Transcript Doing more with less in managing civil infrastructure

Professor Sujeeva Setunge
Head, Civil Engineering Discipline
School of Civil, Environmental and Chemical Engineering
RMIT University
Melbourne
Outline
• Life cycle of infrastructure
• Decision parameters
• Current challenges – doing more with less
• Research projects and outcomes
• A new project – would you like to join ?
Civil Environmental and Chemical Engineering
Plan
Demolish
Refurbish
Design
Life Cycle of
Civil
Infrastructure
Civil Environmental and Chemical Engineering
Maintain
Operate
Construct
Plan
Design
Demolish
Life Cycle of
Civil
Infrastructure
Refurbish
Civil Environmental and Chemical Engineering
Maintain
Operate
Construct
Decision Parameters
Operate
•
Sustainability
•
Climate change
•
Disaster resilience
•
Regulatory
compliance
•
Other ----
• Risk of failure
• Operating Cost
• Energy/water use
Maintain
• Refurbish or
demolish ?
• Best
Material/technique
• Cost
Civil Environmental and Chemical Engineering
Refurbish
• Timing & Method of
inspection,
• Maintenance
methods
• Cost
What is needed to “Do More with Less” ?
• Optimum timing and method of inspections – no more, no less
• Efficient use of inspection data
– Reactive maintenance decisions
– Proactive decision making –forecasting of deterioration
• Maintenance/capital works decisions
– Optimised for the available budget
– Budget required to provide minimum level of service
• Risk of failure
– Probability ? Consequences ?
– Mitigation or adaptation ?
Civil Environmental and Chemical Engineering
• New challenges
– Vulnerability under disasters, climate change
Knowledge gaps
• Forecasting deterioration of different infrastructure
– Using condition data
– Modelling exact mechanisms and reduction in capacity
• Likelihood of failure
– What happens if you do “nothing”
– Extreme events – flood, bush fire, earthquake, storm surge
– Climate change
• Consequences of failure
– Impact on the managing authority
– Impact on the community
Civil Environmental and Chemical Engineering
– Impact on other stakeholders
• Strengthening of Infrastructure
Methods of Deterioration Prediction
 Based on condition data
• Consecutive inspections of the same components
• At least two sets of good data required
• One set of data can be used as a snap shot, predictions can be
approximate
 Based on understanding of deterioration mechanisms
Examples
• Chloride induced corrosion of reinforced concrete structures
• Sulphate attack in sewers
• Carbonation of concrete structures
• Corrosion of steel
Further challenges
Civil Environmental and Chemical Engineering
Component level ?
Network level ?
Incorporating interdependencies of multiple assets ?
Community Buildings in Australia
• Project funded by Australian Research Council
• Six local councils and Municipal Association as partners
• Condition data collected by partners
• Deterioration forecasting and decision making models developed by
researchers
• Stochastic model based on Markov process is used for deterioration
prediction and risk estimation
• Integrated software tool developed by RMIT hosted in cloud, field
implementation at six local councils
www.assethub.com.au
Civil Environmental and Chemical Engineering
Simplified CAMS Workflow
Excel
Import
Excel
Import
Create building
component
hierarchy
Replacement
cost report
Upload
component
data
Display
buildings in
map using
geo
coordinates
Excel
Import
CAMS
Mobile
Upload level
of service
and
replacement
costs
Upload
condition
data
Deterioration
Prediction
Data explorer
Scenario
based risk
cost analysis
Civil Environmental and Chemical Engineering
RMIT University©2014
School of Civil, Environmental & Chemical
Engineering
Backlog
maintenance
Some Screenshots
Civil Environmental and Chemical Engineering
RMIT
School of Civil, Environmental &
CAMS Analytical Output
Data Explorer
Civil Environmental and Chemical Engineering
Civil Environmental and Chemical Engineering
CAMS Analytical Output
Scenario Based Backlog analysis – Backlog/Surplus
Civil Environmental and Chemical Engineering
CAMS Analytical Output
Scenario Based Analysis
Civil Environmental and Chemical Engineering
CAMS Analytical Output
Analysis of a selected building – Building Deterioration
Civil Environmental and Chemical Engineering
Technology
Based on Microsoft’s Web Applications Development Platform
– Microsoft .NET, SQL Server 2008
Hosted on Amazon Web Services in Sydney
– Best in class security, scalability and performance
Each CAMS account runs on a separate database
– Data segregation
Cloud based
– No hardware or special software required
Civil Environmental and Chemical Engineering
– New features and updates are immediately available for all users
– Runs on any compatible browser.
No installations required
RMIT
School of Civil, Environmental &
CAMS is available for implementation in interested
councils – we will upload data and configure the
system for your needs,
• Hands on training workshop scheduled in July 2015. – We
will communicate to LGs via MAV
• Training videos available in youtube
https://www.youtube.com/channel/UCey4F6BuCknHdDlxk
m2bj9w/playlists
• Please contact [email protected] if you are
interested in trying. Civil Environmental and Chemical Engineering
Civil Environmental and Chemical Engineering
Civil Environmental and Chemical Engineering
Civil Environmental and Chemical Engineering
Civil Environmental and Chemical Engineering
Deterioration modelling of bridges

Level 1- Routine
Maintenance Inspection

Level 3- Engineering
Investigation
Condition
1
2
3
4
Slab (8P) 70
15
10
5
Girder
(2P)
30
10
0
Level 2- Structure Condition
Inspection

Element
60
Deterioration curves of timber elements
BUILDINGS HIERARCHY
Condition
Condition
2.0
1.5
1.0
0.5
0.0
0
20
40
60
80
100
0
50
Age in years
Fig. A.1.Deterioration curve of pile
60
50
100
Age in years
80
Fig. A.3. Deterioration curve of Cross beam
Age vs Condition
Deck
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0
50
100
Fig. A.4. Deterioration curve of Deck
150
Age in years
Girder
150
Cross beam
Condition
Condition
Condition
0
150
Age vs condition
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
40
Age in years
100
Fig. A.2.Deterioration curve of Abutment
Age vs Condition
20
Age in years
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Abutment
Pile
0
Age vs C ondition
Age vs Condition
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Condition
Age VS Condition
2.5
Fig. A.5. Deterioration curve of Girder
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0
50
Age in years
100
150
Kerbs
Fig. A.6.Deterioration curve of Kerbs
Condition
Age vs Condition
Markov Process used for forecasting
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0
20
40
Age in years
Barriers
60
80
Fig. A.7. Deterioration curve of Railing
barriers
Non-linear optimisation to derive
the transition matrices
Effect of Climate Change on Seaports
• Project funded by National Climate Change
Adaptation Research Facility
• Failure mechanisms and related models adopted for
critical elements
• Climate change parameters established
• Changes needed to maintenance regimes identified
• Research into effect of change in sea salinity
commenced.
Civil Environmental and Chemical Engineering
Modelling climate system
• Components
• Interaction
• Human component
• 40 emission scenarios
• 23 global circulation models
• Selected two emissions scenarios
• Hotter/drier/most likely
RMIT University©2012
Civil, Environmental & Chemical Engineering
26
Start
Example: Carbonation of concrete
Define exposure and structural design
Input climate variables (T, RH, CO2) and
material properties
Next simulation run
Calculate carbonation penetration depth, xc(t)
Next simulation run
Next year step
NO
IF xc(t) > cover
NO
IF t=2100
YES
YES
Corrosion initiation and damage modelling
NO
IF (finished runs)
YES
Calculate statistics – mean depth, corrosion initiation & damage probability
RMIT University©2012
Civil, Environmental & Chemical Engineering
27
Outcome for Ports
Intervention
required
Deterioration
threshold
RMIT University©2012
Civil, Environmental & Chemical Engineering
28
USAid project – modelling of piles at Port
Suva
RMIT University August 2014
Sujeeva Setunge
29
The change in sea salinity on seaports
It is very likely that regions of the ocean with high salinity where evaporation
dominates have become more saline, while regions of low salinity where
precipitation dominates have become fresher since the 1950s.
This has been confirmed recently by the ARGO Global salinity program –
with over 3500 sensors floating worldwide
RMIT University August 2014
Sujeeva Setunge
30
Laboratory experiments to examine effect
of sea salinity on chloride ingress in
concrete
• Simulated environments varied salinity, humidity,
temperature, and concrete mix design
• Samples were taken at varying depths of concrete to see
how the environments changed the rate of ingress.
RMIT University August 2014
Sujeeva Setunge
31
Testing continued for six months
(Ph.D research – Andrew Hunting)
Chloride Content of high porosity vs. low
porosity
– notable chloride ingress into the
concrete down to depths of 20 mm
0.1000
HPLS Cabinet 010mm
Chloride content
– 38.6% increase in chloride content
in concrete
– 93% increase in penetration rate in
porous concrete
– Humidity increases ingress at the
beginning of tests
0.1200
0.0800
HPLS Cabinet 0-20mm
0.0600
HPLS Cabinet 2030mm
LPHS Cabinet 0-10mm
0.0400
LPHS Cabinet 1020mm
0.0200
LPHS Cabinet 2030mm
0.0000
0
RMIT University August 2014
Sujeeva Setunge
10
20
30
Salinity
40
50
32
Summary
• Developing capabilities to deliver “more with less”
requires addressing the problem from two directions
–Fundamental research to understand mechanisms
of degradation, accurate predictive modelling,
laboratory experiments and field trials to validate
–Top down approach to develop decision making
strategies based on limited data which can offer
immediate solutions to industry
• RMIT has developed a niche capability to cover both
Civil Environmental and Chemical Engineering
aspects
What’s new ?
Automated council tree inventory using airborne
LiDAR and aerial imagery
Airborne LiDAR and imagery
Individual tree detection
3D tree parameter extraction
Composition, structure and distribution over
council area: number of trees, tree density,
tree health, leaf area, and species diversity
Identify and examine the
underlying factors that affect
the growth and health of trees
Models for monitoring the
changing trend in local council
Location, height, canopy size and
extension and species composition
Spatially
enabled
trees
Civil Environmental
and
Chemical 3D
Engineering
Integration within council
GIS
Tree risk
assessment
Planning
……
Will deliver a cost effective tool to conduct tree census
Expected outcomes and deliverables
1) Develop and validate a new methodology to integrate airbone
LiDAR and aerial imagery for improved characterization of tree
canopy;
2) Extraction of geometric and physical parameters of individual tree,
including location, height, canopy size and extension and species
composition;
3) Deliver a cost effective tool to conduct tree census;
4) Identify and examine the underlying factors that affect the growth
and health of trees;
5) Validate the tool using existing data;
6) Disseminate the developed toolkit to the LG and offer training.
Civil Environmental and Chemical Engineering
If you like to join this new project, please let us know.
[email protected]
Centre for Pavement Excellence Asia Pacific
• Established by Brian O’Donnell, formerly from local govt. and EA forming a
consortium of RMIT/ARRB/EA/Latrobe University
• Aims to utilise federal govt. funding available as Aus-aid for Asia Pacific
countries, while delivering outcomes for local practitioners
• Will develop guidelines for improved stabilisation of unbound pavements
Civil Environmental and Chemical Engineering
Resilience of critical road structures – bridges,
floodways and culverts under natural hazards
Structures:
Hazards:
• BRIDGES
• CULVERTS
• FLOOD-WAYS
•
•
•
•
EARTHQUAKE
FLOOD
BUSHFIRE
CLIMATE CHANGE
Civil Environmental and Chemical Engineering
Enhancing Resilience of Critical Road Structures: Bridges,
Culverts and Flood Ways under Natural Hazards
Thank you