THE USE OF CALIBRATED PERFORMANCE MODELS IN SPVIAS FOR PAVEMENT MANAGEMENT

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1 THE USE OF CALIBRATED PERFORMANCE MODELS IN SPVIAS FOR PAVEMENT MANAGEMENT Felipe F. Camargo Dynatest Engenharia LTDA., São Paulo, Brazil Octavio de Souza Campos Agência de Transporte do Estado de São Paulo - Artesp, São Paulo, Brazil Santi Ferri Agência de Transporte do Estado de São Paulo - Artesp, São Paulo, Brazil Douglas P. Negrão Dynatest Engenharia LTDA., São Paulo, Brazil Margareth Brandão Ticianelli * CCR SPVias, São Paulo, Brazil * Avenida Maria do Carmo Guimarães Pellegrini, Bloco Engelog, Bairro do Retiro Jundiaí/SP - Brazil CEP: margareth.ticianelli@grupoccr.com.br ABSTRACT: One of the key challenges for a road concessionary is to be able to predict its roadway network s pavement performance with high accuracy, which, in turn, has a significant impact on the pavement management actions to be taken during the concession period. An impediment to a more common use of models to predict a pavement s performance in Brazil is lack of detailed historical information for calibrating performance models to local conditions. In the case of a concessionary, however, extensive detailed information regarding their roadway network is available. This study was conducted to calibrate pavement performance models in Highway Development and Management Model for the portion of Highway SP-28 under the administration of the concessionary Companhia de Concessões Rodoviárias (CCR) SPVias based on its historical data and to predict future pavement performance based on the calibrated models. Models were calibrated for an asphalt made with rubber modified bitumen and a cape seal and their roughness and cracking parameters progressed with time. KEY WORDS: HDM, pavement performance, performance models, roughness, cracking, asphalt rubber, cape seal 1. INTRODUCTION One of the key challenges for a road concessionary is to be able to predict its roadway network s pavement performance with high accuracy, which, in turn, has a significant impact on the pavement management actions to be taken during the concession period. An impediment to a more common use of models to predict a pavement s performance in Brazil is lack of detailed historical information for calibrating performance models to local conditions. In the case of a concessionary, however, extensive detailed information regarding their roadway network is available. This study was conducted to calibrate the Highway Development and Management (HDM) performance models for the portion of Highway SP-28 that is under the administration of Concessionary CCR SPVias. The SPVias concessionary manages lot of the Concession Program of the State of São Paulo. The highway network managed by SPVias consists of highways Castello Branco (SP-28), João Mellão (SP-255), Antonio Romano Schincariol (SP-127), Raposo Tavares (SP-27), and Francisco Alves Negrão (SP-258), a total of 516 kilometers. The concessionary was founded in and bought by the CCR Group in. The highways under its administration constitute the major link between the state capital and the southeastern part of the state of São Paulo, as well as the states of Paraná and Mato Grosso do Sul. A map of the concessionary s highway network is shown in Figure 1. Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 1

2 Figure 1. Map of SPVias highway network The present study focused on the part of the concessionary that stretches from the kilometer mark (close to Tatuí) until the 315. kilometer mark (close to Espírito Santo do Turvo) of highway Rodovia Presidente Castello Branco (SP-28). Historical data for the stretch in question was used to calibrate the HDM prediction models to local conditions and future performance of the pavements was predicted based on the calibrated models. A separate model was calibrated for each of the two types of existing riding surfaces (rubber asphalt and cape seal) and the roughness and cracking parameters were progressed in time. 2. THE HDM PREDICTION MODELS The HDM program was idealized for economical analysis of road networks for investments within a given period of time. This can be done by analyzing several rehabilitation alternatives for each pre-defined portion of the network and indicating the parameters for the investments, with the main objective of enhancing the conditions of the network. The first step for achieving a model for evaluating highway projects was taken by the World Bank in 1968 by means of a collaboration with the Transport and Road Research Laboratory (TRRL) and the Laboratoire Centrale des Ponts et Chausseés (LCPC). Subsequently, the Massachusetts Institute of Technology (MIT) developed the Highway Cost Model, which was an advance in the analysis of the interaction between construction costs, maintenance and vehicle operations [1, 2]. However, the prediction models still lacked an empirical basis, as well as the necessity for adjustment for the different regions around the world, especially in terms of extending its use for developing countries. In order to Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 2

3 provide this empirical basis for the models, the TRRL and World Bank joined forces in several studies worldwide, including studies in Kenya [3, 4], the Caribbean [5], in India [6], and in Brazil [7] in a joint-study between the Brazilian Government, via the Brazilian Company of Transportation Planning (GEIPOT, in Portuguese), and the United Nations Development Programme (UNDP), which resulted in the development of the HDM-III [8]. The program s pavement performance models originated from the aforementioned studies and have mechanistic-empirical concepts that allow for determining the annual progression of technical parameters, such as the international roughness index (IRI), cracking area, and the structural conditions expressed in terms of the modified structural number (SNC). The HDM program compares the cost estimates of different scenarios and allows for the evaluation of construction, maintenance, and rehabilitation alternatives, providing a system for highway pavement management. In addition, it the programs allows for planning the paving related actions and for allocating the necessary funds, as well as for predicting the performance of the road network. Moreover, the program allows for a sensitivity analysis of the results by varying the most important parameters. 3. CALIBRATION OF THE HDM MODELS The present study consisted of modeling part of the SPVias Concessionary that stretches from the kilometer mark (close to Tatuí, São Paulo) until the 315. kilometer mark (close to Santa Cruz do Rio Pardo, São Paulo) of highway Rodovia Presidente Castello Branco (SP-28) in both directions of traffic (East and Westbound). A situation map of the section in question is provided in Figure 2. Figure 2. Situation map of the highway stretch analyzed The stretch in question was chosen mainly because substantial historical data was available for predicting the future performance of the pavement, supported by the fact that the pavements were to suffer rehabilitation soon, and the concessionary desired to study how the pavements would behave based on the rehabilitation actions they were proposing. The main objective of the current study was to understand how the pavements would perform Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 3

4 until the next rehabilitation cycle (6 years according to the concession contract) in case no overlay was executed in 12 on sections in which cracking, roughness, and deflection were adequate at the time. The HDM prediction models were used to progress cracking and roughness in time for these sections in order to determine whether or not these sections would reach the allowable cracking and roughness values as listed in contract. The general models for cracking and roughness are presented below: Cracking: TYCRA = K ci * [F c RELIC + CRT] (1) where: Roughness: TYCRA = the predicted number of years to the initiation of narrow cracks since last surfacing or resurface; Kci = User-specified deterioration factor for cracking initiation; Fc = the occurrence distribution factor for cracking initiation for the subsection RELIC = 4.21 * exp(.14 SNC YE4/SNC 2 ) CRT = cracking retardation time; SNC: modified structural number; YE4: number of equivalent 8 kn standard axle loads for the analysis year. QI d = 13*K gp [134EMT (SNCK+1) -5. *YE (RDSb RDSa)+.66 CRX d + APOT d ]+K ge*.23*qi a (2) where: QI d = the predicted change in road roughness during the analysis year due to road deterioration, in QI; K gp = User-specified deterioration factor for roughness progression; K ge = User-specified deterioration factor for the environment-related annual fractional increase in roughness; EMT: exp(,23 kge AGE3); SNCK: modified structural number adjusted for cracking; RDSb = standard deviations of rut depth (along the wheel paths), in mm, before maintenance; RDSa = standard deviations of rut depth (along the wheel paths), in mm, after maintenance; CRX d = the predicted change area of 'indexed cracking due to road deterioration; APOT d = the predicted change in the area of potholes during the analysis year due to cracking; QI a = roughness of paved roads after previous maintenance. The pavement sections chosen for analysis exhibited the following conditions: Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 4

5 No cracking observed through visual inspection performed by an engineer during pavement evaluation for rehabilitation design. Roughness expressed in terms of QI smaller than the allowable (QI 35 counts/km). Maximum deflection measured with the Falling Weight Deflectometer (FWD) under the allowable deflection determined for each equivalent single axle load (ESAL). No overlay required according to PRO-269/94 Projeto de restauração de pavimentos flexíveis TECNAPAV [9], which is one of the two current Brazilian standards for designing the rehabilitation of flexible pavements. As a result, 371 sections (176 in Cape Seal and 195 in Rubber Asphalt) of lengths varying from meters to 6, meters (a total of km) met the established criteria and were chosen for analysis. It is important to note that no maintenance or rehabilitation actions were chosen during the modeling with HDM since the main objective was to observe the performance of the pavement sections in time. Also, because the present study aims strictly for the technical evaluation of the pavements, no cost analysis was carried out. The historical data for the sections in question was obtained from pavement roughness measurements taken each year during pavement monitoring, as required by the agency, for the years of 7,, and 11. The study was particularly focused on pavement roughness, which is a means of measuring the ride quality of a pavement and is closely related to operating costs, vehicle dynamics, and drainage. Pavement roughness is defined as the deviations of a pavement surface from a true planer surface with characteristic dimensions according to ASTM E867 []. A pavement profile represents the vertical elevations of the pavement surface as a function of longitudinal distance along a prescribed path of travel [11]. Roughness is presented in terms of the Quarter-Car Index (QI), which is the standard procedure for measuring roughness in Brazil, and is measured by response-type equipment in counts/km. This type of measurement can be converted into IRI by using Equation 3, developed by Patterson 1987 [12]. QI IRI 13 (3) The above mentioned data was used for calibrating the HDM models such that the progression curves for roughness, measured in terms of QI, and cracking area were adjusted to meet the actual measured annual progression of these parameters. By changing the calibration factors in the HDM models so that predicted curve approaches the measured curve, one can determine the observed pavement s performance and predict the future conditions in terms of pavement roughness. As far as cracking is concerned, the standard input parameters were chosen conservatively given that no cracking was observed test sections in question and because this parameter can be significantly affected by maintenance actions taken along the year for concentrated areas where distresses may appear. In addition to the data above, the structural and functional conditions of the pavements, associated to the number of ESALS predicted for stretch in question, were obtained from the rehabilitation design project, where pavement monitoring and traffic counts were conducted. According to the project, each direction can be divided into two distinct parts of homogeneous conditions in terms of traffic, deflection, and roughness. Thus, the same divisions were used for the present study in order to capture the behavior of the pavements for each condition. Also, a separate model was calibrated for each of the two types of existing riding surfaces (rubber asphalt and cape seal), with a total of 8 distinct calibration curves. Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 5

6 The input parameters for calibration are summarized in Table 1 for sections where the riding surface is in cape seal and in Table 2 for sections where the riding surface is in rubber asphalt, along with the expected ESALS for each division and the average values for deflection, adjusted structural number, and roughness. The sections are numbered 1 or 2, where each division is followed by a W, to denote the westbound direction, or an E, to denote the eastbound direction. In addition, ESALS is the yearly equivalent single axle loads, QI is the Quarter-car index, and SNC is the modified structural number. Table 1. Divisions for calibration purposes and respective parameters used for calibrating the HDM models for sections with riding surface in cape seal QI Cape Seal Division km DEF Direction ESALS SNC (count/km) ( m) Start Finish W westbound 3.42E W westbound 1.71E E eastbound 3.98E E eastbound 1.88E Table 2. Divisions for calibration purposes and respective parameters used for calibrating the HDM models for sections with riding surface in rubber asphalt QI Rubber Asphalt km DEF Division Direction ESALS SNC (count/km) ( m) Start Finish W westbound 3.42E W westbound 1.71E E eastbound 3.98E E eastbound 3.66E The results are then exported from HDM and the calibrated models are used to progress the roughness and cracking for each of the 371 sections. The parameters are then compared to the limits as specified by the agency and, in case the roughness or cracking reaches the limit (QI 35 counts/km or cracking area 17%) before the next rehabilitation cycle, a preventive rehabilitation action is required. Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 6

7 QI (counts/km) QI (counts/km) 4. RESULTS As previously mentioned, the HDM models were calibrated by changing the calibration factors in the model so that the predicted curve approaches the measured curve. The measured and projected QI for the sections with riding surface in rubber asphalt are plotted in Figures Figure 3. Calibration curve with riding surface in rubber asphalt for section 1W Figure 4. Calibration curve with riding surface in rubber asphalt for section 2W Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 7

8 QI (counts/km) QI (counts/km) Figure 5. Calibration curve with riding surface in rubber asphalt for section 1E Figure 6. Calibration curve with riding surface in rubber asphalt for section 2E The graphs above show the increment in QI between the years of 7 and for the sections with riding surface in rubber asphalt. In the case of a decreasing observed curve, the projected QI were conservatively predicted to increase in relation to the starting point. Similarly, the measured and projected QI for sections with riding surface in cape seal are plotted in Figures 7-. Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 8

9 QI (counts/km) QI (counts/km) Figure 7. Calibration curve with riding surface in cape seal for section 1W Figure 8. Calibration curve with riding surface in cape seal for section 2W Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 9

10 QI (counts/km) QI (counts/km) Figure 9. Calibration curve with riding surface in cape seal for section 1E Figure. Calibration curve with riding surface in cape seal for section 2E The graphs above show the increment in QI between the years of 7 and for the sections with riding surface in cape seal. With the performance models calibrated in terms of pavement roughness for each of the four divisions and for both types of riding surfaces, the prediction of pavement performance was carried out for each section to find out whether or not roughness and cracking would reach the limits as specified by the agency. An example of a curve obtained for a section with a riding surface of cape seal is shown in Figure 11 in terms of QI and in Figure 12 in terms of cracking. Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page

11 Cracking (%) QI (count/km) QI Progression Limit of Next Rehabilitation Figure 11. Calibrated QI curve for the section from the to the km mark with riding surface in cape seal 35 Cracking Progression Limit of Next Rehabilitation Figure 12. Calibrated cracking curve for the section from the to the km mark with riding surface in cape seal The section shown in Figures 11 and 12 extends from the km mark to the 134.4, with meters in length. The riding surface is in cape seal and the section has an average deflection of 97 m, which yields a SNC of approximately 11, and average QI of 15 measured in 11. The annual average daily traffic (AADT) is 2,92, which yields an expected number of equivalent single axle loads (ESALS) of 3.42 million a year. The progression of QI for this section shows that the section will have a QI of approximately 23 counts/km, which is adequate when compared to the limit of 35 counts/km. In terms of cracking, the section will not begin Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 11

12 Cracking (%) QI (count/km) to crack until the year of 23, which is 5 years after the next planned rehabilitation. Thus, based on the progression of QI and cracking, the pavements in this section will exhibit adequate conditions until the next planned rehabilitation. Similarly, an example of a curve obtained for a section with a riding surface of asphalt rubber is shown in Figure 13 in terms of QI and in Figure 14 in terms of cracking QI Progression Limit of Next Rehabilitation Figure 13. Calibrated QI curve for the section from the to the km mark with riding surface in rubber asphalt 35 Cracking Progression Limit of Next Rehabilitation Figure 14. Calibrated cracking curve for the section from the to the km mark with riding surface in rubber asphalt The section shown in Figures 13 and 14 extends from the km mark to the 133.1, with 5 meters in length. The riding surface is in rubber asphalt and the section has an average deflection of 99 m, which yields a SNC Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 12

13 % of Sections of approximately.9, and average QI of The progression of QI for this section shows that the section will have a QI of approximately 24 counts/km, which is adequate when compared to the limit of 35 counts/km. In terms of cracking, the section will not begin to crack until the year of 22, which is 4 years after the next planned rehabilitation. Thus, based on the progression of QI and cracking, the pavements in this section will exhibit adequate conditions until the next planned rehabilitation. According to the results, all cracking would begin after the year of 18, which requires no further action to guarantee adequate performance of the pavements in terms of cracking. However, some sections exhibit QI higher than the QI required before the next planned rehabilitation, requiring further action to guarantee adequate performance of the pavements in terms of roughness. The percentage of sections, in terms of length, with QI smaller than the limit of 35 counts/km is shown in Figure 15. % 9% 8% 7% 6% 5% % % % % % 96% 92% 82% % % % % Figure 15. Percentage of sections, in terms of length, with QI smaller than the limit of 35 counts/km Based on the results above, one can conclude that the pavements in question shown adequate conditions in terms of roughness, expressed in QI, and cracking area until the next rehabilitation cycle. The observed pavement conditions can be attributed to the high-quality maintenance procedures that have been each year by the concessionary. However, it is important to note that 2% of the sections will exhibit QI above the limit specified by the agency in 16, 4% of the sections will exhibit QI above the limit specified by the agency in 17, and 12% of the sections will exhibit QI above the limit specified by the agency in 18, which is the beginning of the next rehabilitation cycle. Thus, based on the predicted performance of the pavements obtained by calibrating the HDM curves to the real observed conditions, one can conclude that 88% of the pavements would still be in adequate conditions until the next rehabilitation cycle in case no overlay was applied. By calibrating the performance models in HDM and applying the calibrated curves to predict future conditions, the Concessionary was able to determine the future conditions of the pavements and make their management decisions in order to guarantee a safe and adequate pavement in terms of roughness and cracking throughout the concession period. Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 13

14 5. CONCLUSIONS This study was conducted to calibrate the HDM performance models for the portion of Highway SP-28 that is under the administration of Concessionary CCR SPVias based on its historical data and to predict future performance of the pavements based on the calibrated models. A separate model was calibrated for each of the two types of existing riding surfaces (rubber asphalt and cape seal) and the roughness and cracking parameters were progressed in time. Based on the results of this study, the following conclusions are warranted. 371 sections of varying lengths and two riding surfaces (cape seal and rubber asphalt) were used to calibrate the HDM models to the real pavement conditions based on historical data provided by the Concessionary. The calibrated models were used to predict future pavement conditions in terms of QI and cracking for a period of 15 years. The yearly progressions were analyzed to determine whether or not a given section would exceed the roughness and cracking limits before the next rehabilitation cycle in 18. Approximately 88% of the pavements analyzed would still be in adequate conditions until the next rehabilitation cycle based on the predictions of the calibrated HDM curves, which demonstrates the adequate pavement conditions, which can be attributed to the high-quality maintenance procedures that have been each year by the concessionary. The Concessionary was able to predict future pavement conditions and decide whether or not to take action (i.e. apply an overlay) based on the pavement performance obtained for part of their network. ACKNOWLEDGEMENT: The authors would like to acknowledge the Concessionary SPVias and ARTESP for providing the pavement monitoring data. REFERENCES: [1] Moavenzadeh, F., J. Stafford, J. Suhbrier, and J. Alexander. Highway design study phase I: the model. IBRD Economics Department Working Paper No 96. International Bank for Reconstruction and Development, Washington, DC, USA, [2] Moavenzadeh, F., F. Berger, B. Brademeyer, and R. Wyatt. The Highway Cost Model: General Framework. MIT Department of Civil Engineering Research Report No Massachusetts Institute of Technology, Cambridge, MA, USA, [3] Abaynayaka, S.W., G. Morosiuk, and H. Hide. Prediction of road construction and vehicle operating costs in developing countries. In ICE Proceedings, vol. 62, no. 3, pp Thomas Telford, [4] Harral, C.G. The highway design and maintenance standards model (HDM): model structure, empirical foundations and applications. In PTRC Summer Annual Meeting, University of Warwick, July, [5] Hide, H. Vehicle operating costs in the Caribbean: results of a survey of vehicle operators. Transport and Road Research Laboratory Report 31. Crowthorne, UK, [6] CRRI. Road user cost study in India. Final Report, Central Road Research Institute, New Delhi, India, [7] GEIPOT. Research on the interrelationships between costs of highway construction, maintenance and utilization. Final report, 12 Volumes, Empresa Brasileira de Planejamento de Transportes, Brasília, DF, Brazil, [8] Watanatada T., C.G. Harral, W.D.O. Paterson, A.M. Dhareshwar, A. Bhandari, and K. Tsunokawa. The highway design and maintenance standards model volume 1: description of the HDM-III model. The Highway Design and Maintenance Standards Series. The World Bank, Washington, DC, USA, [9] DNER-PRO 269/94. Projeto de restauração de pavimentos flexíveis TECNAPAV. Rio de Janeiro, RJ, Brazil, 1994, pp Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 14

15 [] ASTM Standard E867 6 (12). Standard Terminology Relating to Vehicle-Pavement Systems. ASTM International, West Conshohocken, PA, 6, DOI:.15/E867-6R12, [11] Wang, H. Road Profiler Performance Evaluation and Accuracy Criteria Analysis. MS thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, [12] Paterson, W. Road deterioration and maintenance effects: models for planning and management. The Highway Design and Maintenance Standard Series. The World Bank, Washington, DC, USA, Copyright 13 IJPC International Journal of Pavements Conference, São Paulo, Brazil Page 15