) over cementitious stabilized materials. Cold in-place recycle (CIR) designed as a new flexible pavement. Hot in-place recycle (HIR) designed as mill and fill with asphalt overlay. Asphalt overlays (>2 in.) over existing asphalt and intact concrete pavements, with or without pre-overlay repairs, and milling. JPCP with or without dowel bars, over unbound aggregate, and/or stabilized layers. CRCP over unbound aggregate, and/or stabilized layers. JPCP overlays (>6 in.) over existing concrete, composite, or asphalt pavements (minimum thickness of 6 in. and 10 ft or greater joint spacing). CRCP overlays (>7 in.) over existing concrete, composite, or asphalt pavements (minimum thickness of 7 in.). JPCP restoration diamond grinding, and a variety of pavement restoration treatments. Source: AASHTO (2008). JPCP = jointed plain concrete pavements; CRCP = continuously reinforced concrete pavements. TABLE 1PAVEMENT TYPES INCLUDED IN THE MEPDG

What Is Mepdg Testing

7• Longitudinal cracking model—NCHRP Project 1-52, A Mechanistic-Empirical Model for Top-Down Cracking of Asphalt Pavement Layers [http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=3152].The general approach for conducting a pavement design and analysis is structured according to three major stages, each containing multiple steps; each stage of the MEPDG design process is summarized as follows (AASHTO 2008):• Stage 1—Determine materials, traffic, climate, and exist-ing pavement evaluation (for overlay designs) input val-ues for the trial design.• Stage 2—Select threshold limits and reliability levels for each performance indicator to be evaluated for the trial design. Conduct the analysis on the trial design. If the predicted performance does not meet the criteria at the specified reliability level, the trial design is modi-fied (e.g., thickness, material properties) and re-run until the performance indicator criteria is met (see MEPDG, Tables 14-3 through 14-5).• Stage 3—Evaluate pavement design alternatives. This analysis is conducted outside the MEPDG and may include an engineering analysis and life-cycle cost analy-sis of viable alternatives.GUIDE FOR THE LOCAL CALIBRATION OF THE MECHANISTIC-EMPIRICAL PAVEMENT DESIGN GUIDEThe performance prediction models contained within the MEPDG have been nationally calibrated using the in-service pavement material properties, pavement structure, climate and truck loading conditions, and performance data obtained from the LTPP program. The MEPDG performance prediction mod-els may or may not account for site-specific conditions (e.g., unique traffic loadings, soil conditions, material properties). According to the MEPDG Local Calibration Guide, it is highly recommended that each agency conduct an analysis of the MEPDG results to determine if the nationally calibrated perfor-mance models accurately predict field performance (AASHTO 2010). If not, the performance prediction models used in the MEPDG may require calibration to local conditions. Because of the limited availability of site-specific measured properties (Level 1), the MEPDG performance prediction models are pri-marily based on Level 2 and Level 3 inputs (AASHTO 2010).To aid in local calibration efforts, AASHTO has published the Guide for the Local Calibration of the Mechanistic-Empirical Pavement Design Guide (AASHTO 2010). The Calibration Guide provides the following procedures for calibrating the MEPDG to local conditions:Step 1. Select hierarchical input level. Selection of the hierarchical input level is a policy-based decision that can be influenced by the agency’s field and laboratory testing capabilities, material and construction specifica-tions, and traffic collection procedures and equipment. An agency may choose different hierarchical input lev-els depending on data availability.Step 2. Develop experimental plan and sampling tem-plate. Selection of pavement segments (and replicates, if possible) that represent the agencies standard specifica-tions, construction and design practices, and materials. Selected pavement segments could represent a variety of design types (i.e., new, reconstructed, rehabilitated), pavement types, traffic levels (or facility), and climate. LTPP or other research test sections may also be included in the experimental plan.Step 3. Estimate sample size. Ensure that the proper num-ber of pavement segments is included in the calibration effort so that the results are statistically meaningful. The recommended minimum number of pavement segments includes:• Rut depth and faulting: 20 pavement segments.• Alligator and longitudinal cracking: 30 pavement segments.• Transverse slab cracking: 30 pavement segments.Asphalt Pavements Concrete Pavements Rut depth total, asphalt, unbound aggregate layers, and subgrade (inches). Transverse (thermal) cracking (non-load-related) (feet/mile). Alligator (bottom-up fatigue) cracking (percent lane area). Longitudinal cracking (top-down) (feet/mile). Reflective cracking of asphalt overlays over asphalt, semi-rigid, composite, and concrete pavements (percent lane area). IRI predicted based on other distresses (inches/mile). Transverse cracking (JPCP) (percent slabs). Mean joint faulting (JPCP) (inches). Punchouts (CRCP) (number per mile). IRI predicted based on other distresses (JPCP and CRCP) (inches/mile). Source: AASHTO (2008). JPCP = jointed plain concrete pavements; CRCP = continuously reinforced concrete pavements. TABLE 2PERFORMANCE PREDICTION MODELS INCLUDED IN THE MEPDG

8 • Determine the bias for each performance prediction model (i.e., hypothesis testing). If the null hypothesis is rejected, the performance prediction model should be recalibrated. If the null hypothesis is accepted (i.e., no bias), compare the Se of the local data with the globally calibrated data (see Step 9).Step 8. Eliminate local bias. If the null hypothesis is rejected in Step 7, significant bias exists. Determine the cause of the bias, remove the bias, if possible, and rerun the analysis using the adjusted calibra-tion coefficients. Features to consider in removing bias include traffic conditions, climate, and material characteristics.Step 9. Assess standard error of the estimate. In this step, the Se for the locally calibrated models is compared with the Se of the MEPDG performance prediction models and checked for reasonableness. Reasonable Se values are provided in Table 4.Potential courses of action include:• For errors that are not statistically significantly differ-ent, use the locally calibrated performance prediction model coefficients (go to Step 11).• For errors that are statistically significantly differ-ent and the Se of the locally calibrated performance prediction model is less than the MEPDG perfor-mance prediction model, use the locally calibrated performance prediction model coefficients (go to Step 11).• For errors that are statistically significantly differ-ent and the Se of the locally calibrated performance prediction model is greater than the MEPDG perfor-mance prediction model, the locally calibrated per-formance prediction model should be recalibrated to lower the standard error. Alternatively, the locally calibrated performance prediction model could be accepted knowing it has a higher standard error than the MEPDG performance prediction model.Step 10. Reduce standard error of the estimate. If the standard error cannot be reduced, proceed to Step 11. • Transverse cracking: 26 pavement segments.• Reflection cracking: 26 pavement segments.Step 4. Select roadway segments. Selection of applicable roadway segments, replicate segments, LTPP sites, and research segments to fill the experimental plan devel-oped in Task 2. It is recommended that selected pave-ment segments have at least three condition observations over an 8- to 10-year period.Step 5. Evaluate project and distress data. Verify that the required input data (e.g., material properties, construc-tion history, traffic, measured condition) are available for each selected pavement segment (refer to MEPDG for a detailed list of input requirements). If discrepancies exist between an agency and the LTPP Distress Identi-fication Manual (Miller and Bellinger 2003) data defi-nitions and/or measurement protocols, the agency data may require conversion to meet the MEPDG format. Check pavement segments to ensure they encompass an ample condition range. The MEPDG recommends that the average maximum condition level exceed 50% of the design criteria. For example, if an agency’s rut depth threshold is 0.50 in., the average maximum rut depth of the pavement segments would be at least 0.25 in.Step 6. Conduct field testing and forensic investiga-tion. This step includes conducting field sampling and testing of pavement segments to obtain missing data, if necessary.Step 7. Assess local bias. Plot and compare the measured field performance to the MEPDG predicted performance (at 50% reliability) for each pavement segment. Evalu-ate each performance prediction model in relation to:• Prediction capability—linear regression of the mea-sured and predicted condition values, compute the R-square value. Generally, R-square values above 0.65 are considered to have good prediction capabilities.• Estimate the accuracy—calculate the means of the standard error of the estimate (Se) and compare with the MEPDG performance prediction models (Table 3).TABLE 3SUMMARY OF MODEL STATISTICSPavement Type Performance Prediction Model Model Statistics R-Square Se Number of data points, N New Asphalt Alligator cracking 0.275 5.01 405 Transverse cracking 11: 0.344 2: 0.218 3: 0.057 N/A N/ARut depth 0.58 0.107 334 IRI 0.56 18.9 1,926 New JPCP Transverse cracking 0.85 4.52 1,505 Joint faulting 0.58 0.033 1,239 IRI 0.60 17.1 163 Source: Titus-Glover and Mallela (2009). 1Level of input used in calibration. JPCP = jointed plain concrete pavements; IRI = international roughness index; N/A = not available.

9AASHTOWare PAVEMENT ME DESIGN™The ability to conduct the analysis described in the MEPDG without the aid of a computer program would be extremely time-consuming, if even possible. As previously noted, one of the products of the NCHRP 1-37A project was accompany-ing rudimentary software. There were a number of issues that If the standard error can be reduced, determine if the standard error of each cell of the experimental matrix is dependent on other factors and adjust the local calibra-tion coefficients to reduce the standard error (Table 5).Step 11. Interpretation of the results. Compare the predicted distress (and IRI) with measured distress to verify that acceptable results are being obtained.Pavement Type Performance Prediction Model Se Asphalt-surfaced Alligator cracking (percent lane area) 7 Longitudinal cracking (feet/mile) 600 Transverse cracking (feet/mile) 250 Reflection cracking (feet/mile) 600 Rut depth (inches) 0.10 Concrete-surfaced Transverse cracking—JPCP (percent slabs) 7 Joint faulting JPCP (inches) 0.05 Punchouts CRCP (number per mile) 4 Source: AASHTO (2009). JPCP = jointed plain concrete pavements; CRCP = continuously reinforced concrete pavements. TABLE 4STANDARD ERROR OF THE ESTIMATE VALUESPavement Type Distress Eliminate Bias Reduce Standard Error Asphalt Total rut depth kr1 = 3.35412 r1 = 1 s1 = 1 kr2 = 1.5606 kr3 = 0.4791 r2 = 1 r3 = 1 Alligator cracking kf1 = 0.007566 C2 = 1 kf2 = 3.9492 kf3 = 1.281 C1 = 1 Longitudinal cracking kf1 = 0.007566 C2 = 3.5 kf2 = 3.9492 kf3 = 1.281 C1 = 7 Transverse cracking t3 = 1 kt3 = 1.5 t3 = 1 kt3 = 1.5 IRI C4 = 0.015 (new) C4 = 0.00825 (overlay) C1 = 40 (new) C1 = 40.8 (overlay) C2 = 0.4 (new) C2 = 0.575 (overlay) C3 = 0.008 (new) C3 = 0.0014 (overlay) Semi-Rigid Pavements c1 = 1 C2 = 1 C1 = 1 C2 = 1 C4 = 1,000 JPCP Faulting C1 = 1.0184 C1 = 1.0184 Transverse cracking C1 = 2 C4 = 1 C2 = 1.22 C5 = 1.98 IRI JPCP J4 = 25.24 J1 = 0.8203 CRCP Punchouts C3 = 216.842 C4 = 33.1579 C5 = 0.58947 Punchouts fatigue C1 = 2 C2 = 1.22 Punchouts crack width C6 = 1 C6 = 1 IRI CRCP — C1 = 3.15 C2 = 28.35 Adapted from AASHTO (2010). JPCP = jointed plain concrete pavements; CRCP = continuously reinforced concrete pavements.TABLE 5FACTORS FOR ELIMINATING BIAS AND REDUCING THE STANDARD ERROR

10 cracking, concrete curling). At the time of this report, the current software version (v1.3) included climate data for 1,083 U.S. and Canadian weather stations. In addition, virtual weather stations can be generated from existing weather stations and new weather stations can be added.• Asphalt layer design properties include surface short-wave absorptivity, fatigue endurance limit (if used), and the interface friction. The fatigue cracking endurance limit has not yet been calibrated (AASHTO 2008).• Concrete layer design properties—for JPCP, this infor-mation includes, for example, joint spacing and seal-ant type, dowel diameter and spacing, use of a widened lane and/or tied shoulders, and information related to the erodibility of the underlying layer. For CRCP, design properties include, for example, percent steel, bar diam-eter, and bar placement depth.• Pavement structure—the pavement structure module allows the designer to insert the material types, asphalt mix volumetrics, concrete mix information, mechanical properties, strength properties, thermal properties, and thickness for each layer of the pavement section to be analyzed.• Calibration factors—within the software there are two opportunities to specify the performance prediction model calibration coefficients: (1) program-level and (2) project-specific (AASHTO 2013). The program-level calibration coefficients are the nationally calibrated factors. Unless otherwise noted, the software will utilize the program-level calibration coefficients in the analy-sis. The project-specific calibration coefficients do not change the program-level coefficients and are only used on designer-specified projects. Both the program-level and the project-specific calibration coefficients can be modified by the designer.• Sensitivity—allows the designer to define minimum and maximum values for selected parameters (e.g., air voids, percent binder, layer modulus) to determine the impact on the predicted condition.• Optimization—this feature is used to determine the min-imum layer thickness of a single layer that satisfies the performance criteria. In this mode, the designer inputs the minimum and maximum layer thickness for the layer in question; the software iterates the layer thickness within the specified range while all other inputs remain constant; and the software determines the minimum layer thickness required to meet all performance criteria.• Reports—the input summary, climate summary, design checks, materials properties summary, condition pre-diction summary, and charts can be provided as a PDF file (v9 or above) and Microsoft Excel format (2003 or newer).TRAINING AND WORKSHOPSThe following is a list of currently available training courses and workshops on ME Design, MEPDG, and AASHTOWare Pavement ME Design™ software.required modification before making the software package commercially available. In 2011, AASHTOWare released the first version of DARWin-ME, which was rebranded to AASHTOWare Pavement ME Design™ in 2013. A number of enhancements have been included in the AASHTOWare Pavement ME Design™ software making it a dynamic and effective tool for conducting pavement design evaluations. Enhancements over the rudimentary software include, for example, reduced runtime, an improved graphical user inter-face, and the ability to store input values into a database.The AASHTOWare Pavement ME Design™ software was developed in accordance with the procedures and practices defined in the MEPDG. In that regard, the AASHTOWare Pavement ME Design™ software is comprised of a series of modules that lead the designer through the analysis proce-dure. Because the MEPDG and accompanying software require the designer to consider different levels of various aspects of the pavement layers (e.g., binder type, aggregate structure), the AASHTOWare Pavement ME Design™ software is techni-cally an analysis tool (i.e., the designer must specify the pave-ment structural section to be analyzed). The various modules of the AASHTOWare Pavement ME Design™ software include (AASHTO 2011):• General design inputs, which include information related to the pavement design type (new pavement, overlay, or restoration), pavement type [e.g., asphalt, jointed plain concrete pavements (JPCP), continuously reinforced concrete pavements (CRCP), asphalt over-lay, concrete overlay], design life, and month/year of construction and opening to traffic.• Performance criteria are used in the analysis to deter-mine whether or not the specified pavement section is to be accepted or rejected. The performance criteria are agency-specific (although default values are provided) and therefore should be based on tolerable or accept-able levels of distress and roughness. In this module, the designer specifies both the limiting value for each per-formance prediction model and the level of reliability.• Traffic—traffic data are required to determine the impact of vehicle loadings onto the pavement structure. Required traffic data may be based on weigh-in-motion sites, auto-matic vehicle classification sites, statewide averages, and/or national averages. Needed traffic items include base year truck volume and speed, axle configuration, lat-eral wander, truck wheelbase, vehicle class distribution, growth rate, hourly and monthly truck adjustment factors, axles per truck, and axle-load distribution factors. In addi-tion, the designer can input a traffic capacity value to cap the traffic volume over the design period. National default values are available for the majority of inputs.• Climate—climate data are used in the analysis process to determine the environmental effects on material responses (e.g., impact of temperature on the stiffness of asphalt layers, moisture impacts to unbound materials) and pave-ment performance (e.g., asphalt rutting, asphalt thermal

11include important concrete pavement design details, including subgrade preparation, base selection, drain-age design, thickness design, joint design, and shoul-der characterization. The course explains how to select the proper details to enhance structural per-formance. Emphasis is given to JPCP, although the course includes instruction on jointed reinforced con-crete pavements (JRCP) and CRCP. – NHI 132040 Geotechnical Aspects of Pavements—instructor-led training that includes discussions on geotechnical exploration and characterization of in-place and constructed subgrades; design and construction of subgrades and unbound layers for paved and unpaved roadways, with emphasis on the AASHTO 1993 Guide and the MEPDG. Drainage of bases, subbases, and subgrades and its impact on pro-viding safe, cost-effective, and durable pavements; problematic soils, soil improvement, stabilization, and other detailed geotechnical issues in pavement design and construction; and construction methods, specifications, and quality control and assurance inspection for pavement projects. – NHI 151044 Traffic Monitoring and Pavement Design Programs—web-based training (free) that promotes the interaction and collaboration between traffic moni-toring program staff and pavement program staff. The presentation supports implementation of the MEPDG. FHWA’s Office of Highway Policy Information, in collaboration with the Design Guide Implementation Team, created this presentation to help ensure that pavement data needs are met with the existing traffic monitoring program or adjustments to the program. – NHI 151050 Traffic Monitoring Programs: Guidance and Procedures—instructor-led course that provides guidance on how to manage a successful traffic moni-toring program. The training begins with an over-view of federal traffic monitoring regulations and a presentation of the host state’s traffic monitoring program. Subsequent lessons introduce federal guid-ance, effective practices, and recommended proce-dures for developing a data collection framework for traffic volume, speed, classification, weight, and non-motorized programs. The course also incorporates related traffic monitoring elements of transportation management and operations, traffic data needs and uses, traffic data submittal requirements, and relevant traffic monitoring research. The critical importance of quality data collection is emphasized to support project planning, programming, design, and mainte-nance decisions.• FHWA Design Guide Implementation Team (http:// www.fhwa.dot.gov/pavement/dgit/dgitcast.cfm). – Introductory Design Guide (2004)—webcast includes discussion of asphalt and concrete concepts and implementation activities. – Obtaining Materials Inputs for ME Design (2005)—webcast covers the required material inputs required to a design. – Executive Summary for Mechanistic-Empirical Design (2005)—webcast discusses the benefits and needs for adoption of ME pavement design. – Use of Pavement Management System Data to Cali-brate ME Pavement Design (2006)—webcast covers the various ways that pavement management system data can be used as input to and for calibration of the MEPDG. – Traffic Inputs for ME Pavement Design (2006)—webcast covers traffic inputs required in the MEPDG and how to extract the data using the NCHRP 1-39 TrafLoad software. – Climatic Considerations for Mechanistic-Empirical Pavement Design (2006)—webcast includes descrip-tion of modeling climatic effects on pavement per-formance, reducing climatic effects through materials selection and design, and analyzing current state design methods for climatic effects. – AASHTOWare Pavement ME Design™ webinar series (2013)—a total of ten webinars on software use related to climatic inputs, traffic inputs, material and design inputs, and demonstration of new and rehabilitated pavement designs.• FHWA and AASHTOWare Pavement ME Design™ Webinars (http://www.aashtoware.org/Pavement/Pages/Training.aspx). Each of the following webinars has been pre-recorded and is directed toward the user of the soft-ware. Each webinar is two hours long. – Getting Started with ME-Design – Climate Inputs – Traffic Inputs – Material and Design Inputs for New Pavement Design – Material and Design Inputs for Pavement Rehabili-tation with Asphalt Overlays – Material and Design Inputs for Pavement Rehabilita-tion with Concrete Overlays• National Highway Institute (NHI: http://www.nhi.fhwa. dot.gov/default.aspx). – NHI 131060 Concrete Pavement Design Details and Construction Practices—instructor-led course that provides participants with current guidelines on design and construction details for concrete pavements. Topics

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