professor sujeeva setunge head, civil engineering discipline school of civil, environmental and...

Doing more with less in managing civil infrastructure: Current challenges and knowledge required for

optimised and sustainable decisions

Professor Sujeeva Setunge

Head, Civil Engineering Discipline

School of Civil, Environmental and Chemical EngineeringRMIT UniversityMelbourne

Civil Environmental and Chemical Engineering

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 ?


Plan

Design

Construct

OperateMaintain

Refurbish

Demolish

Life Cycle of Civil Infrastructure


Operate• Risk of failure• Operating Cost• Energy/water use

Maintain

• Timing & Method of inspection,

• Maintenance methods

• Cost

Refurbish

• Refurbish or demolish ?

• Best Material/technique

• Cost

• Sustainability

• Climate change

• Disaster resilience

• Regulatory compliance

• Other ----

Resources constraints

Decision Parameters


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 ?

• 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

– 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 mechanismsExamples

• Chloride induced corrosion of reinforced concrete structures• Sulphate attack in sewers• Carbonation of concrete structures• Corrosion of steel

Further challengesComponent 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

http://www.assethub.com.au/


Simplified CAMS Workflow

Create building component hierarchy

Upload component

data

Upload condition

data

Replacement cost report

Deterioration Prediction

CAMS Mobile

Excel Import

RMIT University©2014 School of Civil, Environmental &

Chemical Engineering

Upload level of

service and replacemen

t costs

Excel Import

Display buildings in map using

geo coordinates

Data explorerScenario based risk cost analysis

Backlog maintenance

Excel Import


Some Screenshots

RMIT University©2014

School of Civil, Environmental & Chemical Engineering


CAMS Analytical OutputData Explorer


CAMS Analytical OutputScenario Based Backlog analysis – Backlog/Surplus


CAMS Analytical OutputScenario Based Analysis


CAMS Analytical OutputAnalysis of a selected building – Building Deterioration


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– New features and updates are immediately available for all users– Runs on any compatible browser.

No installations required

RMIT University©2014

School of Civil, Environmental & Chemical Engineering


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/UCey4F6BuCknHdDlxkm2bj9w/playlists

• Please contact [email protected] if you are interested in trying.

https://www.youtube.com/channel/UCey4F6BuCknHdDlxkm2bj9w/playlists

https://www.youtube.com/channel/UCey4F6BuCknHdDlxkm2bj9w/playlists

mailto:[email protected]

Deterioration modelling of bridges

Level 1- Routine

Maintenance Inspection

Level 2- Structure Condition

Inspection

Level 3- Engineering

Investigation

Element Condition

1 2 3 4

Slab (8P) 70 15 10 5

Girder (2P)

60 30 10 0

BUILDINGS HIERARCHYDeterioration curves of timber elements

Fig. A.1.Deterioration curve of pile Fig. A.2.Deterioration curve of Abutment Fig. A.3. Deterioration curve of Cross beam

Fig. A.4. Deterioration curve of Deck Fig. A.5. Deterioration curve of Girder Fig. A.6.Deterioration curve of Kerbs

Fig. A.7. Deterioration curve of Railing barriers

0.0

0.5

1.0

1.5

2.0

2.5

0 20 40 60 80 100

Age in years

Con

ditio

n

Age VS Condition

0.00.5

1.01.52.0

2.53.0

3.54.0

0 50 100 150Age in years

Con

ditio

n

Age vs Condition

0.00.51.01.52.0

2.53.03.54.0

0 50 100 150

Age in years

Conditi

on

Age vs Condition

0.00.51.01.52.02.53.03.54.0

0 20 40 60 80Age in years

Con

ditio

n

Age vs Condition

0.00.51.01.52.02.53.03.54.0


Con

ditio

n

Age vs Condition

0.00.51.01.52.02.53.03.54.0


Con

ditio

n

Age vs condition

0.00.51.01.52.02.53.03.54.0

0 20 40 60 80Age in years

Con

ditio

n

Age vs Condition

PileAbutment Cross beam

DeckGirder

Kerbs

Barriers

Markov Process used for forecasting

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.

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

Example: Carbonation of concrete


Start

Define exposure and structural design

Input climate variables (T, RH, CO2) and material properties

Calculate carbonation penetration depth, xc(t)

IF xc(t) > cover IF t=2100

NO

YES

Next simulation run

Next year step

Corrosion initiation and damage modelling

IF (finished runs)

Nex

t sim

ulati

on ru

n

NO

Calculate statistics – mean depth, corrosion initiation & damage probability

NO

YES

YES

Outcome for Ports


Intervention required

Deterioration threshold

USAid project – modelling of piles at Port Suva

29RMIT University August 2014 Sujeeva Setunge

RMIT University August 2014 Sujeeva Setunge 30

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

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.

31RMIT University August 2014 Sujeeva Setunge

Testing continued for six months(Ph.D research – Andrew Hunting)

– notable chloride ingress into the concrete down to depths of 20 mm

– 38.6% increase in chloride content in concrete

– 93% increase in penetration rate in porous concrete

– Humidity increases ingress at the beginning of tests

32

5 10 15 20 25 30 35 40 450.0000

0.0200

0.0400

0.0600

0.0800

0.1000

0.1200

Chloride Content of high porosity vs. low porosity

HPLS Cabinet 0-10mm

HPLS Cabinet 0-20mm

HPLS Cabinet 20-30mm

LPHS Cabinet 0-10mm

LPHS Cabinet 10-20mm

LPHS Cabinet 20-30mm

Salinity

Ch

lori

de

con

ten

t

RMIT University August 2014 Sujeeva Setunge


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 aspects

What’s new ?


Automated council tree inventory using airborne LiDAR and aerial imagery

Airborne LiDAR and imagery

Individual tree detection3D tree parameter extraction

Composition, structure and distribution over council area: number of trees, tree density, tree health, leaf area, and species diversity

Location, height, canopy size and extension and species composition

Spatially enabled 3D treesIntegration within council

GIS

Identify and examine the underlying factors that affect

the growth and health of trees

Models for monitoring the changing trend in local council

Tree risk assessment

Planning… …

Will deliver a cost effective tool to conduct tree census


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.

Expected outcomes and deliverables

If you like to join this new project, please let us [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


Resilience of critical road structures – bridges, floodways and culverts under natural hazards

Structures:

• BRIDGES• CULVERTS• FLOOD-

WAYS

Hazards:

• EARTHQUAKE• FLOOD• BUSHFIRE• CLIMATE

CHANGE

Enhancing Resilience of Critical Road Structures: Bridges, Culverts and Flood Ways under Natural Hazards

Thank you

professor sujeeva setunge head, civil engineering discipline school of civil, environmental and...

Documents

climate change slide

partners condition data

set of data

sets of good data

challenges component

community impact

maintenance methods

local councils