Road Infrastructure Asset Management Course

Transcription

1 SEPTEMBER - 7, 7 UNIVERSITY OF TRANSPORT AND COMMUNICATIONS HANOI, VIETNAM Road Infrastructure Asset Management Course JOINTLY ORGANIZED BY: KYOTO UNIVERSITY UNIVERSITY OF TRANSPORT AND COMMUNICATIONS Chaired by Prof. Kiyoshi Kobayashi, Kyoto University

2 Predict the deterioration process of road pavement using Stochastic model Nguyen Dinh Thao Highways and Airfields Engineering Laboratory Civil Engineering Department University of Transport and Communications dinhthao77@yahoo.com

3 Contents Part. Part. Part. Part. Introduction Road System Develop Prediction Model Application Prediction Model Conclusion

4 Contents PLAN PROJECT LEVEL PLAN DO SEE

5 Shares of different Roads (Total length,8km). %. % 7.7 % 9.7 %, Length of different Roads, km,99.8 %. %, National Roads Provincial Roads District Roads Commune Roads Urban Roads Special Roads 7,9,8 National Roads Provincial Roads District Roads Commune Roads 7,7 7,7 Urban Roads Special Roads

6 Shares of different Roads (Total length,8km). %. % 7.7 % 9.7 %, Length of different Roads, km,99.8 %. %, National Roads Provincial Roads District Roads Commune Roads Urban Roads Special Roads 7,9,8 National Roads Provincial Roads District Roads Commune Roads 7,7 7,7 Urban Roads Special Roads The total number of bridges were,9 with the total length of approximately,9m.

7 8, Shares of different Roads in term of percentage (Total length,8km) Shares of different Roads in term of length (Total length,8km).%.% 8, 9,.8%.%.% High Performance,7,9,99 Class I and II - Roads Class III-Roads Class IV-Roads Class VI-Roads Class V-Roads Class I and II - Roads Class III- Roads Class IV- Roads Class V- Roads Class VI- Roads (Classes of roads are defined according to Highway Specification for Design, code TCVN -998)

8 Shares of different Roads (Total length,8km).% 9.89% 9.7%.8%, Length of different Roads, km,8, 7.7%,9 8,7 Cement Concrete Asphalt Concrete Bituminous Surface Treament Macadam Stone Soil, Cement Concrete Asphalt Concrete Bituminous Surface Treament Macadam Stone Soil

9 Management and Maintenance NH. Haivan Pass NH. Haivan Pass

10 Roads Operation without Maintenance S initial S critical Serviceability Index Lost of SI By Reason of War + Lack of Budget Elapsed Time Example: Actual Life Time Designed Life Time Rao Bridge in HaiPhong Province: Operated without any maintenance, so collapsed in 987 with Life Time of 7 years months. Railway Tunnels System: built by French in 9 s, very poor maintenance for 7 years, so in 99 Tunnel No.7 through HaiVan Pass collapsed.

11 Roads Operation with Maintenance/Problem of Planning for Maintenance Work SI initial SI restored after repairing SI critical Lost of SI? Elapsed Time Problems: T When, How? Lack of basics of Sciences to determine!!! T Actual Life Time Designed Life Time Need building prediction model

12 Contents Part. Part. Part. Part. Introduction Road System Develop Prediction Model Application Prediction Model Conclusion

13 Forecasting/Predicting Deterioration of Facilities Performance/ Serviceability Index Present Can you predict the condition of A Markov chain pavement is specified in the by future? :. A Set of States (S). Probability Transition Matrix (P). Probability Vector π p p p Condition states Developing Prediction Model using Markov chain of Stochastic process based on investigated data Maintenance Status Quo Do nothing x t A (Present) x t B (Future) Time

14 Markov Chain Let s study the change of condition states of a system within a set of condition states S. A Markov chain is a sequence of random discrete variables having the property that, given knowledge of the present, the past is irrelevant for predicting the future. In other words, { } n+ = j = =... n = n n = i Pr X s X s X s X s X s { } n+ j n i = Pr X = s X = s

15 Markov Transition Matrix If the space of states is finite, the transition probability distribution can be represented as a matrix, called the transition matrix P. P p p p K p p p = K p p p K K KK { } p = Pr X = s X = s ij n+ j n i

16 Transitional Probabilities Pavement condition Best Worst New Normal Worn Very worn x t n p p p p p p p p Transitional Step x t n+ Initial probability distribution (vector) on the set of states: π Time

17 Probability Vector/Distribution π : Initial probability distribution (vector): K components (Ex. K=) i th component of π means probability that facility is in state i at the current time. π () = π P ( ) ( ) ( n) π = π P ( ) n ( ) π = π P = π P P = π P ( ) ( ) ( ) ( ) ( ) π = π P = π P P = π P... i th component of π (n) means?

18 Contents Part. Part. Part. Part. Introduction Road System Develop Prediction Model Application Prediction Model Conclusion

19 Application Predict the deterioration of some highways in term of Roughness Index (IRI) in Vietnam based upon the investigated data.

20 The common IRI scale ( )

21 Pavement roughness Ratings according to Vietnamese Standard (TCN-77-) AC Pavement Design speed (km/h) Good IRI (m/km) Fair Poor Very Poor ; ; 8 ( ; ] ( ; ] ( ; ] ( ; 8] ( ; ] ( ; ] ( ; 7] (7 ; 9] ; ( ; ] ( ; ] ( ; 8] (8; ] Bituminous surface treatment Pavement Design speed (km/h) Good Fair IRI (m/km) Poor Very Poor ( ; ] ( ; ] ( ; 8] (8; ] ; ( ; ] ( ; 7] (7 ; 9] (9; ]

22 Selected pavement roughness Ratings IRI (m/km) IRI < IRI < IRI < IRI < IRI 7 IRI > 7 States Very Good Good Fair Quite Fair Poor Very Poor Notation of states

23 Data of Road condition investigated by VRA in and IRI (m/km) ID SECT_NAME... [] []... QL8-I:.-7 BAC NINH IRI QL8-I: 7-8 BAC NINH QL8-I: 8-9 BAC NINH < IRI QL8-I: 9- BAC NINH QL8-I: - BAC NINH < IRI QL8-I: - BAC NINH 7 QL8-I: - BAC NINH < IRI 8 QL8-I: - BAC NINH < IRI 7 9 QL8-I: - BAC NINH IRI > 7 Notation of states 9 QLB: LANGSON ROUGHNESS (M/KM)... NMT_LTYPE [7]... [9]

24 Classification of road conditions in and based on investigated data State Sum Number of road sections How roads changes from states to states?

25 Changes of condition states of selected road sections in term of IRI ratings from to 9 sections (~ 9 kilometers) of highways : NH, NH8, NHB State Sum Sum 8 8 9

26 Transition matrix P in Tabular Form State j j p = = j j p = = j j p = = j j p = = j j p = = j j p = =

27 Transition matrix P in Canonical Form P =...

28 Transition matrix P in Canonical Form/Illustration P =... x t P x t+

29 Classification of road conditions in and based on investigated data State Sum Number of road sections Initial probability vector is the initial probability distribution on the set of states in π = (,.,.,.,.97,.7) i= π i =

30 Calculation probability vector π Given Transition Matrix P, Initial probability vector π, and ( n) π = π P n π () = (.,.,.,.,.97,.7) ( ) π = π P = (,.,.,.,.97,.7) π () = (.,.7,.9,.,.9,.)... Number of sections in state 9 x. = 8

31 Given Transition Matrix P, Initial probability vector π, and ( n) π = π P n π () = (.,.,.,.,.97,.7) π () = (.,.7,.9,.,.9,.) π () = (.,.,.,.7,.9,.9) π () = (.,.,.,.,.9,.989) π () = (.,.,.,.,.,.999) Predicted number of road sections in each condition state in the future State Number of road section in year Absorbing State

32 Number of road sections Deterioration of selected road sections according to the first maintenance scenario Time Remain 7 Time (Year) State () State () State () State () State () State () Total

33 Maintenance & Repair Scenarios considered The first scenario: natural deterioration. The second scenario: conducting periodic maintenance or repair to avoid being in state The third scenario: conducting periodic maintenance or repair at higher scale and wider scope to avoid being in states and

34 Modelling nd Maintenance & Repair Scenario x t P x ta R x t P = =... R = Q x x t t+

35 ( t ) ( t' ) (t + ) ( t ) (t + ) P R Q=PR = P =... R = Q = P. R Q =...

36 Given Transition Matrix Q, Initial probability vector π, and ( n) n π () = π () = π () = π () = π () = π () = π () = π (7) = State Number of road sections in year Steady State (., (., (.9, (., (.9, (.9, (.9, (.9, 7 9 π.,.7,.9,.7,.9,.89,.89,.89, 8 9 = π Q ,.9,.9,.,.,.,.,., ,.,.8,.9,.,.8,.7,.7, ,.9,.87,.,.,.99,.,.,.7).).).).).).).)

37 Number of road sections Deterioration curves of selected road sections according to the second maintenance scenario Time (Year) State () State () State () State () State () State () Total

38 Modelling rd Maintenance & Repair Scenario x t P x ta R x t P = =... R = Q x x t t+

39 Modelling rd Maintenance & Repair Scenario ( t ) ( t' ) (t + ) ( t ) (t + ) P R Q=PR = R = Q = P. R Q =.98.

40 Given Transition Matrix Q, Initial probability vector π, and π ( n) = π Q n π () = (.,.,.,.,.97,.7) π () = (.8,.7,.9,.,.,.) π () = (.7,.8,.,.,.,.) π () = (.8,.8,.7,.7,.,.) π () = (.77,.,.9,.9,.,.) π () = (.9,.9,.9,.,.,.) π () = (.89,.,.9,.9,.,.) π (7) = (.89,.,.9,.9,.,.) State Number of road section in year Steady State 7 7

41 Deterioration curves of selected road sections according to the third maintenance scenario Number of road sections Time (Year) State () State () State () State () State () State () Total

42 Contents Part. Part. Part. Part. Introduction Road System Develop Prediction Model Application Prediction Model Conclusion

43 CONCLUSION Predicting the deterioration process of road infrastructure is always the significant and challenging task for road management agencies to ensure the effectiveness of the maintenance and management work. Using prediction model based upon Markov chain of stochastic process, future condition state of roads are definitely determined. Consequently, the most proper scenario of road maintenance should be established. Given the required quality of the roads, the maintenance scenario Will be presentated of minimum in life Life cycle Cycle cost Cost should Analysis be specified. (). Otherwise, given the limit allocated budget, by using prediction model the maintenance scenario of highest quality gained also is pointed out.

44 Q&A Nguyen Dinh Thao, UTC Thank you very much for your attention!