Schedule contingency analysis for transit projects using a simulation approach.
Gurgun, Asli Pelin ; Zhang, Ye ; Touran, Ali 等
Introduction
Schedule delays and cost overruns are challenging for large-scale
construction projects with long durations. Uncertainties embedded in
such projects affect both schedule and cost (Tseng et al. 2009). Project
delays are common in practice and the amount of delay vary with the
nature of the project. In construction, delay could be defined as the
time overrun either beyond completion date specified in a contract, or
beyond the date that parties agreed upon delivery of a project (Assaf,
Al-Hejji 2006). The delays are usually accompanied by cost overruns and
the problem is experienced by both developed and developing countries
(Kaliba et al. 2009). Cost overrun and time overrun generally result
from factors that occur at various phases of the project life cycle
(Bhargava et al. 2010). Delays in construction projects are a universal
phenomenon and develop slowly during the course of the work (Ahmed et
al. 2003). Causes of delay in large construction projects, average of
time overrun is between 10% and 30% of original duration (Assaf,
Al-Hejji 2006).
It is clear that the complex nature and immense size of the
large-scale projects require effective planning (Capka 2004). Project
managers should know the probability of time overrun in order to take
necessary corrective actions. One obvious planning approach is to use
this information to include sufficient contingency for the project
schedule. Other corrective actions may include but not limited to a
change of project delivery method (Design Build vs Design Bid Build,
etc.), use of new equipment or technology, redrafting dispute resolution
procedures and expediting construction permits. Therefore, a distinct
need has emerged to develop facilitated methods for evaluating the
probability of construction time overruns (Luu et al. 2009).
The causes of undesired growths in schedule and cost have attracted
construction management researchers worldwide and many reports and
research studies can be found in the literature. The issue of optimism
bias in organizational dynamics in construction and concluded that it is
imperative to have explicit and systematic evaluation methods to achieve
large-scale projects' objectives (Son, Rojas 2011).
[FIGURE 1 OMITTED]
Transportation projects are typical candidates that deserve
thorough investigations for possible reasons of both schedule and cost
growths. This twofold issue has been investigated at some depth (Bakshi,
Touran 2009). It has been shown that there are many reasons for schedule
delays and cost overruns including optimistic original estimates, lack
of scope definition at the start of the project, increase in scope
during project development phase due to pressure from project
stakeholders, errors in estimation and lack of appropriate contingency
budget (Booz Allen Hamilton Inc. 2005). In many construction projects,
the owner plans for unexpected events that may affect project cost by
adding a contingency to the estimated cost (Touran 2003). If the
contingency is overestimated and allocated, the use of capital may be
deemed inefficient and if it is underestimated, it contributes to
increase the probability that the project may fail (Tseng et al. 2009).
There are many factors affecting a project performance; disturbances in
the supply of materials and equipment, irregular financing, design
errors, inclement weather, equipment failures, inefficient contractors,
administrative and legal disturbances, etc. (Rogalska, Hejducki 2007),
and the risks in several infrastructural projects including road and
railroad projects (Lam 1999). Construction delay and overrun is a
critical function in construction of public projects and the time
required to complete these projects is frequently greater than the time
specified in the contract (Al-Momani 2000). It is clear that contingency
is critical in scheduling and it can be developed for project schedule
as a time buffer that is set aside to cope with uncertainties during
project design and construction.
Several quantitative studies have been made to determine the
project duration, schedule contingencies and time overruns; Bayesian
belief networks to quantify the probability of construction delays (Luu
et al. 2009), real options approach for contingency estimation (Tseng et
al. 2009), and three-stage least-squares technique to identify the
factors that significantly affect cost and time overruns (Bhargava et
al. 2010). Monte Carlo simulation has been used to estimate project
contingency and allocate among project activities (Barraza 2011).
The estimation of highway project duration can be made on the basis
of past experience or using historical data from similar projects in
similar contractual circumstances (Irfan et al. 2011). They investigated
the project duration on the basis of variables known at the planning
phase such as planned cost, project and contract type, and then
developed a model using data from the State of Indiana, spanning the
years 1996-2001.
In this study, a probabilistic approach is proposed to calculate
schedule contingency in transit projects. The objective is to estimate
schedule contingencies for the different level of completion of a
project and to achieve the project completion without delay. For this
purpose, Joint Confidence Level-Probabilistic Calculator (JCL-PC)
approach proposed by Butts and Linton (2009) is adopted as the
probabilistic method. The method is modified and used for transportation
projects and applied on a set of data obtained from Booz Allen Hamilton
Inc. (2005) report.
[FIGURE 2 OMITTED]
[FIGURE 3 OMITTED]
1. JCL-PC approach
In a NASA Cost Estimating Symposium, Butts and Linton (2009)
presented an approach, which aims to provide guidelines for developing
more accurate cost estimates for NASA projects. The objective is
mathematically compensating the optimism bias inherent in NASA cost
estimating activity. The optimism bias is handled by looking at the
historical performance of projects completed in the past and assume that
the effectiveness of the owner agency will be the same as was in
previous projects and hence, the same level of cost overruns and
schedule delays will happen in future projects. The method is called
Joint Confidence Level-Probabilistic Calculator approach (JCL-PC) and is
based on the hypothesis that, in the beginning phases of a project,
there are many unknown risks and over time the project will have a high
probability of exceeding estimated costs and scheduled duration (Butts,
Linton 2009).
The JCL-PC equation developed through this holistic algorithm is
used to correct the overly optimistic cost and schedule estimates in
NASA projects. The aim is to define the probability that the actual cost
and schedule will be equal or less than the targeted cost and schedule
date. The lessons learned and the benefits obtained by using the
proposed method have also been collected (NASA 2010). It basically helps
to improve project planning by strengthening risk management through
quantification of risks in terms of cost and schedule impacts. Enforcing
scheduling best practices, JCL-PC provides the picture of the project
ability to achieve cost and schedule goals, and to help the
determination of schedule and cost reserves. At any confidence level,
the project can be baselined or rebaselined for schedule analysis and
rebudgeted for cost analysis.
In this approach, a histogram of cost or schedule overruns is used.
A ratio is selected using a simulation approach such that it ensures
that the established budget or schedule will not be exceeded with a
specified confidence level (Touran, Zhang 2011). It is assumed that as
the project progresses, optimism biases will fade and quantifiable risks
become clearer.
In order to make the appropriate correction of the estimate at a
specified confidence level, a multiplier is calculated in JCL-PC method
from Eqn (1). Afterwards, the base estimate is multiplied by this
multiplier M and the required budget or schedule is estimated at a
specified confidence level:
M = (1 + z) x 1 x (Percent complete); (1)
projects required budget or duration =
M x projectbaseestimate. (2)
In Eqn (1), the percent cost or schedule growth in previously
completed similar projects is represented by z value from distribution
Z. The sum of percent remaining and percent complete is always 100% and
refer to the project under consideration. Base estimate is project
schedule (or cost) after all contingencies have been removed. These
definitions indicate that as more of the project is completed, the
required contingency becomes smaller for the remaining portion. One
major issue with the JCL-PC approach is that for various levels of
project completion, the delay distribution for z remains the same. It is
reasonable to assume that as project approaches completion, the delay
distribution should represent smaller values because the magnitude of
delays should become smaller. The authors of this paper have modified
the JCL-PC approach to account for this important shortcoming of the
NASA approach.
2. The proposed approach in the context of transit projects
In this study, 28 transit projects' historical data is used to
show the proposed approach for establishing the project's schedule
contingency. The data is obtained from Booz Allen Hamilton Inc. report
(2005). The following phases of the project lifecycle are reported with
their duration and delay data as listed in Table 1. The average duration
of all projects for total, preliminary engineering, final design and
construction phases are 8.4 years, 2.3 years, 2.7 years and 4.0 years
respectively. These are completed transit projects in the United States
characterized by three different mode types; heavy rail, light rail and
bus way.
Project development phases can be defined as:
* Preliminary Engineering (PE)/Final Environmental Impact Statement
(FEIS);
* Final Design (FD), which is at the end of design effort in
traditional design-bid-build contracts and before going to bid;
* Construction.
Since the schedule growth is available for this set of projects, it
is possible to construct the histogram of the distribution of schedule
growth at the end of construction phase which actually reflects the real
project completion times with delays (Fig. 1). It shows that the average
schedule growth is 34% of the original duration and the standard
deviation of the schedule growth is 22% (Fig. 1). Using Chi-square test
of goodness of fit, a Normal Distribution is fitted to this data set.
The means of cumulative schedule growths are then calculated and
the schedule contingency amount at the end of each phase is determined.
Afterwards, these values are mapped against percent completions for all
phases. It is assumed for the purpose of this study that PE/FEIS, FD and
Construction phases refer to 5%, 15% and 100% completions respectively
and the mapping is conducted for each phase independently (Touran, Zhang
2011). The average schedule contingencies at the end of PE/FEIS, FD and
Construction phases are determined as 44%, 13% and 0% respectively as
shown in Table 2. Three fitted sets of data against percent completions
(0%, 5%, 15% and 100%) are shown in Figures 2 (a-c).
The other percent completion levels can be estimated by using the
mean lines of schedule growth at the end of each phase (u) which are
fitted according to the data points expressed above and calculated
depending on the corresponding phase interval.
The separate equations for determining the mean values for the
PE/FEIS, FD and Construction phases are expressed below in Eqns (3-5):
[[mu].sub.PE/FEIS] = -11.122x + 1; (3)
[[mu].sub.FD] = -3.1615x + 0.602; (4)
[[mu].sub.Construction] = -1503x + 0: 1503, (5)
where x is percent completion for the project, expressed in decimal
format.
Eqns (3), (4) and (5) can be used to calculate the means of
schedule contingency remaining at a given percent completion between
0-0.05 (PE/FEIS), 0.05-0.15 (FD) and 0.15-1 (Construction) respectively,
assuming linear changes in delay during each of these phases.
For different completion percentages, the appropriate normal
distribution value is used to determine the values of M which is the
JCL-PC multiplier (Eqn 1). A distribution for M is simulated for each
percentage point and then used to calculate the value of M for the
specified confidence levels as proposed by Butts and Linton (2009). The
amount of the schedule growth at a given percent completion is then
determined by multiplying the total schedule contingency value (obtained
by using JCL-PC multiplier) and the schedule contingency remaining at
that stage. A sample table is provided in Table 3 in order to show the
notations that are used in the calculations.
3. Application
In order to use the lines fitted, a hypothetical transit project is
considered and it is assumed that the owner wants to establish a
schedule contingency at different confidence levels as a percentage of
base duration. Base duration is the established project duration
excluding all contingencies. If the estimate is prepared at the end of
PE/FEIS phase (approximately 5% completion), the simulation results in
Table 5 show that 18.5%, 21.3%, 24.8% schedule contingency is determined
(as the percentage of the base estimate) with a probability of 65%, 75%
and 85%, respectively. If this estimate is made for 50% completion, then
the amount will be about 1.8%, 2.6% and 3.5% of the base duration,
respectively. It is obvious that as the project progresses, the schedule
contingency that should be added to the base estimate decreases. This
pattern is observed in simulation results and it is shown as an example
in Figure 3. The JCL notations used for simulation in the application
and JCL multipliers determined by simulation are presented in Table 4.
All of the simulation results including these values generated for
different levels of project completions vs. probabilities are shown in
Table 5. It should be noted that the aim of the proposed method is to
establish sufficient contingency to ensure the project completion
without any delay.
Summary and conclusions
In this paper, a methodology is proposed to analyze the project
schedule contingency for transit projects by considering various stages
of project completion for different contingency levels. It considers the
usage of schedule contingency as the project progresses. It takes into
account the variations of both the mean values and standard deviations
of time extensions at different percent completions. Since the
calculations are based on actual data set of transit projects, the
schedule growth rates can be obtained more accurately for desired
confidence levels. This would provide opportunity to all project parties
to make more realistic estimates in their schedules and plans during
various stages of the project; and be prepared to take necessary action
in case the available schedule contingency falls below reasonable
levels.
doi: 10.3846/13923730.2013.768542
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Asli Pelin Gurgun (a), Ye Zhang (b), Ali Touran (b)
(a) Department of Civil Engineering, Okan University, Tuzla Campus,
34959, Akfirat-Tuzla, Istanbul Turkey
(b) Department of Civil and Environmental Engineering, Northeastern
University, 360 Huntington Avenue,
Received 27 Jun. 2011; accepted 2 Nov. 2011
Corresponding author: Asli Pelin Gurgun
E-mail:
[email protected]
Asli Pelin GURGUN. She is an Assistant Professor in the Department
of Civil Engineering at Okan University, in Istanbul, Turkey. Her
research interests cover risk analysis, risk management, simulation,
decision making, project management, project delivery systems green
construction.
Ye ZHANG. He is a PhD candidate in the Department of Civil &
Environmental Engineering, Northeastern University, Boston, USA. His
research interests cover project management, construction management,
risk analysis and management, simulation, and cost estimating.
Ali TOURAN. He is a Professor in the Department of Civil and
Environmental Engineering at Northeastern University, in Boston, USA,
where he is the coordinator of the graduate program in Construction
Management. He is the author or co-author of more than 90 technical
papers in journals and conference proceedings. Dr Touran's research
is in the area of risk analysis of infrastructure projects, simulation,
and project delivery systems.
Table 1. Case study project schedule analysis (phases are
expressed in "years")
Original project duration
by phase (years)
A B C D E
Project PE/
Number Case study projects FEIS FD Construction
1 Atlanta North Line 1 3 6
Extension
2 Boston Old Colony 1 6 2
Rehabilitation
3 Boston Silver Line 1 1 10
(Phase 1)
4 Chicago Southwest 2 3 3
Extension
5 Dallas South Oak 2 1 3
Cliff Extension
6 Denver Southwest 4 1 3
Line
7 Los Angeles Red 5 1
Line MOS 1
8 Los Angeles Red 7 4
Line MOS 2
9 Los Angeles Red 10 5
Line MOS 3
10 Minneapolis 6 1 4
Hiawatha Line
11 New-Jersey Hudson- 3 1 5
Bergen MOS1
12 New York 63rd 3 2 7
Street Connector
13 Pasadena Gold Line 3 4 3
14 Pittsburgh Airport 2 1 7
Busway (Phase 1)
15 Portland Airport 2 4
MAX Extension
16 PortlandBanfield 3 1
Corridor
17 Portland Interstate 1 2 2
MAX
18 Portland Westside/ 1 3 4
Hillsboro MAX
19 Salt Lake North- 1 3 1
South Line
20 San Fransisco SFO 4 1 6
Airport Exten.
21 San Juan Tren Urbano 3 1 8
22 Santa Clara Capitol 1 4
Line
23 Santa Clara Tasman 3 4 2
East Line
24 Santa Clara Tasman 1 3 3
West Line
25 Santa Clara Vasona 1 5
Line
26 Seattle Busway Tunnel 1 3 3
27 St Louis Saint 3 1 2
Clair Corridor
28 Washington Largo 3 1 4
Extension
Approximate project delay by
phase (years)
A F G H I
Original total
project duration PE/
Project (Col.C + Col.D + FEIS FD Construction
Number Col.E) delay delay delay
1 10 0 0 4
2 9 1 2 0
3 12 4 0 4
4 8 0 1 2
5 6 1 2 -1
6 8 4 0 0
7 6 1 2
8 11 2 2
9 15 2 1
10 11 3 1 1
11 9 1 1 2
12 12 3 1 0
13 10 2 4 0
14 10 4 3 0
15 6 0 0
16 4 2 0
17 5 0 0 0
18 8 1 3 0
19 5 2 0 -1
20 11 2 0 2
21 12 2 1 4
22 5 0 0
23 9 2 1 0
24 7 2 0 -1
25 6 0 0
26 7 1 3 0
27 6 0 0 0
28 8 2 -1 0
Mean: 34
Standard deviation 22
A J K L
Final Schedule
duration overrun changes
(Col.F + for total
Total project Col.G + project %
Project delay (Col.G + Col.H + (Col.K-
Number Col.H + Col.I) Col.I) Col.F)/(Col.F)
1 4 14 40
2 3 12 33
3 8 20 67
4 3 11 38
5 2 8 33
6 4 12 50
7 3 9 50
8 4 15 36
9 3 18 20
10 5 16 45
11 4 13 44
12 4 16 33
13 6 16 60
14 7 17 70
15 0 6 0
16 2 6 50
17 0 5 0
18 4 12 50
19 1 6 20
20 4 15 36
21 7 19 58
22 0 5 0
23 3 12 33
24 1 8 14
25 0 6 0
26 7 11 57
27 0 6 0
28 1 9 13
Table 2. Schedule contingencies at the end of each phase
Delays in phases (years)
A B C D E
Total
Project PE/ project
Number FEIS FD Construction delay
1 0 0 4 4
2 1 2 0 3
3 4 0 4 8
4 0 1 2 3
5 1 2 -1 2
6 4 0 0 4
7 1 2 3
8 2 2 4
9 2 1 3
10 3 1 1 5
11 1 1 2 4
12 3 1 0 4
13 2 4 0 6
14 4 3 0 7
15 0 0 0
16 2 0 2
17 0 0 0 0
18 1 3 0 4
19 2 0 -1 1
20 2 0 2 4
21 2 1 4 7
22 0 0 0
23 2 1 0 3
24 2 0 -1 1
25 0 0 0
26 1 3 0 4
27 0 0 0 0
28 2 -1 0 1
Cumulative phase delay/total project delay
A F G H
PE/FEIS
Project (Col.B/ FD ([Col.B + Construction ([Col.B+
Number Col.E) % Col.C]/Col.E) % Col.C + Col.D]/Col.E) %
1 0 0 100
2 33 100 100
3 50 50 100
4 0 33 100
5 50 150 100
6 100 100 100
7 0 33 100
8 0 50 100
9 0 67 100
10 60 80 100
11 25 50 100
12 75 100 100
13 33 100 100
14 57 100 100
15
16 0 100 100
17
18 25 100 100
19 200 200 100
20 50 50 100
21 29 43 100
22
23 67 100 100
24 200 200 100
25
26 25 100 100
27
28 200 100 100
Schedule contingency at the end of phases
A I J K
Project PE/FEIS Construction
Number (1-Col.F) % FD (1-Col.G) % (1-Col.H) %
1 100 100 0
2 67 0 0
3 50 50 0
4 100 67 0
5 50 - 50 0
6 0 0 0
7 100 67 0
8 100 50 0
9 100 33 0
10 40 20 0
11 75 50 0
12 25 0 0
13 67 0 0
14 43 0 0
15
16 100 0 0
17
18 75 0 0
19 - 100 - 100 0
20 50 50 0
21 71 57 0
22
23 33 0 0
24 - 100 - 100 0
25
26 75 0 0
27
28 - 100 0 0
Notes: Mean 44, 13 and 0.
Table 3. JCL-PC notations used for simulation
A B C
% completion Schedule contigency Assume no risks occur
remaining
0 Values obtained by 1
using Eq. 3, 4
and 5
A D E F
% completion Normal risk dist. % project % project
complete remaining
0 Risk Normal 0 1- CoLE
(mean:SD)+1
A G H I
% completion JCL-PC Mult. Schedule Schedule
contingency contingency x
contingency
remaining
0 Risk Discrete Col.G-1 Col.H x Col.B
(Col.C;Col.D:
Col.E;Col.F)
Notes: Col. A: a given percent complete; Col. B: cumulative schedule
contingency at the end of given percent completion from real data
calculated by fitted lines in Figures 2 (a c) using Eqns (3 5);
Col. C: onstant assuming that no unknown risk would occur
at the given percent completion; Col. D: simulated values using a
simulation software (e.g. @Risk) with schedule growth mean and
standard deviation values from real data, which are 34%
and 0.22, respectively; Col. E: a given percent complete;
Col. F: % project remaining at that stage; Col. G: simulated
values for M (JCL-PC multiplier); Col. H: total schedule contingency
at the end of a given percent completion; Col. I: the amount of
schedule contingency for the rest of the project
Table 4. JCL-PC notations used for simulation in the application
A B C D
Assume no Normal risk
unknown dist. (mean:
% Schedule contingency risks 0.34, SD:
completion remaining occur 0.22)
-11.12 xCol.A + 1 = RiskNormal
(for PE/FEIS)--3.1615 x (0.34;0.22)+ 1
Col.A + 0.602 (for FD) -
0.1503 x Col.A + 0.1503
(for construction)
0 1.00 1 1.3401
5 0.44 1 1.3401
10 0.29 1 1.3401
15 0.13 1 1.3401
20 0.12 1 1.3401
25 0.11 1 1.3401
30 0.11 1 1.3401
35 0.10 1 1.3401
40 0.09 1 1.3401
45 0.08 1 1.3401
50 0.08 1 1.3401
55 0.07 1 1.3401
60 0.06 1 1.3401
65 0.05 1 1.3401
70 0.05 1 1.3401
75 0.04 1 1.3401
80 0.03 1 1.3401
85 0.02 1 1.3401
90 0.02 1 1.3401
95 0.01 1 1.3401
100 0 1 1.3401
A E F G
% % project % project
completion completed remaining JCL-PC Mult.
1--ColE = RiskDiscrete
(Col.C:Col.D;Col.E;
Col.F)
0 0 100 1.3401
5 5 95 1.3401
10 10 90 1.3401
15 15 85 1.3401
20 20 80 1.3401
25 25 75 1.3401
30 30 70 1.3401
35 35 65 1.3401
40 40 60 1.3401
45 45 55 1.3401
50 50 50 1
55 55 45 1
60 60 40 1
65 65 35 1
70 70 30 1
75 75 25 1
80 80 20 1
85 85 15 1
90 90 10 1
95 95 5 1
100 100 0 1
A H I
Schedule
% Schedule contingency x
completion contingency contingency remaining
= Col.G-1 = Col.Hx Col.B
0 0.3401 0.3401
5 0.3401 0.1510
10 0.3401 0.0972
15 0.3401 0.0435
20 0.3401 0.0409
25 0.3401 0.0383
30 0.3401 0.0358
35 0.3401 0.0332
40 0.3401 0.0307
45 0.3401 0.0281
50 0 0
55 0 0
60 0 0
65 0 0
70 0 0
75 0 0
80 0 0
85 0 0
90 0 0
95 0 0
100 0 0
Table 5. Simulated unused contingency values as a percent of base
duration using normal distribution
Project
completion % 5% 10% 15% 20% 25% 30% 35%
0 - 1.5 6.4 11.6 15.9 19.4 22.7 25.7
5 - 0.6 0.0 3.0 5.5 7.5 9.1 10.7
10 - 0.3 0.0 0.0 2.2 3.7 4.9 6.0
15 0.0 0.0 0.0 0.1 1.2 1.8 2.3
20 0.0 0.0 0.0 0.0 0.1 1.2 1.8
25 0.0 0.0 0.0 0.0 0.0 0.1 1.0
30 0.0 0.0 0.0 0.0 0.0 0.0 0.3
35 0.0 0.0 0.0 0.0 0.0 0.0 0.0
40 0.0 0.0 0.0 0.0 0.0 0.0 0.0
45 0.0 0.0 0.0 0.0 0.0 0.0 0.0
50 0.0 0.0 0.0 0.0 0.0 0.0 0.0
55 0.0 0.0 0.0 0.0 0.0 0.0 0.0
60 0.0 0.0 0.0 0.0 0.0 0.0 0.0
65 0.0 0.0 0.0 0.0 0.0 0.0 0.0
70 0.0 0.0 0.0 0.0 0.0 0.0 0.0
75 0.0 0.0 0.0 0.0 0.0 0.0 0.0
80 0.0 0.0 0.0 0.0 0.0 0.0 0.0
85 0.0 0.0 0.0 0.0 0.0 0.0 0.0
90 0.0 0.0 0.0 0.0 0.0 0.0 0.0
95 0.0 0.0 0.0 0.0 0.0 0.0 0.0
100 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Project
completion % 40% 45% 50% 55% 60% 65% 70%
0 28.6 31.3 34.0 36.7 39.5 42.3 45.3
5 12.0 13.3 14.5 15.8 17.1 18.5 19.8
10 7.0 8.0 8.9 9.7 10.6 11.5 12.4
15 2.8 3.2 3.7 4.1 4.6 4.9 5.4
20 2.4 2.9 3.3 3.7 4.1 4.5 4.9
25 1.7 2.3 2.7 3.2 3.6 4.0 4.4
30 1.2 1.8 2.3 2.8 3.2 3.6 4.0
35 0.2 1.1 1.8 2.3 2.7 3.1 3.5
40 0.0 0.4 1.1 1.8 2.2 2.7 3.1
45 0.0 0.0 0.5 1.2 1.7 2.2 2.6
50 0.0 0.0 0.0 0.5 1.2 1.8 2.3
55 0.0 0.0 0.0 0.0 0.5 1.2 1.7
60 0.0 0.0 0.0 0.0 0.0 0.6 1.2
65 0.0 0.0 0.0 0.0 0.0 0.0 0.6
70 0.0 0.0 0.0 0.0 0.0 0.0 0.0
75 0.0 0.0 0.0 0.0 0.0 0.0 0.0
80 0.0 0.0 0.0 0.0 0.0 0.0 0.0
85 0.0 0.0 0.0 0.0 0.0 0.0 0.0
90 0.0 0.0 0.0 0.0 0.0 0.0 0.0
95 0.0 0.0 0.0 0.0 0.0 0.0 0.0
100 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Project
completion % 75% 80% 85% 90% 95%
0 48.5 52.1 56.3 61.6 69.3
5 21.3 22.9 24.8 27.1 30.5
10 13.5 14.5 15.7 17.1 19.8
15 5.9 6.3 6.9 7.7 8.6
20 5.3 5.8 6.3 7.0 8.1
25 4.9 5.3 5.8 6.5 7.5
30 4.4 4.9 5.4 6.0 7.0
35 3.9 4.3 4.9 5.5 6.3
40 3.4 3.8 4.3 4.9 5.8
45 3.0 3.4 3.8 4.3 5.2
50 2.6 3.0 3.5 4.0 4.6
55 2.1 2.5 3.0 3.4 4.2
60 1.6 2.0 2.4 2.9 3.6
65 1.1 1.5 2.1 2.5 2.9
70 0.5 1.0 1.4 1.9 2.5
75 0.0 0.6 1.0 1.5 1.9
80 0.0 0.0 0.6 1.0 1.4
85 0.0 0.0 0.0 0.7 1.0
90 0.0 0.0 0.0 0.0 0.6
95 0.0 0.0 0.0 0.0 0.0
100 0.0 0.0 0.0 0.0 0.0