NORDIWA - 2013
Wastewater pipes in Oslo: from condition
monitoring to rehabilitation planning
R.M. Ugarelli, PhD, SINTEF, Norway, rita.ugarelli@sintef.no
I. Selseth, SINTEF, NORWAY
J. Røstum, PhD, SINTEF, NORWAY
Y. Le Gat, PhD, IRSTEA, FRANCE
A. H. Krogh, MSc., Oslo VAV, NORWAY
SINTEF Building and Infrastructure
Project motivation
Oslo waterworks reviewing the master
plan for wastewater networks
2015-2030
Research question
How much should Oslo VAV spend to
improve the structural conditions of the
network?
SINTEF Building and Infrastructure
What is the right rehabilitation rate to be
adopted?
2%?
Rehab rates in the Oslo wastewater network
0,7 % ?
To what level should Oslo
aim at?
Picture: VAV yearly report 2011
SINTEF Building and Infrastructure
Oslo Vann og Avløpsetaten (VAV)
NAME
%
AF
Combi ned s ewer
31
DR
Dra i na ge pi pe
0
KF
Cha nnel , combi ned s ewer
0
Nordkapp
Honningsvåg
Skáidi
Tana bru
Rásttigáisá
1
KS
Cha nnel , s ewer
0
OV
Stormwa ter
PS
Force ma i n s ewer (pumpi ng)
SP
Sewer
Trom s ø
Nordreisa
Karasjok
Finnsnes
Kautokeino
Risøyhamn
FINLAND
Bardufoss
Sortland
34
Stokmarknes
Svolvær
Narvik
Lødingen
Leknes
1
SVERIGE
Stamsund
Reine
Leinesfjorden
Værøy
Røst
Fauske
B odø
31
Other
Sørkjosen
Jiehkkevárri
1833 Skibotn
Gryllefjord
Andenes
Vadsø
Kirkenes
1067
Alta
Hars tad
Other
Lakselv
Skjervøy
RU
Cha nnel , s tormwa ter
Berlevåg
Båtsfjord
Vardø
Hasvik
Øksfjord
KO
Mehamn
Kjøllefjord
Hammerfest
SS
LA
ND
FCODE
Suliskongen
Sulitjelma
1907
Ørnes
Polarsirkelen
1
Mo i Rana
Oksskolten
Sandnessjøen
1915
Mosjøen
tota l number of pi pes 53264
Brønnøysund
Oslo
Trofors
Majavatn
tota l l enght 1960 km
Rørvik
Namsos
ma teri a l : 80% concrete
N
S teinkjer
TRONDHEIM
Courtes y, Os l o VAV
Levanger
NORGE
Orkanger
K ris tians und
Teg nforklaring
Molde
Sunndalsøra
Oppdal
Å les und
2286
Tynset
Str yn
Florø
Dombås
Lom
Galdhøpiggen
2469
Førde
Sogndal
Rondslottet
2178
Otta
2465
Glitter tind
Lilleham m er
Fagernes
Gjøvik
Hallingskarvet
BERGEN
Koppang
Årdal
Flåm
Voss
Geilo
0
0
Elverum
Gol
1933
Ham ar
Raufoss
50
100
50
150 Km
100 M iles
am
Kinsarvik
ste
rd
Am
Røros
Snøhetta
Volda
Lerwi
ck
le
cast
New
J ernbane
Riks veier
S tore byer
Mindre byer
S m åbyer
, tetts teder
Åndalsnes
Ørsta
Eidsvoll
Hønefos s
Odda
Kongsvinger
Leirvik
Rjukan
Hauges und
Haukeligrend
1881
Dram m en
K ongs berg
OSLO
Seljord
Valle
Horten Mos s
T øns berg
S arps borg
S kien
Fredriks tad
P ors grunn
S ande- Halden
Larvik fjord
S tavanger
tle
as
Newc
Sandnes
Tonstad
Evje
Egersund
Mandal
K ris tians and
SVERIGE
Fredrikshavn
Grimstad
Farsund
Harwich
Kragerø
Risør
Tvedestrand
A rendal
Flekkefjord
Hirts
hals
Kiel
DANM ARK
© Statens kartverk
SINTEF Building and Infrastructure
Methodology
Application of a model for wastewater pipes deterioration analysis to the
wastewater network of OSLO VAV as tool for knowledge based MASTER
PLAN and INVESTMENT PLAN
Activities
1. Data collection, filtering and adaption
2. Calibration of the deterioration models for the entire system
including all available inspection data
3. Prediction of future deterioration states for inspected and notinspected wastewater pipes of Oslo
4. Development of appropriate rehabilitation strategies to improve the
overall structural conditions of the system under budget constraints
5. Definition of long-term investment plans to achieve set goals of
improved system conditions.
SINTEF Building and Infrastructure
About the software used: GompitZ
GompitZ defines the relationship
between the current state and the
expected service time of sewer pipes
using Close Circuit TV inspections
(CCTV) as classification input.
The GompitZ deterioration modeling
tool has been delivered by IRSTEA
(Le Gat, 2008) within the framework
of the CARE-S FP5 project (Sægrov,
2006).
SINTEF Building and Infrastructure
Condition classes
•
The pipes have been classified in 5 condition classes
according to the structural conditions detected via visual
inspection (CCTV inspection) (1 = best)
•
In Norway pipes are classified according to the NORVARrapport 150/2007: Dataflyt - Klassifisering av
avløpsledninger (Dataflow. Classification of sewer pipes.)
Thanks to this project the standard have been reviewed
since we demonstrated that the scoring system applied is
too pessimistic.
•
SINTEF Building and Infrastructure
Modelling the deterioration process
Data (pipe ID level)
Understanding
the deterioration process:
- Influencing factors,
- Assumptions
- Previous "history" of the pipes
Calibration of the
Parameters influencing the
Deterioration process
of inspected pipes
External factors (soil, traffic, …)
Prediction of future
deterioration for
inspected and notinspected pipes
SINTEF Building and Infrastructure
Data (pipe ID level)
Pipes information and influencing factors
• Data are used for calibrating the deterioration model parameters to identify
which factors influence the current deterioration status and the speed of the
future deterioration process.
• Length, diameter, material, age at inspection and the condition class at the
inspection time are default covariates in the program interface, and each
have a column in the pipe data input file.
• The user can choose other «free» covariates as input for the model
SINTEF Building and Infrastructure
Data (pipe ID level)
Pipes inspections in Oslo
The inspections used for
model calibration were carried
out from 2002 to 2012.
8333 inspected pipes - of
which 7656 could be used to
calibrate the deterioration
models, i.e. without missing
data or inconsistencies
45000
40000
35000
30000
25000
5
4
20000
3
15000
2
10000
1
5000
0
SINTEF Building and Infrastructure
Calibration of the
Deterioration process
of inspected pipes
Calibration of models of deterioration
• Several combinations of covariates were tested and compared with respect
to log-likelihood and number of significant covariates.
• The deterioration of the network is modelled by calibrating separate models
for 4 categories of pipes
Group
BET
cBET
CULV
PIPE
Sum
# of pipes
22 416
4 286
1 189
19 416
47 307
Length [m]
884 489
163 530
47 766
733 111
1 828 896
Comment
Concrete pipes up to 600 mm
Concrete culverts from 600 mm
Other culverts from 600 mm
All other pipes materials up to 600 mm
SINTEF Building and Infrastructure
Example of calibrated model: model for
small concrete pipes (BET)
Calibration of the
Deterioration process
of inspected pipes
• The model BET is calibrated with 3783 inspections.
• Regarding the initial deterioration state:
– No relevant covariate was found to influence the initial deterioration state.
• Regarding the deterioration speed:
–
–
–
–
–
The smaller the diameter of the poorer condition
Combined (AF) is worse than OV and SP
Construction period 1946 - 1969 is sign of worst installation procedures
Pipes installed close to trees are more susceptible than others
Soils with marine soil or "rock" is accelerating the deterioration process
SINTEF Building and Infrastructure
Prediction of future
deterioration for
inspected and notinspected pipes
Prediction
Given the calibrated models, the next step has been the use of the same to
predict the future amount of pipes in each condition class if no rehabilitation
takes place ("do nothing scenario") and to analyse how different
rehabilitation scenarios can impact on the system condition.
3 possible methods for rehab strategy can be modelled with GompitZ:
• Rehab length
• Budget
• Optimized strategy
In Oslo the following predictions have been compared:
• Do-nothing 2013-2062
• Optimized strategies 2015-2030
SINTEF Building and Infrastructure
Do-nothing scenario 2013-2062 (50 years)
Prediction of future
deterioration for
inspected and notinspected pipes
Simulation of the evolution of the condition distribution in the absence of rehabilitation
(“do nothing” scenario) (example for the whole network)
SINTEF Building and Infrastructure
Do-nothing scenario 2013-2062 (50 years)
(Different groups)
Prediction of future
deterioration for
inspected and notinspected pipes
Group BET_1 (low consequences)
Group CULV_1 (low consequences)
Group cBET_1 (low consequences)
Group PIPE_1 (low consequences)
Group CULV_1 (low consequences)
SINTEF Building and Infrastructure
Rehabilitation costs
To compare the costs of the alternative
strategies with the benefit estimated as
improvement of the network condition a
detailed cost assessment was performed.
The tables show precise and average unit
costs of interventions to bring pipes from
class 2, 3, 4 or 5 back to class 1
CC
Cost from-to
Average cost [kr/m] in
2012 NOK
1
0
0
2
185
185
3
2500-8000
3000
4
2500-8000
3000
5
20000-30000
20000
20
SINTEF Building and Infrastructure
Objectives defined including a preliminary criticality
analysis
1 Water quality
2 Water quality
Location/type of pipe
Interceptor close to river;
Main sewer from a hospital
3 Customers
Pipes from vulnerable customers (hospital, nursing
home, museum, city hall)
4 Customers
Big pipes; storm water from 1000 mm and sewer
pipes from 600 mm
5 Infrastructure
pipes close to buildings (2 m)
6 Infrastructure
pipes close to road with high traffic
7 Infrastructure
pipes below granite paved streets
SINTEF Building and Infrastructure
Optimized scenarios
To identify the correct rehabilitation rate to be applied in the long term, four scenarios have been
tested and for each scenario two strategies have been applied to pipes with low and high
consequences in case of failure.
The predictions are done by setting stronger objective for pipes with high expected consequences
(CN>=3) than for pipes with lower or no consequences (CN<3).
Scenario
A1
A2
A3
A4
Consequence
Low
High
Low
High
Low
High
Low
High
Max portion of pipes in condition class
CC1 CC2 CC3
CC4
CC5
1.0
0.8
0.4
0.2
0.01
1.0
0.8
0.4
0.01
0.01
1.0
0.8
0.4
0.2
0.05
1.0
0.8
0.4 0.025
0.025
1.0
0.8
0.4
0.2
0.075
1.0
0.8
0.4 0.025
0.025
1.0
0.8
0.4
0.2
0.1
0.05
1.0
0.8
0.4
0.05
More ambitious
Less ambitious
+ A4 bis = A4 but all culvert considered @ high consequence
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Deterioration process
predicted (whole network)
Ca 2,5%
renovation needed
until 2030
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Rehabilitation rate
computed based on
objectives on pipes classes
About 1.5% renovation
required until 2030
A4 - Max 10 % in condition
class 5?:  1,5 % (average)
A1 - Max 1 % in condition
class 5?:  4,5 % (average)
About 4.5% renovation
required until 2030
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Summary of rehabilitation strategies
Scenario
A1
A2
A3
A4
A4b
Sum rehab
length in
2015-2030
1327.4
853.3
642.0
425.8
466.6
Average annual Sum rehab rate
rehab rate (NET) in period 20152015-2030
2030
4.5 %
2.9 %
2.2 %
1.5 %
1.6 %
73 %
47 %
35 %
23 %
26 %
Sum costs (MNOK, 2012
value) 2015- 2030
8075.7
6566.8
5639.3
4663.9
4875.0
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Conclusions
Four deterioration models have been calibrated and four (five) rehabilitation
strategy scenarios have been predicted
Importance to include risk aspects
The results have been included in the Oslo VAV master plan and the coming
risk based results will be included in the following rehabilitation plan.
Time to revise the Norwegian Standard 145 (2005) and 150 (2007)
Importance of data quality and procedures for data collection
SINTEF Building and Infrastructure
SMS from OSLO VAV - Secure and Monitored Service from Oslo
VAV project is funded by Oslo Vann og Avløp and the The research
Council of Norway.
The Project team:
• Arnhild Krogh, Oslo kommune, VAV
• Erik Gløersen, Oslo kommune, VAV
• Steinar Nilo, Oslo kommune, VAV
• Thomas Refsdal, Oslo kommune, VAV
• Bjørn Christoffensen, Oslo kommune, VAV
• Hallvard Oen, Oslo kommune, VAV
• Ingrid Selseth, SINTEF
• Yves Le Gat, Irstea (F)
• Jon Røstum, SINTEF
SINTEF Building and Infrastructure
So what was the rate selected by Oslo?
1,6%
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Technology for a better society
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