Managing Railway Bridges

Bryant LeProfessor John Andrews

>100 years old

Bridge Asset• Railway bridges is a

major railway asset group

• 35,000 bridges• 50% of the population

more than 100 years old

• Bridge management and maintenance planning is a difficult task

Aims and Objectives

• Develop a management tool• Maintenance strategy (repair and renew) can be

investigated and optimised• Longer term objective to minimise the whole life costs.

Current bridge system

• Structure condition marking index (SCMI)• Bridge condition is often rated in term of condition score from 0-100

Problems

• Data not available with the rating system started in 2000, only 60% of bridges were inspected by 2006

• Large inspection interval (6 years)• Asset contains only one set of score• Concern from the ORR (Office of Rail Regulation) about the accuracy

of the scores

Degradation study

• Study historical work done data

• Analyse the time of the component requiring a certain type of repair

• Distribution fitting

As New

Component life

As new condition Good condition Poor condition Very poor condition

Needs minor repair

Needs major repair

Needs replacementβ1,η1 β3,η3

t (year)

β2,η2

Weibull distribution

Weibull distribution

Weibull distribution

Degradation studyWeibull Fitting (Weibull 2p RRXY) Number of data

Bridge component Material Condition Intervention Beta Eta (year) Mean (year) Complete Censored

GIRDER Metal

Good Minor Repair 1.71 23.39 20.86 37 72

Poor Major Repair 0.87 44.27 47.49 12 35

Very Poor* Replacement* 1.14 149.63 142.77 3 1

 

DECK

Metal

Good Minor Repair 1.265 10.28 9.54 16 67

Poor Major Repair 1.038 20.00 19.71 10 58

Very Poor Replacement 1.009 28.47 28.36 14 72

Concrete

Good Minor Repair 1.082 19.09 18.52 3 7

Poor* Major Repair* 1.000 26.67 26.67 0 4

Very Poor Replacement 0.976 34.26 34.63 2 10

Timber

Good Minor Repair 1.312 3.99 3.68 12 5

Poor Major Repair 1.371 7.13 6.52 5 6

Very Poor Replacement 1.501 6.12 5.52 27 40

 

BEARING Metal

Good Minor Repair 0.838 14.94 16.41 12 39

Poor Major Repair 2.129 14.43 12.78 5 10

Very Poor* Replacement* 1.000 21.92 21.92 1 2

 

ABUTMENT Masonry

Good* Minor Repair* 1.000 51.94 51.94 1 9

Poor* Major Repair* 1.000 100.87 100.87 1 2

Very Poor* Replacement* 1.000 150.00 150.00 0 0

• The bridge is considered in term of principal elements:

– girder, – deck, – bearing, – abutment

• Weibull distribution is best fitted

• Increasing failure rates

Bridge models

• Widely adopted• Easy and fast to construct and run• Consider opportunistic maintenance,

servicing, environment, repair delay.• Constant deterioration rates• Model size increases exponentially for

more complex problem

Markov model Petri-Net model

• Non-constant deterioration rates• Models coating of metal element• Interventions is not effective after a

certain no. of times carried out• Model size is manageable• Possession schedule is taken into

account when carry our repair

Model outputs

• The expected maintenance cost

10 20 30 40 50 600

1000

2000

3000

Co

st (

k£)

Year

Strategy 3

0

20

40

60

80Cumulative cost (k£)

InspectionServicingComponent 1Component 2Component 3Component 4Component 5Cumulative cost

5 10 15 20 25 30 35 40 45 50 55 600

0.2

0.4

0.6

0.8

1

Year

Pro

ba

bili

ty

PETRI-NET MODEL - Concrete DeckProbability of being in different states over the lifetime

NewGoodPoorVery Poor

• Bridge future condition profile

Model comparison

0 10 20 30 40 50 600

0.2

0.4

0.6

0.8

1

Year

Pro

ba

bili

ty

MARKOV MODEL - Concrete DeckProbability of being in different states over the lifetime

NewGoodPoorVery Poor

5 10 15 20 25 30 35 40 45 50 55 600

0.2

0.4

0.6

0.8

1

Year

Pro

ba

bili

ty

PETRI-NET MODEL - Concrete DeckProbability of being in different states over the lifetime

NewGoodPoorVery Poor

Markov model

Petri-Net model

0 10 20 30 40 50 600

0.2

0.4

0.6

0.8

1

Year

Pro

ba

bili

ty

Component: Deck, Material: ConcreteProbability of being in a Very Poor condition

Petri-NetMarkovAverage

0 10 20 30 40 50 600

0.2

0.4

0.6

0.8

1

Year

Pro

ba

bili

ty

Component: Deck, Material: ConcreteProbability of being in a Good condition

Petri-NetMarkovAverage

Model comparison

0 10 20 30 40 50 600

0.2

0.4

0.6

0.8

1

Year

Pro

ba

bili

ty

Component: Deck, Material: ConcreteProbability of being in a New condition

Petri-NetMarkovAverage

0 10 20 30 40 50 600

0.2

0.4

0.6

0.8

1

Year

Pro

ba

bili

ty

Component: Deck, Material: ConcreteProbability of being in a Poor condition

Petri-NetMarkovAverage

Maintenance Strategy Optimisation

• Multi-objective Genetic Algorithm Optimisation• Find optimum maintenance strategy gives:

– Best condition profile– Lowest WLCC cost

• Model inputs (variables)– Repair strategy– Scheduling of maintenance (delay repair)– Inspection, servicing interval– Possession schedule

Optimisation Results

• Optimised maintenance strategies

0 2 4 6 8 10 12 14 16 18 201

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

Ave

rag

e C

on

diti

on

Optimum maintenance strategy ID

Optimum maintenance strategies

0 2 4 6 8 10 12 14 16 18 203

4

5

6

7

8

9

10

11x 10

4

WL

CC

Thank you for your time