Dr Boulent Imam presented a seminar titled "Risk-based bridge assessment under changing load-demand and environmental conditions" as part of the SMART Seminar Series on 17th July 2018. More information: https://news.eis.uow.edu.au/event/risk-based-bridge-assessment-under-changing-load-demand-and-environmental-conditions/ Keep updated with future events: http://www.uoweis.co/events/category/smart-infrastructure-facility/

1. Risk-based Bridge Assessment Under Changing
Load-Demand and Environmental Conditions
Dr Boulent Imam
Department of Civil and Environmental Engineering
University of Surrey
b.imam@surrey.ac.uk
SMART Seminar Series

2. • Introduction on metallic railway bridges
• Fatigue analysis of riveted railway bridges
- Load modelling (current, past & future)
- Finite element analysis
- Probabilistic/reliability analysis
• Long-term deterioration of metallic bridges
- Long-term material deterioration
- Impact of changing environmental conditions
• Scour analysis of bridges
- Climate change effects
Contents

3. Introduction
• Degradation through corrosion and fatigue
• Large number of aged railway bridges in UK (> 15,000)
and Europe (> 30,000)
• Heavily utilised networks – replacement is impossible
• Improved assessment methods & repair/strengthening
techniques are sought
• Infinite life assets!
• Meeting asset management objectives for next
generation of transport infrastructure

4. 1840 1848
Railway network in the UK
Introduction

5. Introduction
Cast iron
bridges

6. Introduction
Wrought
iron
bridges

7. Introduction
Steel
bridges

8. Introduction

9. Introduction
Ageing &
Deterioration
Inter-dependencies
Are assets
safe?
If so, for
how long?
At what
cost?

10. Fatigue analysis of riveted
railway bridges

11. Metallic railway bridges
Age
<20 yrs 20-50 yrs 50-100 yrs >100 yrs
Material
Cast Iron Wrought Iron Steel
Span
<10 m 10-40 m >40 m
Total number in UK: 16000

12. Motivation of research
• Riveted bridges may be close to or have exceeded their fatigue lives
• Able to cope with current load demands
• Unusual material – wrought iron
• Cracks have been discovered in riveted connections (in many cases
hidden)
• More reliable fatigue assessment methodology
• Better (optimal) decision making

13. Fatigue damage

14. Fatigue damage
Riveted connections

15. Typical riveted bridge
Cross-
girder
Stringer

16. Fatigue analysis
Global bridge modelling vs. Local connection modelling

17. Fatigue analysis
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
S7-S5
S8-S6
S3-S5
S4-S6
S6-S8
S5-S7
S7-S9
S8-S10
S3-S1
S6-S4
S4-S2
S5-S3
S2
S1
S10
S9
Totalfatiguedamage
Connection
Modified Class B Class WI Class D
Fatigue ranking of connections (global vs. local)
0.0E+00
5.0E-05
1.0E-04
1.5E-04
2.0E-04
2.5E-04
3.0E-04
3.5E-04
4.0E-04
Hole 4 Hole 5 Hole 1 Hole 2 Angle Fillet Hole 3 Rivet 3 Rivet 2 Rivet 1 Rivet 5 Rivet 4
Singletrainfatiguedamage
Connection region
50 MPa 100 MPa 150 MPa 200 MPa
Global Local
Identify most critical
connections in bridge
Identify most critical regions
in connection itself
Inspection
planning

18. Global analysis

19. Past, present and future …
• Understand the past
• Evaluate the present
• Predict the future
Stress
TimePresent
Past Future
TimePresent
Accumulated
damage ?
Future
Cumulative
fatigue damage
Failure
? Remaining life

20. Railway loading (past)
Freight (1900-1940)
Freight (1940-1970)
Passenger (1920-1970) Suburban (1900-1970)Passenger (1900-1920)

21. Railway loading
Historical
(1900-1970)
BS 5400
(1970- )

22. Probabilistic Fatigue Life Estimates for Riveted Railway Bridges
B.M. Imam, T.D. Righiniotis, M.K. Chryssanthopoulos & B. Bell
Railway loading
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
0 20 40 60 80 100 120 140 160 180
Stress(MPa)
Load step
Engine 1st wagon

23. Fatigue damage

24. Fatigue damage
Cumulative Damage of Connections S7-S5 & S8-S6
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1900 1920 1940 1960 1980 2000 2020 2040
Year
CumulativeDamage
S7-S5 (Class B) S8-S6 (Class B) S7-S5 (Class D) S8-S6 (Class D) S7-S5 (Class WI) S8-S6 (Class WI)
80 yrs
85 yrs
120 yrs
128 yrs
289 yrs
303 yrs
Modified
Class B
Class WI
Class D

25. Fatigue damage
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1900 1920 1940 1960 1980 2000 2020
CumulativeDamage
Year
Cumulative damage of connection S7-S5 (Class WI)
No DAF Byers EC1 Tobias & Foutch D23 Network Rail
21-31 yrs
120 yrs
No DAF
With DAF

26. Connection ranking
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40 S7-S5
S8-S6
S3-S5
S4-S6
S6-S8
S5-S7
S7-S9
S8-S10
S3-S1
S6-S4
S4-S2
S5-S3
S2
S1
S10
S9
Totaldamage
Connection
Modified Class B Class WI Class D
80yrs
85yrs
289yrs120yrs
128yrs

27. Probabilistic fatigue analysis
•Loading Uncertainties
Dynamic amplification factor (DAF)
Annual train frequency (fti )
•Material Uncertainties
S-N curve (fatigue resistance behaviour)
Damage index Δ in Miner’s sum (fatigue failure limit)
•Model Uncertainties
Factor  accounting for the differences between measured and
calculated stresses in steel bridges

28. 0.0E+00
1.0E+05
2.0E+05
3.0E+05
4.0E+05
5.0E+05
6.0E+05
7.0E+05
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100
Stress range (MPa)
Numberofappliedcycles
Modified Class B
fatigue limit
Class WI
fatigue limit
Class D
fatigue limit
Mean = 5.79 MPa
CoV = 1.09
Weibull distribution parameters
η=6.02 , β=1.0
0.0E+00
1.0E+05
2.0E+05
3.0E+05
4.0E+05
5.0E+05
6.0E+05
7.0E+05
8.0E+05
9.0E+05
1.0E+06
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100
Stress range (MPa)
Numberofappliedcycles
Modified Class B
fatigue limit
Class WI
fatigue limit
Class D
fatigue limit
Mean = 6.75 MPa
CoV = 0.84
Weibull distribution parameters
η=5.05 , β=0.98
0.0E+00
1.0E+05
2.0E+05
3.0E+05
4.0E+05
5.0E+05
6.0E+05
7.0E+05
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100
Stress range (MPa)
Numberofappliedcycles
Modified Class B
fatigue limit
Class WI
fatigue limit
Class D
fatigue limit
Mean = 8.91 MPa
CoV = 0.64
Weibull distribution parameters
η=4.35 , β=0.90
0.0E+00
5.0E+04
1.0E+05
1.5E+05
2.0E+05
2.5E+05
3.0E+05
3.5E+05
4.0E+05
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100
Stress range (MPa)
Numberofappliedcycles
Modified Class B
fatigue limit
Class WI
fatigue limit
Class D
fatigue limit
Mean = 12.6 MPa
CoV = 0.75
Weibull distribution parameters
η=15.0 , β=1.60
Period 1900-1920
Probabilistic fatigue analysis
Period 1920-1940
Period 1940-1970 Period 1970-

29. Probabilistic fatigue analysis
0.000001
0.00001
0.0001
0.001
0.01
0.1
1
0 50 100 150 200 250 300 350 400
ProbabilityoffailurePf
Time after 2005 (years)
μ = 685 years
sdev = 411 years
μ = 515 years
sdev = 304 years
No load evolution
Increase in train frequencies
Increase in train
frequencies & axle
weights
μ = 242 years
sdev = 133 years
6,000 similar
bridges
100,000 similar
connections
Very high standard deviations
Intensified inspection,
monitoring and repair
plans for old bridges

30. Global analysis

31. Refined Assessment

32. Refined Assessment

33. Refined Assessment
• Influence of:
• Rivet clamping force
• Friction between connection elements
• Most highly stressed parts of the connection
itself
• Different damage scenarios (cracking, loss of
rivet clamping force, loss of rivets)

34. Refined Assessment
0
1
2
3
4
Hole 1 Hole 2 Hole 3 Hole 4 Hole 5 Rivet 1 Rivet 2 Rivet 3 Rivet 4 Rivet 5 Angle
Fillet
Dl/Dg
100 MPa 200 MPa
Global model
damage
>100yrs
>100yrs
>100yrs
>100yrs
>100yrs
>150yrs
>150yrs
>150yrs
7yrs
12yrs
81yrs
30yrs
84yrs
39yrs
73yrs
73yrs

35. Refined Assessment

36. System analysis

37. System analysis

38. System analysis

39. Main findings
• Inner stringer-to-cross-girder connections are the most fatigue
critical
• Significant increase in the damage accumulation rate during the last
few decades
• Very high standard deviations in fatigue life → high uncertainty in
fatigue evaluation procedures → improvement of assessment
procedures unlikely → importance of inspection and management
plans
• Some connections approaching the end of their service life → given
their very large number in the bridge network → adopting timely
management of repairs and replacement
• Traditional code methods can underestimate fatigue damage in
some cases by a factor of 3

40. Long-term deterioration of
metallic bridges

41. 41
Material corrosion
Level Description Comments
1
Empirical models. Effect of all influencing
factors taken into account through model
constants
Model coefficients are associated with very high
uncertainty. Statistical properties may not be reliable due
to inhomogeneous samples. Questionable model
transferability.
2
Empirical models which relate the rate of
corrosion to specific exposure variables, also
known as dose response functions (DRF).
Reliance on spatial data for atmospheric pollutants and
climatic parameters. Potentially suitable for probabilistic
analysis, though uncertainty modelling largely untested.
3
Theoretical models involving simulation
techniques to predict airflow patterns and
pollutant mass transfer on exposed surfaces.
Heavy reliance on modelling assumptions. Complex
uncertainty modelling, currently lack of input data at
desired granularity level.
B
AttC )(
   0T2
1
SOTOW Cl
1 1
D F H
J TB
C t At e
C E G
     
      
   
Level 1:
Level 2:

42. 42
Material corrosion
Corrosivity
category
Description Corrosion rates 1
(mm/year)
C1 Very low corrosivity: Dry or cold zone, atmospheric environment with
very low pollution and time of wetness, e.g. certain deserts, Central
Arctic/Antarctica.
≤ 0.0013
C2 Low corrosivity: Temperate zone, atmospheric environment with low
pollution (SO2 < 5 μg/m3
), e.g. rural areas, small towns. Dry or cold
zone, atmospheric environment with short time of wetness, e.g. deserts,
subarctic areas.
0.0013 < A ≤ 0.025
C3 Medium corrosivity: Temperate zone, atmospheric environment with
medium pollution (SO2: 5 μg/m2
to 30 μg/m3
) or some effect of
chlorides, e.g. urban areas, coastal areas with low deposition of
chlorides. Subtropical and tropical zone, atmosphere with low pollution.
0.025 < A ≤ 0.050
C4 High corrosivity: Temperate zone, atmospheric environment with high
pollution (SO2: 30 μg/m3
to 90 μg/m3
) or substantial effect of chlorides,
e.g. polluted urban areas, industrial areas, coastal areas without spray of
salt water or, exposure to effect of de-icing salts. Subtropical and tropical
zone, atmosphere with medium pollution.
0.050 < A ≤ 0.080
C5 Very high corrosivity: Temperate and subtropical zone, atmospheric
environment with very high pollution (SO2: 90 μg/m3
to 250 μg/m3
)
and/or significant effect of chlorides, e.g. industrial areas, coastal areas,
sheltered positions on coastline.
0.080 < A ≤ 0.200
CX Extreme corrosivity: Subtropical and tropical zone (very high time of
wetness), atmospheric environment with high SO2 pollution (higher than
250 μg/m3
) including accompanying and production factors and/or
strong effect of chlorides, e.g. extreme industrial areas, coastal and
offshore areas, occasional contact with salt spray.
0.200 < A ≤ 0.700
Notes: 1
Corrosion rates correspond to the first year of exposure.
1

43. Changing environmental conditions
10 11 12 13 14 15 16 17 18 19
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Air temperature (oC)
PDF
UKCP09 simulation: period 2010-2039
Normal fit (mean = 11.67, SD = 0.52)
UKCP09 simulation: period 2070-2099
Normal fit (mean = 13.08, SD = 0.95)
10 11 12 13 14 15 16 17 18 19
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Air temperature (oC)
PDF
UKCP09 simulation: period 2010-2039
Normal fit (mean = 11.65, SD = 0.51)
UKCP09 simulation: period 2070-2099
Normal fit (mean = 14.68, SD = 1.33)
Low emissions scenario High emissions scenario
UKCP09 – UK Climate Projections Database

44. Changing environmental conditions
0
2
4
6
8
10
12
0 15 30 45 60 75 90
Time (years)
Corrosionloss(mm)
TOW = 5500h/year
TOW = 4000h/year
TOW = 2500h/year
SO2 = 60 μg/m3
Cl = 0 mg/m2
/day
T = 12 o
C
0
2
4
6
8
10
12
0 15 30 45 60 75 90
Time (years)
Corrosionloss(mm)
Cl = 300 mg/m2/day
Cl = 60 mg/m2/day
Cl = 0 mg/m2/day
SO2 = 60 μg/m3
TOW = 4000 h/year
T = 12 o
C
0
2
4
6
8
10
12
0 15 30 45 60 75 90
Time (years)
Corrosionloss(mm)
Sulphur dioxide = 250 μg/m3
Sulphur dioxide = 90 μg/m3
Sulphur dioxide = 30 μg/m3
Cl = 0 mg/m2
/day
TOW = 4000 h/year
T = 12 o
C
0
2
4
6
8
10
12
0 15 30 45 60 75 90
Time (years)
Corrosionloss(mm)
T = 17 oC
T = 15 oC
T = 12 oC
SO2 = 60 μg/m3
Cl = 60 mg/m2
/day
TOW = 4000 h/year
Time-of-
wetness
(TOW)
Cl
deposition
rate
SO2
concentration
Temperature

45. Changing exposure conditions
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0 10 20 30 40 50 60
Corrosionloss(mm)
Time (years)
Constant exposure conditions
With sharp SO2 change at t=20 years
with gradual SO2 change over a 20-year period

46. Case study

47. Material corrosion – Probability of
failure
Effect of temperature
1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
2012 2032 2052 2072 2092
Year
Cumulativeprobabilityd
offlexuralfailure,pf(0,t)
S2
S3
S4
S5
1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
2012 2032 2052 2072 2092
Year
Cumulativeprobabilityd
offlexuralfailure,pf(0,t)
S1
S5
S6
S7
1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
2012 2032 2052 2072 2092
Year
Cumulativeprobabilityofd
flexuralfailure,pf(0,t)
S5
S8
Effect of SO2
Effect of TOW

48. Material corrosion - Risk
Risk of failure = probability of failure × consequences of failure
   tCtptR ff )(
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
2012 2032 2052 2072 2092
Year
Time-dependentrisk(GBP,£)
S1
S5
S6
S7

49. Network-based risk
Consequences Examples
Human Fatalities
Injuries
Economic Reconstruction cost
Repair costs
Loss of functionality/downtime
Traffic delay / re-routing costs
…..
Environmental CO2 Emissions
Energy use
Pollutant releases
Societal Loss of reputation / public
confidence
Changes in professional practice
Loss of business
Often practical to express all consequences in terms of monetary units

50. Consequences of failure
• System boundaries
– Structural domain (structural
system itself)
– Spatial domain (transportation
network)
• Extent of spatial domain
– Single route with diversions
– Wider network (redundancy)
• Further layers can be added
(environment, society, …)

51. Human consequences
• Fatalities and / or injuries
• Highly variable in terms of predicting & valuing
• Valuation of human life
- UK DfT: £1.43 million for road fatalities (2005 prices)
- EU: €1.5 million for road fatalities
- RSSB: £3.46 million for rail fatalities (2003 prices)
- HSE: £1 million for fatality (2001 prices)
• Encompass direct human and economic loss i.e. loss of
output, medical costs, amount to reflect pain & grief

52. Economic consequences
• Reconstruction time:
- highway bridges: mean=230 days, st.dev.=110 days
- railway bridges: mean=110 days, st.dev.=73 days
• These can be used to estimate traffic delay costs
• Debris clean up costs:
- transportation of failed material
- number of trucks, capacities, distance to disposal site, fuel
consumption

53. Economic consequences
• UK Highways Agency:
- £9.30/hour (2002 prices) for average vehicle
• U.S. Department of Transportation
- $8.90/person-hour for local travel
- $12.20/person-hour for intercity travel (in 1997 prices)
- $16.50/person-hour for trucks
• EU countries (in 1998 prices)
Passenger Transport Freight Transport
Car
Business: €21.00/person-hour
Commuting/Private: €6.00/person-hour
Leisure/Holiday: €4.00/person-hour
Light Goods Vehicle: €40.0/vehicle-hour
Light Goods Vehicle: €43.0/vehicle-hour
Interurban Rail
Business: €21.00/person-hour
Commuting/Private: €6.40/person-hour
Leisure/Holiday: €3.20/person-hour
Full train load (950 tonnes): €725.0/tonne-hour
Wagon load (40 tonnes): €30.0/tonne-hour
Average per tonne: €0.76/tonne-hour

54. Economic consequences
• Traffic management costs in case of bridge repairs:
- over or under the bridge
- selection of scheme depends on traffic volume and road type
Carriageway closure /
full contraflow
One-lane closure Two-lane closure
Motorway
£850 (1 km TM scheme) £350 £450
£1250 (3 km TM scheme)
Dual
carriageway
£500 £350 £450
Single
carriageway
£800 (traffic signal control
management)
£300

55. Economic consequences
• Consequences on business
• Disruption of normal business activities
• Delays on customers, deliveries, suppliers
• Loss of business, increased production costs etc.
• Economic expertise is required

56. Economic consequences
• Infrastructure interdependencies
- bridges can be part of electricity, telephone, water, gas
networks

57. Environmental consequences
• Carbon emissions from production of bridge materials
Material Carbon emitted
Steel 1820 Kg CO2/te
Cement 800 Kg CO2/te
Reinforced Concrete 260-450 Kg CO2/te
Asphalt 46 Kg CO2/te

58. Environmental consequences
• Emissions from traffic related sources
Vehicle type CO2 emissions
Petrol car 0.1730-0.2994 kg CO2 / passenger km
Diesel car 0.1452-0.2455 kg CO2 / passenger km
Hybrid car 0.1191-0.2173 kg CO2 / passenger km
Light commercial van (petrol) 0.1941-0.2558 kg CO2 / vehicle km
Light commercial van (diesel) 0.1571-0.2691 kg CO2 / vehicle km
Heavy goods vehicle (diesel) 0.5276-1.163 kg CO2 / vehicle km
Rail (passenger) 0.05340 kg CO2 / vehicle km
Rail (freight) 0.02850 kg CO2 / tonne km

59. Environmental consequences
• Air / Noise pollution
• Number of affected households
• PM10 pollution; NOx pollution
• ~ €135/household/1μg/m³ for PM10
• ~ €1,300/tonne for NOx
• Different noise severity levels
• €40/household for 50 decibels
• €165/household for 75 decibels.

60. Bridge failure consequences
Consequence type
Costs
(€ millions)
Percentage (%)
Fatality/Casualty costs €393.8 28.7
Traffic delay/ Detour costs €844.3 61.5
CO2 emission costs €7.2 0.52
Noise pollution costs €5.4 0.39
Air quality costs €59.8 4.36
Traffic management costs €0.32 0.02
Reconstruction costs €62.0 4.52
Total Costs €1,372.8 100.0

61. Scour Analysis of Bridges

62. Bridge scour
• Bridge scour is the most common cause for bridge failure!
• Most prediction methods are empirical
• Sources of uncertainty
 River/flow characteristics
 Effect of changing environmental conditions (climate change)
 Unknown foundation depths
 Empirical models
• Framework for scour reliability assessment under changing
conditions

63. Bridge scour

64. Bridge scour

65. Low flow Normal flow Extreme flow
More record
extreme flow
Statistical variability of climate change
Bridge scour & Climate change

66. • Scour assessment based on max annual flow, Q, with return period T=2 years
• Q obtained from annual maxima series of pooling group flood data (FEH)
• Expected annual flow best described by Generalised Extreme Value (GEV)
distribution
• Flow events in different years assumed to be independent
• Parametric study to quantify changing conditions through changes in distribution
parameters
Bridge scour & Climate change

67. Random variables
Variables Mean COV Distribution Reference
River
Width B (m) 65 0.05 Normal Assumed
Streambed conditions (K3) 1.1 0.05 Uniform
NCHRP
(2003)
Bed material size (K4) 1.0 - Deterministic Assumed
Slope s 0.0032 0.05 Lognormal Assumed
Manning’s coefficient n 0.035 0.28 Lognormal
NCHRP
(2003)
Bridge
piers
Foundation depth DF (m) 4.5 - Deterministic Assumed
Pier nose shape (K1) 1.0 - Deterministic Assumed
Angle of attack (K2) 1.0 - Deterministic Assumed
Pier width, D (m) 2 0.05 Normal Assumed
Scour
eqn.
Modelling uncertainty, λsc 0.55 0.52
Normal/
lognormal
NCHRP
(2003)
Bridge scour – Case Study

68. Parametric analyses: 20%, 40% and 60% increases over a 60-year period
A: Annual; C: Cumulative probabilities of failure
Effect of river flow characteristics
Bridge scour – Case Study

69. Effect of foundation depth
Bridge scour – Case Study

70. Effect of scour modelling uncertainty (FD = 5m)
Bridge scour – Case Study

71. Effect of asset data uncertainties
Bridge scour – Case Study

72. Concluding remarks
• Better understanding of fatigue and deterioration – more effective
asset management
• Still a lot to learn from the first infrastructure asset population –
invaluable to next generation of structures
• Utilising the best out of our assets’ service
• Environmental sensitivity becoming more and more important
• Climate change uncertainty is often quoted as a major barrier for
adaptation. However, in some cases be overshadowed by other
modelling and asset uncertainties, which can be reduced
• Understanding uncertainties and variabilities is key to effective
risk assessment

73. Key publications
• Imam B.M., Righiniotis T.D., Chryssanthopoulos M.K. (2007). Numerical modelling of riveted railway bridge
connections for fatigue evaluation. Engineering Structures, 29(11): 3071-3081.
• Imam B.M., Righiniotis T.D., Chryssanthopoulos M.K. (2008). Probabilistic fatigue evaluation of riveted railway bridges.
Journal of Bridge Engineering (ASCE), 13(3): 237-244.
• Righiniotis T.D., Imam B.M., Chryssanthopoulos M.K. (2008). Fatigue analysis of riveted railway bridge connections
using the theory of critical distances. Engineering Structures, 30(10): 2707-2715.
• Imam B.M., Righiniotis T.D. (2010). Fatigue evaluation of riveted railway bridges through global and local analysis.
Journal of Constructional Steel Research, 66(11): 1411-1421.
• Imam B.M., Chryssanthopoulos M.K, Frangopol D.M. (2012). Fatigue system reliability analysis of riveted railway
bridge connections. Structure and Infrastructure Engineering, 8(10), 967-984.
• Imam B.M., Chryssanthopoulos M.K. (2012). Causes and consequences of metallic bridge failures. Structural
Engineering International, 22(1): 93-98.
• Kumar P., Imam B.M. (2013). Footprints of air pollution and changing environment on the sustainability of built
infrastructure. Science of the Total Environment, 444, 85-101.
• Kallias A.N., Imam B. (2015). Probabilistic Assessment of Local Scour in Bridge Piers under Changing Environmental
Conditions. Structure and Infrastructure Engineering, 12(9): 1228- 1241.
• Kallias A.N., Imam B.M., Chryssanthopoulos M.K. (2016). Performance profiles of metallic bridges subject to coating
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