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Paper No. 17-00404

Implementation of the Structural Condition Index into the Louisiana Pavement Management System

Based on Rolling Wheel Deflectometer Testing

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Title: Implementation of the Structural Condition Index into the Louisiana Pavement Management System Based on Rolling Wheel Deflectometer Testing

Authors: Omar Elbagalati, Mostafa A. Elseifi, Kevin Gaspard, and Zhongjie Zhang

Transportation Research Board

96th Annual Meeting

January 8 to 12, 2017

Washington, D.C.

Elbagalati, et al. 2

Implementation of the Structural Condition Index into the Louisiana Pavement Management System Based on

Rolling Wheel Deflectometer Testing

Omar Elbagalati Graduate Research Assistant

Department of Civil and Environmental Engineering Louisiana State University

3316s Patrick Taylor Hall, Baton Rouge, LA 70803 e-mail: [email protected]

Mostafa A. Elseifi

Associate Professor Department of Civil and Environmental Engineering

Louisiana State University 3316s Patrick Taylor Hall, Baton Rouge, LA 70803

e-mail: [email protected]

Kevin Gaspard Senior Pavement Research Engineer

Louisiana Transportation Research Center Louisiana State University

4101 Gourrier Ave., Baton Rouge, LA 70808 e-mail: [email protected]

Zhongjie Zhang

Pavement Geotechnical Research Administrator Louisiana Transportation Research Center

Louisiana State University 4101 Gourrier Ave., Baton Rouge, LA 70808

e-mail: [email protected]


ABSTRACT 1

Structural condition data are commonly collected at the project level using Falling 2 Weight Deflectometer (FWD) measurements. However, recent development in 3 continuous deflection devices has offered the potential to characterize pavement 4 structural conditions at the network level. This study introduces a framework for 5 incorporating pavement structural conditions into the Louisiana Pavement Management 6 System (PMS) decision matrix at the network level. The proposed framework aims at 7 filling the gap between network level and project level decisions and eventually, allowing 8 more accurate budget estimation. In this study, Rolling Wheel Deflectometer (RWD) 9 measurements were utilized to evaluate pavement structural conditions in terms of the 10 Structural Condition Index (SCI). Two enhanced decision trees, for collectors and 11 arterials, were developed, such that both functional and structural pavement conditions 12 are considered in the decision-making process. Implementation of the SCI in the 13 decision-making process is demonstrated and is expected to improve the overall 14 performance of the pavement network. Furthermore, the enhanced decision trees are 15 expected to reduce the total maintenance and rehabilitation (M&R) construction costs if 16 applied to relatively high volume roads (e.g., Interstates, Arterials, and Major Collectors). 17

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Keywords: Structural Condition, Pavement Management System, Rolling Wheel 21 Deflectometer, Maintenance and Rehabilitation 22 23

Word Count Text 4,350 Figures 4x250 Tables 8x250 Total 7,350

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INTRODUCTION 1

Pavement conditions can be divided into two components; functional condition and 2 structural condition. Functional condition encompasses ride quality, roughness, surface 3 distresses, and friction. Structural condition describes the pavement ability to carry 4 traffic loads over its intended life (1). Pavement structural condition evaluation is 5 commonly conducted at the project level for design purposes such as calculating overlay 6 thickness. A recent study showed that only 16% of highway agencies are measuring 7 pavement structural capacity at the network level, and those agencies reported that this 8 approach is advantageous and cost-effective (2). On the other hand, lack of consideration 9 of pavement structural capacity at the network level increases the gap between project 10 level and network level decisions and results in imprecise budget estimation (3). Two 11 types of errors would occur because of the lack of consideration of pavement structural 12 conditions: adding structure to a pavement that does not require it (Type I error – False 13 Positive) and not adding structure to a pavement that requires it (Type II – False 14 Negative) (4). 15

Recent developments in continuous deflection measurement devices offer the 16 capability for highway agencies to collect pavement structural capacity data at the 17 network level (5-7). A number of studies have been conducted proposing modifications 18 to PMS such that pavement structural condition is incorporated in the decision-making 19 process (8-11). In Louisiana, a comprehensive testing program has been conducted in 20 District 05 using the Rolling Wheel Deflectometer (RWD). Results of the testing 21 program demonstrated the capability of RWD to distinguish between structurally-22 deficient and structurally-sound pavements in addition to acceptable measurements’ 23 repeatability (12). 24

OBJECTIVE AND SCOPE 25

The objective of this study is to develop a framework to incorporate pavement structural 26 measurements into the Louisiana PMS decision matrix. A model to calculate pavement 27 Structural Number (SN) based on the RWD measurements was utilized to calculate the 28 Structural Condition Index (SCI). Two enhanced decision trees were developed, for 29 arterials and collectors, utilizing the SCI, such that both structural and functional 30 pavement conditions are considered in the decision-making process. The implementation 31 of the proposed framework on selecting maintenance and rehabilitation (M&R) activities 32 is demonstrated. The cost-effectiveness of implementing the SCI into the Louisiana PMS 33 decision-making process was evaluated. 34

BACKGROUND 35

Rolling Wheel Deflectometer 36

The rolling wheel deflectometer was developed by Applied Research Associate Inc. 37 (ARA) in collaboration with the Federal Highway Administration (FHWA) Office of 38 Asset Management. It consists of a 53-ft. long semitrailer applying a standard 80 KN 39 load on the pavement structure by means of a regular dual-tire assembly over the rear 40 single axle (13). The device has the capability to measure pavement surface deflection, 41 under the rear axle, while operating at traffic speed, up to 80 km/hr., causing no delay to 42 the road users. A recent Strategic Highway Research Program 2 (SHRP2) study 43


identified the RWD as one of the most promising continuous deflection measurement 1 devices (7). 2

A Model to Predict Pavement Structural Number 3

A model to predict pavement structural number based on RWD measurements at an 4 interval of 0.16 km (SNRWD0.16) was developed by the authors and is defined as follows 5 (14): 6

7

SNRWD0.16= -14.72+27.55*ACth

D0

0.04695-2.426* ln SD+0.29* ln ADTPLN (1) 8

9 where, 10 ACth = Asphalt concrete layer(s) thickness of the pavement structure (in.); 11 D0 = Avg. RWD deflection measured at an interval of 0.16 km (mils.); 12 SD = Standard deviation of the RWD deflection each 0.16 km (mils.); and 13 ADTPLN= Average Annual Daily Traffic per lane (Vehicle/day). 14 15 The predicted structural number was correlated to extracted core samples and was found 16 to be an accurate indicator of pavement structural conditions (14). In most cases, 17 pavement sections with low SNRWD0.16 were found to suffer from asphalt stripping, 18 material deterioration, and/or debonding distresses. The model was found to be 19 acceptably accurate with an R2 of 0.8 and a root mean square error (RMSE) of 0.8 in the 20 validation phase when compared to the structural number obtained from FWD 21 measurements. More details about the model development have been presented 22 elsewhere (14). 23

The Structural Condition Index 24

The concept of Structural Condition Index (SCI) was introduced by Zhang and co-25 workers (15). The SCI is calculated by dividing the effective structural number (SNeff) 26 based on FWD measurements by the required SN (SNreq) for 10 years based on the 27 following equation: 28

SCI (2) 29

where, 30 SNeff = the existing pavement structural number; and 31 SNreq= the required pavement structural number for 10 years. 32 33 The SCI showed good correlations with both rutting and fatigue remaining service life for 34 asphalt pavements with R2 of 0.98 and 0.92, respectively. Results showed that the SCI 35 was sensitive to pavement deterioration based on sensitivity analysis conducted on Texas 36 Department of Transportation (TxDOT) PMS data for years 2000, 2001, and 2002 and 37 the corresponding deflection data (4). 38

Based on the results of the analysis, the authors recommended to adopt the SCI as 39 a screening tool that could be used at the network level in PMS applications (4). A recent 40 study conducted in Virginia also selected the SCI as the most promising structural 41


capacity indicator to be implemented into the Virginia Department of Transportation 1 (VDOT) decision-making process (5). 2

CURRENT TREATMENT SELECTION PRACTICE IN LOUISIANA 3

Decision Matrix 4

Pavement surface distresses data are collected in Louisiana once every two years using 5 the Automatic Road Analyzer (ARAN) vehicle. Every pavement distress is reported and 6 stored in a database using a scale from 0 to 100, where 100 indicates excellent pavement 7 condition (16). The treatment decision matrix used by the Louisiana Department of 8 Transportation and Development (LADOTD) PMS depends on surface distress indices 9 and the highway functional class (e.g., Interstate, Arterial, and Collector). Table 1 10 presents the thresholds and trigger values, which are currently used by LADOTD for 11 treatment selection (17). 12

Overlay Pavement Design 13

According to the LADOTD office of design, the current overlay design procedure is 14 presented. First, a thin overlay treatment is considered a “functional” overlay with a 15 thickness of 50.8 mm (2 in.) and no design is conducted. Second, if the treatment 16 decision is “medium overlay” or “structural overlay”, the overlay thickness is assumed 17 preliminary to be 89.0 mm (3.5 in.) and 152.4 mm (6.0 in.), respectively, then it is 18 reviewed by the office of design based on the AASHTO 1993 procedure (18). The 19 design life of the overlay is considered 10 years and a 50% loss in pavement structural 20 capacity is assumed as shown in Equations (3) and (4): 21

Overlaythickness SNreq‐SNeff

a1 (3) 22

where, 23 SNreq = required structural number for a design life of 10 years; 24 SNeff = effective structural number assuming 50% loss in structural capacity and 50.8 mm 25 (2 in.) milling as shown in Equation (4); and 26 a1= asphalt layer structural coefficient (assumed 0.44). 27 28

SN ∗ ∗ ∗ ∗ ∗ 2 ∗ a (4) 29

where, 30 a2 and a3 = structural coefficient for the base and the subbase layers, respectively; 31 m2 and m3 = base layer and subbase layer drainage coefficients, respectively; 32 D1, D2, and D3= thickness of the asphalt, base, and subbase layers, respectively. 33 34 The assumption of 50% loss in structural capacity of the existing pavement is another 35 source of the aforementioned Type I and Type II errors, which was addressed through the 36 proposed framework. 37

DATA DESCRIPTION 38

RWD measurements used in this study were obtained from a comprehensive testing 39 program conducted in Louisiana. The testing program was conducted in two phases; in 40 the first phase, the complete asphalt road network (about 1,600 km [1,000 mi.]) in 41


District 05 was tested using the RWD deflection system based on the manufacturer 1 standard testing protocol (19). Researchers also selected 58 sections to be tested using 2 FWD. In the second phase, 16 road-sections (2.4 km each [1.5 mi.]), were selected and 3 used for a detailed evaluation of RWD technology. In addition, a ground penetrating 4 radar (GPR) and coring surveys were conducted to validate the pavement layers’ 5 thicknesses. Testing was conducted on segments with various functional classes as 6 shown in Table 2. 7

RESEARCH METHODOLOGY AND ANALYSIS 8

To develop the proposed framework in order to incorporate pavement structural 9 measurements into the Louisiana PMS decision matrix, the following research 10 methodology was adopted: 11

1. Step 1: The SNRWD0.16 was calculated for each road segment at a 0.16 km interval for 12 the entire road network in District 05 according to Equation (1). 13

2. Step 2: The SCI was calculated using SNRWD0.16 as the SNeff and the required SN for 14 a design life of 10 years as the SNreq according to Equation (2). SCI intervals were 15 also defined. 16

3. Step 3: PMS data were compared for each SCI interval to study the correlation 17 between the SCI and the rate of pavement deterioration. 18

4. Step 4: Enhanced decision trees were developed to implement the SCI into the 19 decision-making process. In addition to the enhanced decision trees, a methodology 20 was developed to incorporate the SNRWD0.16 in the overlay design in order to avoid 21 Type I and Type II errors. 22

Step 2: Define SCI intervals 23

The SCI was calculated for each 0.16 km-segment of the road sections according to 24 Equation (2). It was noticed that the SCI follows a normally-distributed function for the 25 road sections with a mean of 1.4. Initial SCI intervals were defined such that each 26 interval had approximately the same number of pavement segments. Pavement structural 27 capacity categories for the SCI intervals were defined as follows: 28

Very low: SCI less than 0.6; 29 Low: SCI between 0.6 and less than 1; 30 Low to Medium: SCI more than 1 and less than 1.5; 31 Medium: SCI greater than 1.5 and less than 2; and 32 High: SCI greater than 2. 33

Step 3-1: Comparison of Rates of Pavement Deterioration 34

For each SCI interval, PMS data were collected for survey cycles from 2005 to 2013. 35 However, the number of sections that did not receive treatment during that time range 36 was too small to definitively assess the deterioration trends. Hence, the time range was 37 altered to include all sections that did not receive treatments from 2009 to 2013 such that 38 all pavement sections selected had three data points from the pavement distress surveys 39 in 2009, 2011, and 2013. 40

Figure 1 presents an example of the deterioration trends of the Alligator Crack 41 Index (ALCR) for each predefined SCI interval and for the functional class of major 42


collectors. By fitting these data points linearly, the slope of each line is an indicator of 1 the deterioration rates. Table 3 presents the deterioration trends for all distresses indices. 2 The trends shown in Figure 1 and Table 3 were observed in all functional classes with 3 relatively high traffic volume such as rural and urban arterials and major collectors; 4 however, functional classes with low traffic volumes such as minor collectors and local 5 roads was observed to have slow rate of deterioration regardless of the SCI interval due 6 to the low traffic volumes on these roads. 7

As shown in Table 3, there is a correlation between the SCI category and the rate 8 of pavement deterioration. For example, sections in the “very low” and the “low” SCI 9 categories were deteriorating faster than sections in the “high” and the “medium” SCI 10 categories. This observation was expected as the rate of deterioration increases with 11 falling pavement structural conditions and is consistent with what have been reported by 12 other researchers in Virginia and Texas (4, 5). 13

Statistical analyses were conducted to support the findings presented in Table 3 14 and Figure 1. First, the net drops (differences) in surface distresses from year 2009 to 15 year 2013 were calculated for segments with SCI < 1 (i.e., low SCI) and segments with 16 SCI > 2 (i.e., high SCI). T-tests were then conducted with the null hypothesis of “low 17 SCI = high SCI” and the alternative hypothesis of “low SCI > high SCI”, for each surface 18 distress. In all cases, the null hypothesis was rejected at 95% confidence level except for 19 rutting in arterials. Summary of the t-test results is presented in Table 4. 20

Step 3-2: Comparison of SCI with LADOTD Performance Models 21

In order to determine whether pavement age has a major effect on the results presented in 22 Tables 3 and 4, pavement performance in the low and the very low SCI categories was 23 compared with the LADOTD PMS performance models, so that the effect of pavement 24 age is neutralized. Based on this analysis, pavement segments in the low and the very 25 low SCI categories were found to be deteriorating faster than the pavement performance 26 prediction models adopted by LADOTD, as shown in Figure 2. This observation 27 supports that pavements in the low and very low SCI categories require treatments that 28 add structural capacity to the pavement section, such as medium overlay, structural 29 overlay, or in place stabilization, to avoid the aforementioned Type II error. 30

Equations adopted in the LADOTD PMS as performance prediction models for 31 arterials for ALCR, RNDM, and RUT are presented as follows (17): 32

33 ALCR = 100 - 0.7027 * Age (5) 34 RNDM = 100 – 1.602 * Age (6) 35 RUT = 100 * e (-0.0121 * Age) (7) 36

Step 4-1: Incorporate SNRWD0.16 in Overlay Design 37

The aforementioned assumption of 50% loss in structural capacity may lead to two types 38 of error. First, if the actual loss in structural capacity is less than 50%, the designed 39 overlay using current design practice would be overestimated (Type І error). Second, if 40 the actual loss in structural capacity is more than 50%, the designed overlay using the 41 current practice would be underestimated (Type II error). Both types of error will lead to 42 loss of funds. Therefore, a procedure was developed to incorporate SNRWD0.16 in the 43


overlay design. Such a procedure would allow taking into consideration actual in-service 1 pavement structural conditions instead of assuming 50% loss in structural capacity. 2

The proposed procedure follows the same AASHTO 1993 design procedure 3 shown in Equation (3); however, the effective structural number of in-service pavements 4 is calculated based on the SNRWD0.16. According to a study conducted by Wu and 5 Gaspard, the SN calculated from FWD measurements (SNFWD) needs to be calibrated for 6 Louisiana’s soil conditions when used in overlay design purposes (20, 21). The 7 researchers developed a model to estimate the SNeff from the SNFWD as shown in 8 Equation (8). Since the model established for the SNRWD0.16 was developed and validated 9 based on the SNFWD, the SNRWD0.16 was adjusted based on the same procedure as shown 10 in Equation (9): 11

SNeff = 2.58 ln (SNFWD) -0.77 (8) 12 SNRWDeff= 2.58 ln (SNRWD0.16) -0.77 (9) 13 14

Validation of the Proposed Overlay Design Procedure 15

The objective of this analysis was to check the reliability and the effectiveness of the 16 proposed overlay design approach. Sections with noteworthy differences in the overlay 17 thickness designed using the proposed approach in comparison to the current approach 18 and that had available backcalculated layer moduli were considered for further analysis. 19

A multi-layer elastic software (Ken-Pave) was used to calculate the critical 20 pavement responses (tensile strain at the bottom of the asphalt concrete layer and vertical 21 strain on top of the subgrade) for the two design approaches, i.e., current and proposed 22 overlay design procedures. Pavement responses were calculated for a load application of 23 40 KN on a plate with a radius of 150 mm. The numbers of cycles for fatigue and 24 subgrade rutting failure were calculated according to the Asphalt Institute (AI) 25 methodology using Equations (10) and (11): 26

Nd=1.365*10-9 (εc)-4.477 (10) 27 28 where, 29 Nd = Number of repetitions for subgrade rutting failure; and 30 εc = Vertical compressive strain at the top of the subgrade. 31 32 Nf =0.0796*10-9 (εt)-3.291(E)-0.854 (11) 33 34 where, 35 Nf = Number of repetitions for fatigue failure; 36 εt = Horizontal tensile strain at the bottom of the AC layer; and 37 E = Surface layer modulus. 38 39 The number of repetitions calculated from Equations (10) and (11) were compared to the 40 actual traffic equivalent single axle load (ESALs) to determine the overlay design life. 41 Table 5 presents the results of the analysis. As shown in Table 5, the modified design 42 procedure was more precise in meeting the required 10-year design life for the different 43 pavement sections. 44


Step 4-2: Development of Enhanced Decision Trees 1

Results of Step 3 showed that pavement segments in the low and the very low SCI 2 categories were deteriorating faster than pavement sections in high and very high SCI 3 categories. Hence, there is a need to incorporate the SCI in the decision making process 4 to ensure that the most appropriate treatment is selected. The SCI was utilized to develop 5 an enhanced decision tree, as shown in Figure 3. The main advantage of the enhanced 6 decision tree is that it uses SNRWD0.16 to estimate in-service pavement structural 7 conditions instead of considering a 50% loss in structural capacity as it is currently 8 assumed. The decision trees were constructed based on the following assumptions: 9

Sections in poor structural conditions should receive (M&R) actions that increase 10 pavement structural capacity. 11

Sections in good structural conditions would receive the same (M&R) actions 12 selected according to the current LADOTD decision matrix, see Table 1. 13

Medium and structural overlays would be designed utilizing the SNRWD0.16 as 14 described earlier. 15

Minimum overlay thickness was set at 50.8 mm (2 in.). 16

EFFECTS OF RWD IMPLEMENTATION ON DECISION AND COST 17

To assess the influence of implementing RWD and SCI into the decision-making process, 18 the enhanced decision trees were applied to the whole road segments in District 05 that 19 were surveyed in the testing program. Comparisons between the PMS decisions using 20 the current decision matrix (see Table 1) and the proposed enhanced decision procedure 21 was conducted. Table 6 presents the PMS decision (treatment) and the number of road 22 segments (0.16 km. long) selected to receive each treatment using both the current 23 decision matrix and the proposed enhanced decision procedure. 24

To compare the difference in total cost between the two decision-making 25 processes, the cost of performing each treatment practice was obtained from the PMS 26 database, as shown in Table 7. Construction and RWD testing costs were considered in 27 this analysis; other sources of cost such as road user cost were not accounted for. The 28 cost and productivity of RWD testing were based on data obtained from ARA, Inc. as 29 shown in Table 8 (19). Monetary savings were calculated as follows: 30

31 Savings = current decision cost – (enhanced decision cost+ RWD testing cost) (12) 32 33 As shown in Table 6, the implementation of the enhanced-decision tree would save 34 significant funds to the state with the exception of the minor collector categories. 35

In lights of the results presented in Table 6, it was observed that there is a 36 correlation between the savings that could be achieved through the enhanced decision 37 procedure and the roadway functional class. For instance, applying the enhanced 38 decision procedure on major collectors and arterials resulted in cost savings; however, 39 applying the enhanced decision procedure on local roads and minor collectors resulted 40 did not result in cost savings. A good correlation between the dollar saving amount and 41 the Average Annual Daily Traffic (AADT) was found with an R2 of 0.92 as shown in 42 Figure 4. 43


The trend shown in Figure 4 was expected as local roads with low traffic volume have 1 slower rates of deterioration than relatively high volume roads, as traffic characteristics 2 have the highest impact on pavement performance (22). A recent study conducted by the 3 SCI developers concluded that using the SCI concept in low volume roads results in 4 overestimated treatment selection (23). Furthermore, there is a significant reduction in 5 RWD productivity while operated on local roads, which result in increasing the cost of 6 testing as indicated in the data shown in Table 8 (19). 7

SUMMARY AND CONCLUSIONS 8

The objective of the present study was to develop a methodology to implement pavement 9 structural conditions into the Louisiana PMS at the network level. RWD measurements 10 were utilized to predict the effective pavement SN and the SCI. Enhanced decision trees 11 were developed to incorporate SN and SCI into current overlay design and decision 12 practices. Based on the results of the analysis, acceptable correlation was observed 13 between the SCI and the rate of pavement deterioration indicating that pavement 14 segments in the low and the very low SCI categories were deteriorating faster than 15 pavement sections in high and very high SCI categories. Hence, pavements with low SCI 16 values require M&R actions that add structural capacity to the pavement sections. 17

The incorporation of pavement structural capacity data at the network level for 18 roads with relatively high traffic volumes would save state agencies significant funds and 19 would allow for better allocation of resources. The monetary savings were estimated 20 based on the construction cost for each M&R activity and RWD testing cost. Yet, other 21 cost sources such as road users’ cost should be considered in a future study. 22 Furthermore, cost savings presented in this study should be verified by monitoring 23 performance and cost of pavement sections over time. 24

ACKNOWLEDGEMENTS 25

The financial support provided by the Louisiana Transportation Research Center (LTRC) 26 is greatly appreciated. The contents of this paper do not necessarily reflect the official 27 views or policies of the Louisiana Department of Transportation and Development 28 (LADOTD) or the Louisiana Transportation Research Center. 29

REFERENCES 30

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8. Mitchell, M. R., R. E. Link, D. S. Gedafa, M. Hossain, R. Miller, and D. A. Steele. 11 Network Level Testing for Pavement Structural Evaluation Using a Rolling Wheel 12 Deflectometer. Journal of Testing and Evaluation J. Test. Eval., Vol. 38, No. 4, 2010, 13 p. 102320. 14

9. Sivaneswaran N. Network Level Pavement Structural Evaluation – A Way Forward. 15 National Pavement Evaluation Conference 2014 Blacksburg, Virginia, 2014. 16

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14. Elbagalati, O., M.A. Elseifi, K. Gaspard, and Z. Zhang. Prediction of In-Service 29 Pavement Structural-Capacity Based on Traffic-Speed Deflection Measurements. 30 Paper accepted by the ASCE Journal of Transportation Engineering, 2016, In Press. 31

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17


LIST OF TABLES AND FIGURES 1

Table 1 Current LADOTD Trigger Values for Treatment Selection (17) 2

Table 2 Functional Class Distribution of the Tested Sections 3

Table 3 Linear Fitting of Deterioration Rates 4

Table 4 Statistical Analysis Results for Rates of Deterioration for (a) Arterial and 5 (b) Major Collectors 6

Table 5 Comparison between Overlay Design Procedures Using a Mechanistic-7 Empirical Approach 8

Table 6 Impact of SCI Implementation on Treatment Selection and Cost 9

Table 7 Construction Cost for Each Treatment Strategy per 1.6 km (1 mile) 10

Table 8 Cost and Productivity of the RWD Testing per 1.6 km (1 mile) 11

FIGURE 1 Alligator Cracking Deterioration for Major Collectors 12

FIGURE 2 Example of Actual Pavement performance in Comparison to LADOTD 13 Prediction Models (Route LA 549) (Very low SCI category) 14

FIGURE 3 Proposed Enhanced Decision Trees for (a) Arterials and (b) Collectors 15

FIGURE 4 Correlation between Monetary Savings and Traffic Volume 16


Table 1 Current LADOTD Trigger Values for Treatment Selection (17) 1

Treatment type Indices Thresholds

ALCR RNDM PTCH RUT RUFF

Micro-surfacing on interstate ≥98 ≥98 ≥98 ≥80 <90

≥85

Thin overlay on interstate ≥90 ≥85 ≥90 <80 ≥85 <90

Medium overlay on interstate ≥65 <90

<90 ≥65 <90

--- <85

Structural overlay on interstate

<65 --- <65 --- ---

Micro-surfacing on arterial ≥95 ≥95 ≥95 ≥65 <80

≥80

Thin overlay on arterial ≥90 ≥80 <95 ≥80 <65 ≥70 <80

Medium overlay on arterial ≥50 <90

<80 ≥60 <80

--- <70

Structural overlay on arterial <50 --- <60 --- ---

Surface treatment on collector

≥85 <95

≥80 <95

≥85

≥65

≥80

Micro-surfacing on collector ≥95 ≥95 ≥95 ≥65 <80

≥80

Medium overlay on collector ≥60 <85

<80 ≥65 <85

<65 ≥60 <80

In place stabilization on collector

<60 --- <65 --- <60

where ALCR= Alligator Cracking Index, RNDM= Random Cracking Index, PTCH= 2 Patching Index, RUT= Rutting Index, and RUFF= Roughness Index 3


Table 2 Functional Class Distribution of the Tested Sections 1

Functional Class Total Length (km) Average AADT (Vehicle /day)

Rural Major Collector 718.7 1,800 Rural Minor Collector 514.4 898 Rural Local 243.5 598 Rural Minor Arterial 113.9 4,800 Urban Minor Arterial 90.1 6,400 Urban Collector 11.0 3000


Table 3 Linear Fitting of Deterioration Rates 1

Class SCI Interval ALCR Equation RUT Equation RNDM Equation M

ajor

C

olle

ctor

s very low y = -4.25x + 8636 y = -4.75x + 9632 y = -5.70x + 11556 Low y = -2.79x + 5707 y = -1.40x + 2907 y = -3.63x + 7395 low- medium y = -2.37x + 4855 y = -1.01x + 2140 y = -2.35x + 4812 Medium y = -0.69x + 1493 y = -0.73x + 1569 y = -0.48x + 1063

High y = -0.25x + 602 y = -0.40x + 903 y = -0.48x + 1071

Art

eria

ls

very low y = -3.25x + 6598 y = -3.50x + 7113 y = -2.00x + 4088 Low y = -2.50x + 5093 y = -3.50x + 7116 y = -1.75x + 3590 low- medium y = -1.75x + 3601 y = -1.75x + 3602 y = -1.00x + 2095 Medium y = -1.50x + 3110 y = -3.00x + 6617 y = -0.25x + 600 High y = -1.25x + 2610 y = -2.25x + 4616 y = -0.25x + 1104

where, x= date in years, and y= performance index value 2


Table 4 Statistical Analysis Results for Rates of Deterioration for (a) Arterial 1 and (b) Major Collectors 2

(a) 3

Parameter Performance Indices

ALCR RNDM RUT RUFF P-value 0.017 0.0075 0.14 0.0019 t 2.17 2.5 -1.0 3 t-critical 1.67 1.67 1.67 1.67 mean low SCI 7.95 6.70 6.32 19.80 mean high SCI 4.90 4.40 7.34 4.43 decision Not Equal Not Equal Equal Not Equal

4 (b) 5

Parameter Performance Indices

ALCR RNDM RUT RUFF P-value 0.012 0.00097 0.026 <0.00005 t 2.32 3.2 1.95 5.48 t-critical 1.67 1.67 1.67 1.67 mean low SCI 8.44 11.50 5.14 8.77 mean high SCI 5.12 7.35 3.60 3.64 decision Not Equal Not Equal Not Equal Not Equal

6


Table 5 Comparison between Overlay Design Procedures Using a 1 Mechanistic-Empirical Approach 2

Section#

Current overlay design procedure Proposed overlay design procedure

Overlay thickness

(cm) ɛt ɛv

Design life

(years)

Overlay thickness

(cm) ɛt ɛv

Design life

(years) 837-15 10.0 6.6E-6 1.7E-4 16 5.0 4.8E-6 2.1E-6 12 831-07 11.5 2.2E-4 2.5E-4 14 6.4 3.1E-4 3.5E-4 10 167-04 11.5 2.7E-4 1.1E-3 4 19.0 8.4E-5 7.6E-4 13 68-02 10.0 3.3E-5 7.2E-4 7 12.7 2.3E-5 6.1E-4 12


Table 6 Impact of SCI Implementation on Treatment Selection and Cost 1

*IPS = in place stabilization 2

Class Current Decision # Segments Enhanced Decision # Segments

Ru

ral

Art

eria

l

Do Nothing 328 Do Nothing 260 Microsurfacing 9 Microsurfacing 3 Thin Overlay 73 Thin Overlay 342 Medium Overlay 301 Medium Overlay 66 Structural Overlay 1 Structural Overlay 41 Treatment Cost $ 11,525,100 Treatment Cost $ 9,915,400

Maj

or C

olle

ctor

s Do Nothing 1,875 Do Nothing 1601 Microsurfacing 21 Microsurfacing 18 Surface treatment 268 Surface treatment 210 Thin Overlay 0 Thin Overlay 1,265 Medium Overlay 1969 Medium Overlay 831 IPS* 359 IPS* 567 Treatment Cost $ 85,641,300 Treatment Cost $ 80,787,200

Urb

an

Art

eria

l

Do Nothing 260 Do Nothing 217 Microsurfacing 10 Microsurfacing 7 Thin Overlay 121 Thin Overlay 238 Medium Overlay 161 Medium Overlay 52 Structural Overlay 10 Structural Overlay 48 Treatment Cost $ 8,352,800 Treatment Cost $ 7,799,700

Min

or C

olle

ctor

s Do Nothing 551 Do Nothing 367 Microsurfacing 11 Microsurfacing 10 Surface treatment 340 Surface treatment 240 Thin Overlay 0 Thin Overlay 561 Medium Overlay 1476 Medium Overlay 843 IPS* 837 IPS* 1,194 Treatment Cost $ 93,335,300 Treatment Cost $ 99,496,600


Table 7 Construction Cost for Each Treatment Strategy per 1.6 km (1 mile) 1

Treatment Type Construction cost/ 1.6 Km 2 lanes

Microsurfacing $ 67,000 Polymer Surface treatment $ 50,000 Thin Overlay $184,000 Medium Overlay $ 334,000 Structural Overlay $ 682,000 In place Stabilization $ 496,000


Table 8 Cost and Productivity of the RWD Testing per 1.6 km (1 mile) 1

Functional Class Productivity lane -1.6 km/ day Cost $ per lane - 1.6 km Interstate 250 $ 42 Secondary roads 150 $ 70 Local Roads 100 $ 105

2


1

2 FIGURE 1 Alligator Cracking Deterioration for Major Collectors 3

4

75

80

85

90

95

100

105

2008 2009 2010 2011 2012 2013 2014

AL

CR

Year

Very low Low Low to medium Medium High


1

2 3

FIGURE 2 Example of Actual Pavement performance in Comparison to 4 LADOTD Prediction Models (Route LA 549) (Very low SCI category) 5

6

60

65

70

75

80

85

90

95

100

105

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

ALCR Actual ALCR Model RNDM Actual

RNDM Model RUT Actual RUT Model


1

2 3

(a) 4

5

6 7

(b) 8 FIGURE 3 Proposed Enhanced Decision Trees for (a) Arterials and (b) Collectors 9

10


1

2 3

FIGURE 4 Correlation between Monetary Savings and Traffic Volume 4

5

R² = 0.9158

-50000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

0 1000 2000 3000 4000 5000 6000 7000

$ S

avin

g / l

ane

1.6

km (

1 m

ile)

AADT

Rural Local Minor Collector Major Collector

Rural Arterial Urban Arterial

paper no. 17-00404 implementation of the structural ...docs.trb.org/prp/17-00404.pdfdepartment of...

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