Estimation for Pavement Performance Curve based on Kyoto Model : A Case Study for Highway in the State of Sao Paulo

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1 Estmaton for Pavement Performance Curve based on Kyoto Model : A Case Study for Kazuya AOKI, PASCO CORPORATION, Yokohama, JAPAN, Emal : kakzo603@pasco.co.jp Octávo de Souza Campos, Publc Servces Regulatory Agency of Transportaton Delegates of the State of Sao Paulo, Brazl Carlos Yuko Suzuk, PLANSERVI Engneerng, Sao Paulo, Brazl Fatma Tostes, BASE aerofotogrametra e projetos S.A., Sao Paulo, Brazl ABSTRACT Ths paper focuses upon a case study to conduct the estmaton for pavement performance curve usng the archved hghway pavement data n the state of Sao Paulo. One of the most mportant ssues for the pavement asset management s to keep the accuracy of estmaton for a deteroraton predcton model. The deteroraton predcton model, based on the Kyoto Model, formulates the regularty of the deteroraton process of pavement usng hazard model and represents the deteroraton probabltes as Markov Transton probabltes. Ths study experments the estmaton for deteroraton performance based on Kyoto Model usng archved data of pavement nspecton carred out for and the practcal avalablty was nvestgated. Keywords: pavement management, deteroraton forecastng, PDCA cycle, Kyoto Model INTRODUCTIONS Recently, the dea of the asset management for an optmal mantenance work on the nfrastructure system n whch a huge stock s formed s promoted and a vast amount of studes about nfrastructure asset management have been reported. Asset management has been acheved by contnuous accumulaton of a varety of knowledge data generated from annual mantenance works. Especally, the development of the nspecton technology whch gathers nformaton about deteroraton of nfrastructures contrbuted greatly. Furthermore, knowledge database, ncludng deteroraton data and repar record, mproved PMS functons and made t possble to provde useful nformaton for pavement management. For nfrastructure asset management mplementaton, t s mportant to formulate a deteroraton forecastng model of nfrastructure faclty. In the case of road pavement management, automatc data acquston system to collect the pavement condton data n a quanttatve way has been ntroduced, therefore, t s possble to acqure huge nspecton data quckly. The statstcal methodology to forecast a deteroraton model of road pavement condton usng archved nspecton data has been used wdely.

2 Varous methodologes for estmaton of deteroraton forecastng model have been proposed. In ths paper, above all, the methodology, named Kyoto Model, s used to estmate a deteroraton forecastng mode. Kyoto Model formulates the regularty of the deteroraton process of pavement usng hazard model and represents the deteroraton probabltes as Markov Transton probabltes usng archved nspecton and repar data. In addton, the case study usng Kyoto Model s descrbed for the hghway n the State of Sao Paulo. RESEARCH FUNDAMENTALS Outlne of the research A large number of studes were conducted to develop the methodologes for estmatng the deteroraton predcton model of nfrastructure facltes. For example, HDM-4 s one of the world-famous pavement management systems. HDM-4 requres several nput data n order to calbrate deteroraton predcton curve sutable for actual road condtons. The methodology, called as Kyoto Model, s proposed to statstcally estmate a deteroraton performance curve usng archved data ncludng past nspecton and repar hstory data. The deteroraton predcton model, based on the Kyoto Model, formulates the regularty of the deteroraton process of pavement usng hazard model and represents the deteroraton probabltes as Markov Transton probabltes. The specal feature of Kyoto model s that t ams at evaluatng pavement performance and supples the feedback for next plannng; therefore Kyoto Model has hgh affnty wth PDCA(PLAN DO Check - Acton) cycle for pavement management. Ths study experments the estmaton for deteroraton performance based on Kyoto Model usng archved data of pavement nspecton carred out for and the practcal avalablty was nvestgated. In ths research, t was concluded that the Kyoto Model s capable to estmate a suffcent and practcal performance curve usng the mnmum data. Also, we descrbed that a contnuous accumulaton of archved data s mportant for evaluatng the performance based on PDCA cycle. In Federatve Republc of Brazl, the total length of road s approxmately.6 mllon km, and the road of 0.2 mllon km s paved. The way of road mantenance s dvded nto two type, one s drect control and the other s concesson contract. Concesson contract wll be ntroduced strongly especally for man hghway wth large traffc. In the management of State road, concesson contract wll be popular as well. In the State of Sao Paulo, the paved road n 5,000 km of the total road n 23,000km managed by the State of Sao Paulo s managed on concesson contract. ARTESP(Publc Servces Regulatory Agency of Transportaton 2

3 Strategc Management Cycle New polcy Re-examnng of nput Polcy Evaluaton Cost reducton, mprovng servce and performance New polcy, goal, techncal standard Adjustment of logc model Modelng of work process Logc Model INPUT PROCESS OUTPUT OUTCOME New plannng See Plan Budget control management cycle Budget Plannng Do See Plan Do Plan See Repar Plannng Do Effcency Data Management reference/nput Pavement DB Inventory data update Effectveness Daly montorng Repar nformaton Scheduled nspecton Fg. Whole Structure of Kyoto Model Pavement Management System Delegates of the State of Sao Paulo) has a responsblty of management of concesson contract n ths State and has perodcal nspecton data of managed road. Kyoto Model Kyoto model whch s the methodology to be used n the case study s descrbed smply. Kyoto model has a herarchal structure nvolvng the management cycle for budget control and the strategc management cycle. The management cycle for budget control supports daly mantenance work. On the other hand, the strategc management cycle ams to control daly work by montorng from the vew pont of strategc level and pont out for mprovement of management work based on PDCA cycle. Road pavement management work s descrbed by ths herarchal management cycle and that whole process of management work s formulated by logc model. On the other hand, varous knd of data requred by daly mantenance work s accumulated nto pavement database and they are referred for decson makng n mantenance work and are used as evaluaton ndex of logc model for assessment. The one of the most mportant functon of Kyoto Model s the methodology to estmate a deteroraton forecastng model usng actual nspecton and repar archved data. Deteroraton Forecastng Model The methodology to estmate a deteroraton forecastng for road pavement condtons s explaned brefly. 3

4 Markov Transton Probabltes The deteroraton process of road pavement can be formulated as Markov transton probabltes. The deteroraton process of pavement s uncertan and forecastng future states cannot be accomplshed determnstcally. The Markov transton probablty s used to represent the uncertan transton of the condton state of a pavement segment durng two ponts n tme. The observed condton state of the pavement segment at tme t A s expressed by usng the state varable h(t A ). If the condton state observed at tme t A s, then the state varable h(t A )=. A Markov transton probablty, gven a condton state h(t A )= observed at tme t A, defnes the probabltes that the condton state at t future tme (t B for example) wll change to h(t B )=j. That s Prob [ h( t = j h( )] = π () B t A j The Markov transton probabltes matrx can be defned by usng the transton probabltes between each par of condton state (, j ) as π = M 0 L O L π π J M JJ (2) From the defnton of transton probablty π j =0 (>j). Then, t holds for the Markov transton probablty π π j j 0 = 0( when > j) (3) J = π j = j And n ths case π JJ =. Mult-Stage Exponental Hazard Model By usng the exponental hazard functon, t s possble to represent a deteroraton process of a pavement segment that satsfes the Markov property (ndependency from past hstory). The Markov transton probabltesπ j =(z) that a transton n the condton state from to j (j>) occurs between the nspecton tme ponts t A and t B =t A +z becomes π ( z) = Pr ob[ h( τ ) = j = j m= m s= s B λs λ λ j h( τ ) = ] j A m s= m s+ λs exp( λmz) λ λ m (4) 4

5 Here the followng notaton rule s gven. m s= j s= m λs λ λ s s+ m = λs = λ λ m ( when m= ) ( when m= j) The hazard rate characterzng the deteroraton process of a pavement segment s consdered to change n relaton to the vector x as follows: λ = x β (5) where β s a row vector of unknown parameters. And also, usng the parameter " k whch ndcates varablty characterstc of hazard rate, comparatve assessment of deteroraton speed can be formulated usng mxture hazard model theory as follows: k k λ = λε (6) where λ k s the average hazard rate of group a k and heterogenety parameter ε s a stochastc varable whch expresses the degree of dssocaton from average hazard rate. CASE STUDY Archved Inspecton Data for Estmatons In ths case study, the performance curve of pavement condton was estmated by usng the nspecton data of the hghway managed by ARTESP n Sao Paulo. Inspecton archved data managed by ARTESP has condton data, such as IRI (Internatonal Roughness Index) and QI (Quarter-car Index), of each pavement segment collected by perodcal nspecton. Condton nspecton s conducted every year and ts data s evaluated every 200m of pavement segment. As data for estmaton of performance curve n ths research, nspecton data collected from 2007 to 200 on the roads of SP50 (Rodova Ancheta), SP60 (Rodova dos Imgrantes) and SP 248(Rodova Cônego Domenco Rangon) was used. The target condton ndex was IRI. In order to estmate a performance curve, two tme nspecton data are requred. Unknown parameters for calculaton of Markov transton probabltes s estmated usng sample data nvolvng a par of nspecton data and the nformaton of nspecton nterval. Some sample data of the secton where the condton rank of IRI s mproved are excluded because t can be 5

6 SP 248 Fg. 2 Route map and locaton of the target route assumed that that segment was repared. Fnally, the number of sample data for estmaton s 3,707. Estmaton Results Table. shows the defnton of condton rankng of IRI and the estmaton result of the hazard parameters n the case of bench-markng. Furthermore, the estmaton result of heterogenety parameters by each target route s descrbed n Table. 2 and the performance curves(expected lfe length of pavement surface) of bench-markng and each route s drawn n Fg. 3. The average lfe length of pavement surface of IRI to condton rank 6 (IRI 5) s approxmately 24 years. The deteroraton speed n the ntal stage of deteroraton s faster. On the other hand, the deteroraton speed after the condton of rank 5 (IRI 4) s rapdly ncreasng. The comparson of deteroraton speed among routes shows that the deteroraton speed of k route SP50 s faster rather than the bench-markng case( ε =.2) and the heterogenety parameter of route SP60 s 0.83, whch means that the deteroraton speed of route SP60 s gradual. Table. 3 and Fg. 6 descrbes the results of the comparson of deteroraton speed among lanes. In ths results, there are no dfferences of deteroraton speed among routs. Generally, deteroraton speed of pavement surface s affected by some characterstcs, such as traffc volume, surface type, pavement structure, etc. In ths research, these factures are not ncluded n data to estmate the deteroraton performance. Actuary margnal lane has some pavement segments whch surface type s segment 6

7 concrete. The ssue that remans s the mpact analyss of these characterstcs of each pavement segment. Table. Estmaton Result (Bench-Markng Case) IRI (mm/m) Condton Rankng Hazard Rate 0 IRI IRI IRI IRI IRI IRI 6 - Elapsed Tme (Year) Condton Rankng (IRI) SP50 SP60 SP248 average Fg. 3 Performance Curve (Bench-Markng and Each Route) Table. 2 Estmaton Result (Comparson among Routes) Route Name Epslon SP 50.2 SP SP

8 Elapsed Tme (Year) Elapsed Tme (Year) Condton Rankng (IRI) Condton Rankng (IRI) SP50 NORTE_FX SP50 NORTE_FX2 SP60 NORTE_FX SP60 NORTE_FX2 SP50 SUL_FX SP50 SUL_FX2 SP60 NORTE_FX3 SP60 NORTE_FX4 SP50 MARGINAL_NORTE_FX SP50 MARGINAL_NORTE_FX2_ SP60 SUL_FX SP60 SUL_FX2 SP50 MARGINAL_SUL_FX SP50 MARGINAL_SUL_FX2 SP60 SUL_FX3 SP60 SUL_FX4 Fg. 4 Performance Curve of SP50 and SP60 (Comparson among Routes & Lanes) Fg. 5 Pctures of SP50 and SP60 Elapsed Tme (Year) Condton Rankng (IRI) SP248 LESTE_FX SP248 LESTE_FX2 SP248 OESTE_FX Fg. 6 Performance Curve of SP248 (Comparson among Routes & Lanes) 8

9 Table. 3 Estmaton Result (Comparson among Routes & Lanes) SP 50 SP 60 SP 248 Lane Name Epslon Lane Name Epslon Lane Name Epslon NORTE_FX.7 NORTE_FX 0.92 LESTE_FX 0.96 NORTE_FX2.5 NORTE_FX LESTE_FX SUL_FX.07 NORTE_FX OESTE_FX.00 SUL_FX2.04 NORTE_FX MARG._NORTE_FX 0.97 SUL_FX 0.9 MARG._NORTE_FX2.0 SUL_FX MARG._SUL_FX.05 SUL_FX MARG._SUL_FX2.03 SUL_FX CONCLUSIONS Ths paper shows the mportance of deteroraton performance evaluaton usng nspecton data to acheve effectve road asset management. The paper s a case study to estmate the deteroraton performance by usng nspecton data of the hghway n Sao Paulo. In ths case study, the deteroraton performance of IRI was estmated and the pavement segments whch has the deteroraton speed greater devatng from the bench mark case based on the comparson of deteroraton speed among routes and lanes are dscovered. The methodology proposed by ths research s a part of the evaluaton of the PDCA cycle of road asset management. It was shown that unusual segments of deteroraton speed can be dentfed. The results derved are useful nformaton for mantenance plannng. By changng the evaluaton segments, t s possble to fnd out a varety of problematc road segments. It s mportant to mprove the management system contnuously for the mplementaton of effectve road asset management. The research results suggests a soluton for carryng out the contnuous mprovement based on the PDCA cycle. REFERENCES Tsuda, Y., Kato, K., Aok, K. and Kobayash, K. (2006): Estmatng Markovan Transton Probabltes for Brdge Deteroraton Forecastng, Journal of Structural Eng./Earthquake Eng., JSCE, Vol.23, No.2, pp.24s-256s. Kato, K. and Kobayash, K. (2009): Dsaggregated Hazard Rates Evaluaton and Benchmarkng for Infrastructure Asset Management, Internatonal Semnar on Urban Infrastructure Management, The Unv. of Tokyo. 9

10 Aok, K. (202): Road Performance Assessment Kyoto Model, Kyoto Unversty UTC Jont Summer Course of Road Infrastructure Asset Management, Hano. Aok, K., MORI, H. and OKADA, K. : Effect of Pavement Servce Level Upon Operaton Tmng of Perodcal Inspecton Polcy, 7th ICPT, Bangkok, Thaland, 20. Aok, K.: Pavement Inspecton System and Measurng the Deteroraton Performance for Pavement Management, Proc. of Internatonal Semnar on Asset Management Implementaton n Asan Countres, Kuala Lumpur,