Post on 30-Aug-2018
Practices for Establishing Contract Time for Highway Projects
Thursday, May 10, 20181:00-2:30 PM ET
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Purpose
Discuss NCHRP Synthesis Report 402.
Learning ObjectivesAt the end of this webinar, you will be able to:
• List varying approaches to estimating contract time
• Identify factors influencing contract time• Describe practices for improving accuracy in
estimating contract time
NCHRP Synthesis Report 502: Practices for Establishing Contract Completion Dates for Highway Projects
NCHRP Project 20-05 Topic 47-09
NCHRP is a State-Driven Program
– Suggest research of national interest
– Serve on oversight panels that guide the research.
• Administered by TRB in cooperation with the Federal Highway Administration.
• Sponsored by individual state DOTs who
Practical, ready-to-use results• Applied research aimed at
state DOT practitioners• Often become AASHTO
standards, specifications, guides, syntheses
• Can be applied in planning, design, construction, operations, maintenance, safety, environment
Today’s Speakers
• Roy Sturgill, University of Kentucky• Ying Li, University of Kentucky• Paul Goodrum, University of Colorado at
Boulder
• Moderated by Tim Taylor, University of Kentucky
Practices for Establishing Contract Time for Highway Projects
TRB Webinar
PRESENTER: TIMOTHY R. B. TAYLOR, P.E., PH.D.PAUL GOODRUM, P.E., PH.D.ROY STURGILL, P.E.YING LI, PH.D.
May 10 t h, 2018
BackgroundKey Findings from NCHRP Synthesis 502Contract Time Tool Overview & ExampleoProduction & Quantity Based Approaches (Gantt Chart, Critical
Path Method, Linear Scheduling)oProject Parameter Based Methods
• Multiple Least Squares Regression• Artificial Neural Networks
Webinar Agenda
Establishing contract time is an important part of the highway project development processoMore aggressive completion deadlines tend to increase
construction costs.oSetting contract time goes beyond project specific monetary
considerations.oAccurately setting contract time can accelerate the delivery of
projects across a state transportation agency portfolio through improved efficiencies with both state and contractor resources.
Background
The Federal Code states, “The STD [State Transportation Department] should have adequate written procedures for the determination of contract time” (23 CFR 635.121). Federal Highway Administration (FHWA) Guide for Construction
Contract Time Determination Procedures (FHWA 2002). The guides specifies that the essential steps in determining contract
time should include: “(1) establishing production rates for each controlling item; (2) adopting production rates to a particular project; (3) understanding potential factors such as business closures, environmental constraints: and (4) computation of contract time with a progress schedule” (FHWA 2002).
Background
Findings from NCHRP Synthesis 502
Purchased off the shelf11%
Custom developed
53%
Purchased off the shelf and
then customized11%
Other 25%
Findings from NCHRP Synthesis 502
+/- 0-25%33%
+/- 25-50%4%
+/- 50-100%26%
Unsure37%
Yes30%
No55%
Unsure15%
Evaluate Effectiveness? Accuracy of Estimates
Findings from NCHRP Synthesis 502
Note: Column widths are proportional to the numbers of responses in each group(“Construction”, “Design” or “Other”). Block heights are proportional to the numbers ofrespondents who selected the corresponding accuracy level within each group.
• States in which contract time was estimated in the construction divisionreported greater accuracy in contract time estimates than states that estimate contract time within the design division.
27
13
2
Design-Bid-Build Design-Build ConstructionManagement/General
Contractor
Num
ber o
f Res
pond
ents
Formal Duration Estimating Procedures for Alternative Delivery Methods
Findings from NCHRP Synthesis 502
Findings from NCHRP Synthesis 502
2
5
5
9
9
10
17
0 2 4 6 8 10 12 14 16 18
No Improvement Needed
Improved Usability
Other - please specify
Increased Automation
Adaptability to Multiple Delivery Methods
Improved Accuracy
Increased Feedback/Communication…
Number of Respondents
Note: Respondent were asked to
check all that apply
Contract Time Tool Overview
START
Bar Chart
Critical Path Method
Est. Contract
Days
Est.Contract
Days
Est.Contract
Days
Time Estimation Methods
Regioinal Conditions
Project Priority
STA Staff Expertise
Project Risk
ContractMethods
Work Environment
Site Conditions
Technology Usage Methods Complexity Existing
Utilities
Work to be Done
Production & Quantity Based Methods
Project Parameter Based Methods
Multiple Least-Squares Regression
Linear SchedulingEst.
Contract Days
Artificial Neural NetworksEst.
Contract Days
Validation Through Construction
Expertise
Completion of Project & Accuracy
Feedback
System Feedback
System Feedback
Contract Time Tool Example – Production & Quantity Based
Screenshot for Microsoft Access Quantity Input (developed by Oklahoma Department of Transportation)
Contract Time Tool Example – Production & Quantity Based
Screenshot for Microsoft Project Schedule Output (Oklahoma Department of Transportation, 2008)
Original Kentucky Contract Time Determination System◦ Served as the model for many state systems◦ Used production rates & predefined logic◦ Error of the previous version: 233% mean error◦ New system shows a 52% mean error
◦ Lack of Use
KYTC wanted a simpler, more user friendly, and more accurate approach
Contract Time Tool Example – Project Parameter Based
Updated Kentucky Contract Time Determination System
Research Methods & Data AnalysisCollected/Analyzed Data from over 4,000 projects (2002-2011)~ only able to incorporate around 2,600 projects due to completeness Data:◦ Actual start & completion dates◦ Bid Items Quantities◦ Construction Estimates
Parametric Modeling/Linear Regression𝑌𝑌 = 𝛽𝛽0 + 𝛽𝛽1𝑋𝑋1 + ⋯+ 𝛽𝛽1𝑋𝑋𝑛𝑛
Project Grouping Criteria◦ Construction Estimates
◦ Larger Projects ( >=1Million)◦ Smaller Projects ( <1Million )
◦ Project type
Validation Steps
Updated Kentucky Contract Time Determination System
Classify Project
Automated Calculation of
Duration Estimates
Select Project Type
Input Construction
Estimate
Finalize Time Estimate
Limited AccessRegression
Bridge Replacement
Regression
Bridge Rehabilitation
Regression
New RouteRegression
Open AccessRegression
< $1,000,000
Review with Project
Development and Construction
PersonnelAutomated Calculation of
Duration Estimates
Finalize Time Estimate
YES
NO
Parametric Modeling for Larger ProjectsProject Type Project Description
Limited Access A project that utilizes the existing alignment but may revise the profile grade for an overlay
Open Access A project where a road is rebuilt that has either “access by permit” or “partial control” while using the existing right-of-way
New Route A project that is built from point “A” to point “B”
BridgeRehabilitation
A project that closes a lane on a bridge for reconstructing or widening the deck width
BridgeReplacement
A project whose main focus is building a new bridge.
Project Type
Sample Size
Model Variables Validation|(Predicted Duration-Actual Duration)/Actual Duration *100% )|
R2
Limited Access
23 Construction Estimate ($)Dirt Work Roadway (CY)Asphalt Base (Tons)Concrete Pavement (Tons)
% Error:Median- 21.53%
.916
Open Access
78 Construction Estimate ($)Dirt Work Roadway Excv (CY)PVC Pipe (LF)Stone Base Crushed Stone (Tons)Storm Sewer (LF)Culvert Pipe (LF)Striping (LF)
% Error: Median- 34.98%
.891
New Route
34 Construction Estimate ($)Steel Reinf (LF)DirtWork Granular Emb (CY)Perforated Pipe (LF)Striping (LF)
% Error: Median- 54.69%
.900
Bridge Rehab.
6 Construction Estimate ($) % Error:Median- 77.26%
.805
Bridge Replace.
14 Construction Estimate ($)Class AA Concrete (CY)Dirt Work_Granular Emb (CY)
% Error: Median- 17.03%
.936
Parametric Modeling for Larger ProjectsModel(N=78)
Coefficient B
95% Confidence Interval for B t Sig.
Goodness of Fit
Mean Lower Bound
Upper Bound F Sig. Adj R2
(Constant) 173.642 115.429 231.8555.97
8 .000
73.27 .000 .891
Construction Estimates (in 2005
$)1.188E-5 2.251E-6 2.150E-5 2.473 .017
Roadway Excavation 2.92E-4 2.177E-4 3.655E-4 7.910 .000
Stone Base/Crushed
Stone.048 0.015 0.082 2.892 .005
PVC Pipe .006 0.004 0.009 4.597 .000
Storm Sewer .036 0.024 0.048 6.031 .000
Culvert Pipe .075 0.042 0.110 4.456 .000
Striping -.001 -0.001 -2.347E-4 -2.876 .006
Mean % Error 61.26%
Median % Error 34.98%
• Y (days)=173.6+1.188E-5×Construction Estimate (in 2005 $) + 2.92E-4×Roadway Excavation (cu yd) + 0.048×Stone Base: Crushed Stone (cu yd) + 0.006 × PVC Pipe (lf ) + 0.036 × Storm Sewer (lf ) +0.075 × Culvert Pipe (lf ) -0.001 × Striping (lf )
Parametric Modeling for Smaller ProjectsProposed Solution ~ By-Hand Bar Chart◦ Did not meet KYTC needs◦ Counterintuitive that the process for smaller projects would be more
cumbersome than larger projects
Refocused Analysis on Projects Less than $1 million
Production Rates did not prove to be a good predictor
Collected additional data for 4,604 projects with construction estimates of less than $1 million◦ Average duration 75 calendar days◦ Over 90% were completed within 180 days◦ 95% were completed within 240 days ◦ A single construction season for Kentucky, but a tool was still needed
Numerous variables were considered (month, region, district, etc.)
A parsimonious model based on design project type and the construction estimate ◦ Leveraged 231 project data points◦ Data loss occurred due to omitting projects prior to 2005 and data gaps. ◦ Resulting model was statistically significant (p < 0.05, R2=0.398) and its
accuracy deemed acceptable to KYTC
Parametric Modeling for Smaller Projects
Parametric Modeling for Smaller ProjectsModel Parameter Mean Lower 95% CI Upper 95% CI
BRIDGE REHAB -28.012 -73.84893 17.824936BRIDGE REPLACEMENT 50.771987 30.465602 71.078372CONGESTION MITIGTN 3.5490812 -49.44283 56.540988
GUARDRAIL REPLCMNT -12.70174 -41.51222 16.108746
MINOR WIDENING -3.312152 -56.08908 49.464777NEW ROUTE 2.5356311 -78.59201 83.663274
PAVEMENT REHAB -11.46744 -45.50797 22.573093RECONSTRUCTION 55.440977 16.420342 94.461612
RESURFACING -71.53783 -138.4699 -4.605795SAFETY/SAFETY-HAZARD ELIM 0 0 0
Intercept 28.741366 7.6043166 49.878415Construction Est.(2005 $) 0.0001325 0.0000978 0.0001672
Simplified Tool Development•Cumbersome Equations to User-Friendly Spreadsheet
•Incorporated Cautionary Notes
•Incorporated the ability to adjust for Weather
•Included Estimates Along Three Contract Time Approaches (Working Day, Calendar Day, Completion Date)
•Automatically Escalates Costs by FHWA’s Construction Cost Index
Implementation & Demonstration•Goal: Get it to the end-user and adjust to meet their needs
•Developed User-Guide
•Developed YouTube Demonstration Videos
•Conducted a 1-hour Webinar
•Provide Updates and Support as Necessary
•Tool Access:• http://transportation.ky.gov/Highway-Design/Pages/Software-and-
Support.aspx
ANN – How it works
X1
X2
X3
Xn
Output ŷ
Input layer
Hidden layers
Y
In every cycle, the network compares the predicted vs. actual values and adjusts the cost function (relationship between variables and neurons). That’s called backpropagation
The input layer consists on our known predictor variables. They relate to the first hidden layer of neurons
The hidden layers are the neurons with the cost function.They interact with one another and, while the first layer interactsWith the input layer, the last one produces the output.
The cost function produces a weight that is comparable with the coefficients of a regression.
Why?•Accuracy of time estimation improves significantly by using ANN compared to MLR models.
•These models are developed using the same data.
•ANN is robust to the assessments that need to be check in MLR (i.e. normality, linearity, homoschedasticity, etc.)
•Dynamic models improve (through learning) over time
Duration estimation – Artificial Neural Network (ANN) approach – Our process
Input raw data TrainInput
Prediction data
Predict
Preliminary results
Sample predictions (three randomly selected projects)
cidCharged Days MLR
ANN W/O EE ANN W EE LOCATION DESCRIPTION
C12418* 250 271 209 233Berthoud Falls W. 2 Mi. RECONSTRUCTIONC13192 816 758 788 844N/O SH 119 - N/O SH 66 MAJOR WIDENING
C13923T* 180 153 185 182LOS PINOS RIVER IN IGNACIO (STR. P-06-AA)
BRIDGE REPLACEMENT
* Project Smaller than $1,000,000
Comparison Between ANN and MLR Models
Absolute Median Percent Error Absolute Mean Percent Error
All projects for training and predicting 7% 20%MLR Large for both 25% 38%
This model was developed using the variables excluded in the previous model, as an example of what can be achieved with further analysis
Comparison Between ANN and MLR Models
R² = 0.8948
-200-100
0100200300400500600700800900
-200 0 200 400 600 800 1000
ANN actual vs predicted
R² = 0.6585
0100200300400500600700800900
1000
-200 0 200 400 600 800 1000
MLR Actual Vs Predicted
Summary of use of Artificial Neural Networks
Advantages:◦ Improved accuracy◦ Ability to learn over time
Disadvantage◦ “Black Box” effect
Contract Time Estimation Methods Pros Cons Accuracy
Prod
uctio
n & Q
uant
ity Ba
sed A
ppro
ache
s Gantt (Bar Chart)• Easy to visualize• Commonly used• System requires little maintenance
• Time and effort intensive to produce• Accuracy depends on high level of expertise• No display of precedence or logic
+50%
Critical Path Method• Commonly used• Presents activity logic• System requires little maintenance
• Time and effort intensive to produce• Expertise dependent• Accuracy depends on high level of expertise• Accuracy depends on production rates
+50%
Linear Scheduling• More readily corresponds to the
linear nature of highway projects• System requires little maintenance
• Not commonly used• Time and effort intensive to produce• Expertise dependent• Accuracy depends on production rates• No display of precedence or logic
+50%
Proj
ect P
aram
eter
Base
d M
etho
ds
Multiple Least-Squares Regression
• Time efficient in producing estimate• Accuracy• Does not rely on production rates
• “Black-box” feel to the user/low comfort level• Requires complex analysis for development and
revisions• Accuracy diminished for outlier projects
+25%
Artificial Neural Networks
• Time efficient in producing estimate• Most accurate approach• Does not rely on production rates• Continuously updated
• “Black-box” feel to the user/low comfort level• Difficult development & integration• Accuracy diminished for outlier projects
+10%
THANK YOU! Questions?
Timothy R. B. Taylor, P.E., Ph.D. (tim.taylor@uky.edu)Paul Goodrum, P.E., Ph.D. (paul.goodrum@colorado.edu)Roy Sturgill, P.E. (roy.sturgill@uky.edu)Ying Li, Ph.D. (ying.li@uky.edu)
NCHRP 08-114:
Systematic Approach for Determining Construction Contract Time: A Guidebook
Research Team: Dr. David Jeong (Iowa State -> Texas A&M),
Dr. Doug Gransberg (Gransberg and Associates)
Dr. Kunhee Choi (Texas A&M)
Dr. Ali Touran (Northeastern Univ.)
Mr. Michael Rahgozar (Keville Enterprises)
Research Goal
• Develop a comprehensive guidebook encompassing procedures, methods, and tools for determining construction contract time that can work for a wide spectrum of DOT projects.
• Principle components for contract time and risks• Best practices / Innovative practices • Procedures and methods for alternate project delivery methods
Preliminary Framework
ConstructionFinal DesignPreliminary DesignPlanning /Programming
Top-Down Contract Time
Estimating
Bottom-Up Contract Time
EstimatingMos
t Effe
ctiv
e
Mos
t Effe
ctiv
e
Level A
Contract Time Estimating(Low Accuracy)
Contract Time Estimating(Low Accuracy)
Level B
Contract Time Estimating(Medium Accuracy)
Contract Time Estimating(Medium Accuracy)
Level C
Contract Time Estimating(High Accuracy)
Contract Time Estimating(High Accuracy)
Level D
Monitoring and Post-Construction EvaluationMonitoring and Post-
Construction Evaluation
Feedback Loop and Continuous Improvement
Contact Points
David Jeong (PI)H.david.Jeong@gmail.com
Cell) 515-509-5400
Today’s Participants• Tim Taylor, University of Kentucky,
tim.taylor@uky.edu• Roy Sturgill, University of Kentucky,
roy.sturgill@uky.edu• Ying Li, University of Kentucky,
ying.li@uky.edu• Paul Goodrum, University of Colorado at
Boulder, paul.goodrum@colorado.edu
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