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

Contract Time Tool Example – Project Parameter Based

Artificial Neural Networks (ANN)

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

Duration estimation – User’s tasks

Input Prediction data Predict

ANN Tool description

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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