methodologies for incorporating traffic speed

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METHODOLOGIES FOR INCORPORATING TRAFFIC SPEED DEFLECTION DEVICES BASED FLEXIBLE PAVEMENT STRUCTURAL EVALUATION WITHIN A PAVEMENT MANAGEMENT APPLICATION Senthilmurugan Thyagarajan (corresponding author) Highway Research Engineer, ESC Inc / FHWA Turner Fairbank Highway Research Center 6300 Georgetown Pike, F210 Mclean, VA, USA 22101 Phone: 1-202-493-3157 Email: [email protected] Nadarajah Sivaneswaran Senior Research Civil Engineer FHWA, Turner-Fairbank Highway Research Center 6300 Georgetown Pike, HRDI-20 McLean, VA, USA 22101 Phone: 1-202-493-3147 / Fax: 1-202-493-3161 E-mail: [email protected]

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Katherine Petros Team Leader, Infrastructure Analysis and Construction Team FHWA, Turner-Fairbank Highway Research Center 6300 Georgetown Pike, HRDI-20 McLean, VA 22101 Phone: 1-202-493-3154 / Fax: 1-202-493-3161 E-mail: [email protected]

Word count: 3,904 words + 10 tables/figures x 250 words (each) = 6,404 words

Submission Date: August 1, 2014

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Abstract Current State highway agency's Pavement Management Systems are primarily based on surface condition data and surface cracking is mainly used as an indicator of pavement structural condition. However, with effective pavement preservation treatment that intervene early to preserve and extend the life of pavements and increasingly thicker long-life pavements, the surface cracks no longer tell the true structural condition, or health, of the pavement structure. In addition, surface cracks are a lacking indicator of pavement deterioration. The true pavement structural condition and rate of deterioration are needed not only to plan optimal structural rehabilitation activities and future budget needs, but also for implementing a performance-based Federal-aid program. This paper presents a methodology for interpreting Traffic Speed Deflection Devices (TSDD) measurements to track flexible pavement structural condition over time and for assessing future rehabilitation needs at network level to address structural adequacy in addition to surface condition. The study also demonstrates the methodology to interpret TSDD measurements to estimate pavement remaining structural capacity. Curvature indices measured from TSDD was found to be reasonable estimator of pavement structural condition and was used in the demonstration. Horizontal tensile strain, a primary initiator of fatigue damage and cracking, can be estimated from periodic TSDD measurements and used as a leading indicator of pavement deterioration and structural performance. Any differences in pavement structural performance arising from as-designed versus as-constructed and assumed versus actual traffic and climate effect can be assessed and future treatments can be modified as necessary.

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Keywords: Traffic speed deflection device, pavement management system, remaining structural capacity, pavement performance, deflection, curvature index.

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Introduction Current State highway agency's (SHA's) Pavement Management Systems (PMS) are primarily based on surface condition data and surface cracking is mainly used as an indicator of the pavement structural condition. However, with effective pavement preservation actions that intervene early to preserve and extend the life of pavements and increasingly thicker long-life pavements, the surface cracks can no longer be relied on as a reliable indicator of structural condition or “health” of the pavement structure. This is because most preservation treatments correct surface cracks but do not correct bottom-up fatigue cracking, instead concealing them, while the bottom-initiated cracks continue to develop. In addition, the prevalence of top-down cracking in thicker pavements also makes it difficult to distinguish bottom-up fatigue cracking which is the common indicator of structural deterioration. The true pavement structural condition and rate of deterioration are needed not only to plan optimal structural rehabilitation activities and future budget needs but also for adequately assessing progress under a performance based Federal-Aid program. Recognizing this need, SHAs have investigated the use of Falling Weight Deflectometer (FWD). While FWDs are the preferred device for project level structural evaluation, they are inefficient at the network level. FWD measurements are made at discrete points along the pavement sections and the equipment should remain stationary on the road during each testing point (typically 1-4 minutes, depending on the protocol). Since the equipment has to be stationary during measurements, this requires lane closures that disrupt traffic and traffic control. This limits the productivity and the number of discrete points where measurements can be obtained. Traffic speed deflection devices (TSDD) were developed as a practical alternative to FWD for network level pavement structural evaluation. These devices can continuously measure pavement deflection due to the dual tire dynamic axle load at traffic speed. TRB’s second Strategic Highway Research Program (SHRP 2) R06(F) project (1) reviewed all such devices and concluded that, for network level applications, there are two potential devices currently on the market – the Greenwood Engineering’s Traffic Speed Deflectometer (TSD) and the Applied Research Associate’s Rolling Wheel Deflectometer (RWD). RWD is based on spatial coincidence principle and is currently able to measure deflection at up to two locations. TSD utilizes Doppler lasers to measure pavement deflection velocity, currently at up to nine locations, which is divided by vehicle speed to calculate the slope of the deflection basin. The slope can be integrated to compute deflection. More details on the equipment can be found in the SHRP 2 R06(F) final report. Several studies have discussed the application of TSDD in identifying weak sections (2), (3) that can then be further investigated for project level decision making. Often they were limited to the use of center deflection and the relative magnitude along the tested sections is used to delineate structurally sound and weak sections. The limitation in exclusive reliance on center deflection is that it represents the pavement system as a whole, with 60-80% of its magnitude coming from subgrade and base layers (4), and is not robust enough in differentiating a section with deteriorated pavement layer from that of a sound pavement layer over somewhat weaker subgrade. For brevity, the pavement layer is used to refer to the asphalt bound pavement layer above unbound base layer. This paper presents a robust methodology that utilizes TSDD measurements in estimating remaining structural capacity and tracking the pavement structural performance for assessing structural adequacy and future rehab needs.

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ESTIMATION OF REMAINING STRUCTURAL CAPACITY Though extensive literature and guidelines are available for the use of FWD deflection and deflection indices in pavement structural evaluation, efforts on the development of methods for the use of TSDD data for pavement structural evaluation have been limited and have exclusively focused on the use of center deflection for such methods. Design charts (Figure 1) have been developed by Transport and Road Research Laboratory (TRRL) (5) to estimate the pavement remaining life for a given deflection at a standard temperature of 200C. The relationship was based on systematic measurements over a 20-year period on more than three hundred sections of pavements from the many full-scale road experiments built by TRRL. Deflection was measured by deflection beam or deflectograph under its loaded dual wheel moving at creep speed. Different charts were developed for pavements with granular, cement-bound and bituminousbound base materials. Temperature correction charts were also developed for measurements made at different climatic conditions. Design charts (Figure 2) were also developed to estimate overlay thickness required to achieve a desired extension of the life, expressed in standard axles. Vavrik et al. (6) used the center deflection measured from RWD as pavement structural condition along with pavement condition index to develop the treatment matrix for Champaign County in Illinois, USA.

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FIGURE 1 Relation between standard deflection and life for pavements (after 5) as used in 8).

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FIGURE 2 Overlay design chart for pavements with granular road base (after 5). In a typical flexible pavement, unbound layers account for about 60 – 80% of the center deflection (4) and change in center deflection due to the seasonal and spatial variation in unbound layer moduli will mask that due to the deterioration of the pavement layer. Thus the utility of TSDD devices are limited when it measures only the center deflection. An earlier study by the authors (7) showed that curvature indices derived from two surface deflections were better measures in capturing the variation in the structural adequacy of the pavement layer at the network level and can effectively isolate the effect of seasonal and spatial variation in unbound layer moduli. South African technical recommendations for highways has developed a design charts (Figure 3) to identify the structural capacity of individual structural layers from curvature indices measured from FWD (8). Carvalho et al. (2012) (9) summarized the indices commonly used in interpreting and evaluating FWD deflection data for network-level pavement management system (PMS) applications. Curvature indices are generally defined as the difference or ratio between the deflections at a fixed lateral distances.

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Note: YMAX- Center Deflection; SCI - Surface Curvature Index= δ0 – δ305; BCI - Base Curvature Index = δ610 - δ915; BDI- Base Damage Index = δ305 - δ610 FIGURE 3 Relation between curvature indices and structural capacity of pavement layer (8). The National Cooperative Highway Research Program (NCHRP) project 10-48 (10) incorporates artificial neural network models and regression equations for estimating layer condition indicators from FWD deflection data. The methodology adopted in this project for assessing layer condition relies on a comparison of the estimated pavement material properties to those expected for pavements in good conditions. Horak (11) found excellent correlations between FWD deflection basin parameters and layer moduli values. The deflection basin was divided in to three zones and the deflections in each zone were used to compute the corresponding moduli values. Base Layer Index (same as Surface Curvature Index) was found to inversely relate to the modulus of the top zone. A previous study by the authors (7) investigated the use of curvature indices in tracking the deterioration of the asphalt concrete (AC) layer in flexible pavements. A comprehensive database of 15,000 pavement structures were simulated considering uniform distribution for each modulus and thickness ranges as summarized in Table 1. Layered elastic analysis program JULEA (12) was used to compute surface deflection at typical FWD sensor spacing and horizontal tensile strain at bottom of the AC layer (referred as fatigue strain subsequently for

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brevity). A typical dual tire configuration of 9000 lbs (40kN) with 120 psi (827.4kPa) tire pressure and 12 inch (30.5cm) dual tire spacing were used. Surface Curvature Index (SCI) was computed as the difference between deflection at load center and 12 inch (30.5cm) forward of the dual tire, the measurements that can be made with available TSDD. TABLE 1 Pavement Structure Range Used in Database Layer Properties HMA Layer Base Layer Sub-Base Layer 40,000

20,000

5,000

Maximum 1,000,000 80,000 Minimum 3 4 Thickness, inch Maximum 14 12 Note: 1 psi = 0.006895 MPa, 1 inch = 2.54cm

40,000 6 12

20,000 Infinite Infinite

Modulus, psi

7 8 9 10 11 12 13

14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Minimum

100,000

Subgrade

In the current study presented in this paper, Asphalt Institute (AI) (13) fatigue equation shown below is used with the developed database to compute the number of load repetitions to failure (Nf) for each pavement structure. AI fatigue equation was chosen as it represents the common form used, along with calibration, in project level pavement design and analysis programs. 𝟏 𝟑.𝟐𝟐𝟐 𝟏 𝟎.𝟖𝟖𝟖 𝑵𝒇 = 𝟎. 𝟎𝟎𝟎𝟎 ∗ � � � � 𝜺𝒕 𝑬 where, εt = horizontal tensile strain at the bottom of the asphalt layer E = modulus of AC layer, psi

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Two related methodologies for the interpretation of TSDD measurements are suggested in this study 1) Use of curvature indices, such as SCI, computed from TSDD measurements are used along with design charts to estimate remaining structural capacity. 2) Use of SCI to estimate and track fatigue strain of in-service pavement over time and compare against as-designed strain values in the mechanistic analysis to assess performance deviations. USE OF DESIGN CHARTS TO ESTIMATE REMAINING STRUCTURAL CAPACITY Figure 4 (a) show the relation between the SCI and the computed Nf in terms of equivalent single axle load (ESAL) for AC layer in different conditions. The modulus is grouped in the range typically observed in new, in-service, weak and poor asphalt layers. The coefficient of determination (R2) for the relationship in each condition is shown within the parenthesis in the legend. Figure 4 (b) shows the relationship between JULEA computed fatigue strain and Nf computed by the asphalt institute equation.

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ESAL's Capacity, million

1000 100 10 1 0.1 0.01 0

2

4

6

8

10

12

Surface Curvature Index SCI, mils

1 2

AC Modulus, 100-200 ksi (0.72)

AC Modulus, 200-300 ksi (0.9)

AC Modulus, 300-500 ksi (0.95)

AC Modulus, 500-1000 ksi (0.97)

(a) ESAL's Capacity, million

1000 100 10 1 0.1 0.01 0

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200

300

400

500

Horizontal Tensile Strain, 10^-6

3 4 5 6 7 8 9 10 11 12 13 14 15 16

AC Modulus, 100-200 ksi (0.97)

AC Modulus, 200-300 ksi (0.99)

AC Modulus, 300-500 ksi (0.99)

AC Modulus, 500-1000 ksi (0.99)

(b) FIGURE 4 Remaining structural capacity design charts developed from pavement structure database. (a) Relation with TSDD measured curvature index (b) Relation with horizontal fatigue strain. Note: 1mils =0.0254mm, 1ksi = 6.895MPa. USE OF RELATION BETWEEN CURVATURE INDEX-FATIGUE STRAIN IN PERFORMANCE TRACKING Modern mechanistic-empirical (M-E) pavement analysis tools, such as MEPDG (14) and CalME (15), use incremental distress analysis method. These tools divides the design period in to a number of equal time increments and modeled the pavement structure at each increment through models that incorporate the effect of accumulated aging, climatic and traffic loading on an inservice pavement. The tools then compute fatigue strain at the bottom of the AC asphalt layer at each time increment to compute fatigue damage in the pavement structure using Miner’s rule.

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The computed damage is then used along with empirical transfer functions to compute bottomup fatigue cracking as a function of time throughout the design period. FWD deflection measurements have been effectively used to assess pavement structural response (16), (17). It can be construed from the Figure 4 (a) and (b), a strong relationship exists between the SCI and fatigue strain. Thyagarajan et al. (7) conceptually showed that fatigue strain estimated with curvature indices are robust structural performance indicators of in-service pavements and the relationship was validated using advanced modeling and FWD field data. The study also reported that other curvature indices, such as Delta8 and Delta18, also have good correlation with fatigue strain. This study demonstrates the use of TSDD data in tracking the fatigue performance of inservice pavement section. A properly designed pavement will fail in fatigue at the end of design period. Properly chosen treatment applied at appropriate time will extend the pavement service life with lowest cost to the highway agency. The methodology presented in this paper uses the fatigue strain predicted from the TSDD measured deflections to track pavement structural deterioration with time to assist in the selection of appropriate structural rehab action and timing to preserve pavement structural adequacy and extend pavement service life. The pavement structure presented in Table 2 is used in demonstrating the two interpretation methodologies for the use of TSDD measurements. The CalME software is used in this study since it takes in to account the progressive deterioration of the pavement structure over the design period and also can consider sequence of treatments during the design period to preserve and extend pavement service life. The CalME default layer material properties, traffic and climatic conditions were used in the analysis. Figure 5 shows the CalME analysis results for the pavement structure. The Figure 5 (a) shows deterioration in each pavement layer represented by layer modulus over 30 year analysis period. AC layer shows an initial increase in modulus (legend E1) due to aging followed by gradually decreasing modulus due to progressive deterioration of the asphalt layer from traffic and environmental loading. Layer modulus values are shown for the same month each year over the analysis period. The predicted rutting and bottom-up fatigue cracking are shown in the Figure 5 (b). CalME deterministic analysis does not compute international roughness index (IRI) of the pavement structure. Therefore, the same pavement section was used in MEPDG to compute IRI shown in Figure 6. The performance threshold for fatigue cracking, total rutting and IRI are defined at 0.15 ft/sq.ft (0.5m/sq.m), 0.4 inch (1cm) and 170 inch/mile (2.7m/km), respectively. The corresponding performance period computed are 17, 23 and 20 year respectively.

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TABLE 2 Pavement Section Used in CalME Thickness, Layer No Material inch HMA Type A 3/4" PG 641 8 22 AAC AAA 2 AB-Class 2 10 3 CL Note: 1psi = 0.006895MPa, 1inch = 2.54cm

Modulus, psi 612,800 43,500 10,200

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Layer Modulus, ksi

1,000 100 10 1 0 1 2

3 4 5 6 7

10 HMA

20 Life, Year Base

30

40

Subgrade

(a)

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(b) FIGURE 5 CalME analysis. (a) Predicted pavement layer structural deterioration (b) Computed distress. Note: 1ksi = 6.895MPa, 1inch =25.4mm, 1ft/sq.ft=3.28m/sq.m. 200

IRI, Inch/mile

160 120 80 40 0

8 9 10 11 12 13 14

0

5

10 15 Life, Year

20

25

FIGURE 6 Predicted IRI from MEPDG. Note 1inch/mile=63.36m/km. Asphalt layer modulus computed by CalME over 16 years of pavement service life before fatigue cracking reaches the defined threshold value is shown in Figure 7. This is also shown earlier in the CalME output screen capture in Figure 5. Figure 7 also shows the cumulative traffic loading in ESAL’s as computed by CalME. CalME computed layer moduli values were

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used along with JULEA to compute SCI and fatigue strain as a function of time as shown in Figure 8. The design chart presented in Figure 4(a) can be used to compute the remaining structural capacity in terms of ESAL’s for the measured SCI values. For example, after initial aging at year 2, the SCI is 1.5 mils (0.038mm) (Figure 8), AC layer modulus is in the range of 500-1000 ksi (3,447-6,894MPa) (Figure 7) and when used in Figure 4 (a) design chart, the corresponding ESAL’s is 21 million. At year 16, the SCI is 2.6 mils (0.0635mm) (Figure 8), AC layer modulus is in the range of 300-500 ksi (2,068-3,447MPa) (Figure 7) and when used in Figure 4 (a) design chart the, corresponding ESAL’s is 8 million. Thus from the design chart the pavement has been subjected to 13 million (=21-8) ESAL’s loading in 14 (=16-2) years. From CalME analysis, for the same period, it can be seen that the pavement has carried 14 million ESAL’s (17 and 3 million at year 16 and 2, respectively) as shown in Figure 7. Figure 8 also contains the fatigue strain predicted by the relationship with SCI developed in the earlier study (7) that shows a similar trend. The inset in the Figure 8 also shows the onset of fatigue cracking that appears on the surface only at the 11th year, while the fatigue strain can construe the continuous deterioration in the AC layer. Thus it is evident that deflection indices derived from TSDD measurements are effective leading indicators of in-service pavements structural performance and are reasonably well related to fatigue strain.

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Asphalt Layer Modulus, ksi

800

20 18

700

16

600

14

500

12 10

400

8

300

Asphalt Concrete Modulus, ksi

200

Cumulative ESAL's

100

4 2 0

0 0

19 20 21

6

Traffic Capacity, million ESAL

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

5

10

15

20

Design Life, year

FIGURE 7 Asphalt layer deterioration and cumulative ESAL’s over service period. Note: 1ksi = 6.895MPa.

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3.0 2.5 2.0 1.5 1.0 SCI_JULEA Fatigue Strain_JULEA Fatigue Strain _SCI relationship

0.5 0.0

0

5

10

15

20

200 180 160 140 120 100 80 60 40 20 0

Tensile Strain, microns

Surface Curvature Index SCI, mils

Thyagarajan, Sivaneswaran and Petros

Design Life, year

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FIGURE 8 Illustration of TSDD measurement in tracking fatigue strain values. Note: 1mils =0.0254mm, 1ft/sq.ft = 3.28m/sq.m. The magnitude and change over time of the TSDD measurements are small hence they must be both accurate and precise for the methodologies presented in this paper for their interpretation to be robust and reliable for network level pavement structural assessment and performance tracking. Accurate and precise TSDD measurements are essential to estimate pavement response, such as fatigue strain, and to reliably track them over time. The study presented in this paper was conducted in planning for a Federal Highway Administration (FHWA) research project to assess, field evaluate and validate on instrumented and other test pavement sections the capability of the RWD and TSD devices for pavement structural evaluation at the network level and for developing analysis methodologies for enabling their use in pavement management applications. Field evaluation and validation, which included evaluation of measurement accuracy and precision for a range of pavement structures and testing conditions, have been completed. The project will also develop operational guidelines and limitations of TSDD. It is envisioned that once the accuracy of fatigue strain derived from TSDD measurements is validated with field measurements through the FHWA research effort, that fatigue strain prediction can form the basis of fatigue performance and for determining future structural rehabilitation needs and timing. For a newly designed pavement, fatigue strain prediction would be from the M-E tool used for the initial design and this will be adaptively adjusted as actual fatigue strain values from periodic TSDD measurements become available, turning over the fatigue prediction from as-designed to as-built and performing.

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CONCLUSION Pavement structural condition and rate of deterioration are needed to plan optimal structural rehabilitation activities and future budget needs. Most PMS use surface cracking as a surrogate for pavement structural condition, however, with effecting pavement preservation practice, this is no longer a reliable indicator of true pavement structural condition. This study demonstrates the use of information derived from TSDD measurements for improved pavement structural evaluation at the network level for pavement management applications. It is shown that the TSDD measurements can be used to reasonably assess remaining structural capacity of the pavements and also to track pavement fatigue performance. Curvature indices computed with TSDD measurement can be used with asphalt layer condition in design charts to estimate remaining structural capacity. Alternatively, curvature indices can be related to fatigue strain to track and compare against as-designed fatigue performance over the pavement service period to assess deviation and to adjust predicted performance. The TSDD measurements are highly sensitive to climatic conditions at the time of testing and methods to adjust them to a “standard” or “reference” condition is critical for use in pavement management.

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REFERENCES 1) Flintsch, G., B. Ferne, B. Diefenderfer, S. Katicha, J. Bryce, S. Nell, and T. Clack. Assessment of Continuous Pavement Deflection Measuring Technologies. SHRP 2 Report R06(F), Strategic Highway Research Program, June 2013. 2) Rada, G.R. and S. Nazarian. The State‐of‐the‐Technology of Moving Pavement Deflection Testing, Report No. FHWA‐HIF‐11‐013, Office of Pavement Technology, Federal Highway Administration, Washington, D.C., January 2011. 3) Gedafa, D.S., M. Hossain, R. Miller, and T. Van. Estimation of Remaining Service Life of Flexible Pavements from Surface Deflections. Journal of Transportation Engineering, Vol. 136, No. 4, 2010, pp. 342–352. 4) Horak, E. Surface Moduli Determined with the Falling Weight Deflectometer used as Benchmarking Tool. Proceedings of the 26th Southern African Transport Conference, Pretoria, South Africa, 9-12 July 2007, pp 284-293. 5) Kennedy, C. K., and N. W. Lister. Prediction of Pavement Performance and the Design of Overlays. Report LR 833. Transport and Road Research Laboratory, Crowthorne, U.K., 1978. 6) Vavrik, W.R., J. Blue, and D.A. Steele. Rolling Wheel Deflectometer-Based Pavement Management System Success: Champaign County, IL, Paper No. 08-2728 Presented at the 87th Transportation Research Board Annual Meeting, Washington, D.C., 2008. 7) Thyagarajan, S., N. Sivaneswaran, K. Petros, and B. Muhunthan. Development of a Simplified Method for Interpreting Surface Deflections for In-Service Flexible Pavement Evaluation, 8th International Conference on Managing Pavement Assets, Santiago, Chile, November 15-19, 2011. 8) Flexible Pavement Rehabilitation Investigation and Design TRH-12, Technical Recommendations for Highways, Pretoria, South Africa, 1997. 9) Carvalho, R., R. Stubstad, R. Briggs, O. Selezneva, E. Mustafa, and A. Ramachandran. Simplified Techniques for Evaluation and Interpretation of Pavement Deflections for Network-Level Analysis. Publication FHWA-HRT-12-023, Federal Highway Administration, McLean, VA. 2012. 10) Assessing Pavement Layer Condition Using Deflection Data. NCHRP Project 10-48, Final Report, Transportation Research Board, Washington, D.C. 2000. 11) Horak, E. Benchmarking the Structural Condition of Flexible Pavements with Deflection Bowl Parameters. Journal of the South African Institution of Civil Engineering, Vol. 50 No.2, 2008 pp. 2-9. 12) JULEA - Jacob Uzan Layered Elastic Analysis. Developed by Dr. Jacob Uzan, Technion University, Israel (undated). 13) Asphalt Institute. Research and Development of the Asphalt Institute’s Thickness Design Manual. Research Report 82-2, 1982. 14) Guide for Mechanistic Empirical Design Guide of New and Rehabilitated Pavement Structures. NCHRP Project 1-37A. National Research Council, Washington, D.C., 2004. 15) Ullidtz, P., J. Harvey, I. Basheer, D. Jones, R. Wu, J. Lea, and Q. Lu. CalME: A New Mechanistic-Empirical Design Program for Flexible Pavement 10 Rehabilitation. In Transportation Research Record: Journal of the Transportation Research Board, No.

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16)

17)

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2153, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 143-152. Benkelman, A. C., and W. N. Carey Jr. Prediction of Flexible Pavement Performance from Deflection Measurements. In Highway Research Record: Journal of the Highway Research Board, No. 73, Highway Research Board, National Research Council, Washington, D.C., 1962, pp. 224-226. Xu, B., S. R. Ranjithan, and Y. R. Kim. New Relationships between Falling Weight Deflectometer Deflections and Asphalt Pavement Layer Condition Indicators. In Transportation Research Record: Journal of the Transportation Research Board, No. 1806, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 48-56.

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