International conference on transportation and development 2018 airfield and highway pavements

Virginia Polytechnic Institute and State University Nasir Gharaibeh Texas A&M University Scott Gibson Regional Transportation Commission of Washoe County, Nevada Konstantina Gkritza Purd

Yinhai Wang, Ph.D.

Edited by

I NTERNATIONAL C ONFERENCE ON

2018

SELECTED PAPERS FROM THE INTERNATIONAL CONFERENCE ON

TRANSPORTATION AND DEVELOPMENT 2018

July 15–18, 2018 Pittsburgh, Pennsylvania

SPONSORED BY The Transportation & Development Institute

of the American Society of Civil Engineers

EDITED BY Yinhai Wang, Ph.D

Michael T McNerney, Ph.D., P.E

Published by the American Society of Civil Engineers

Published by American Society of Civil Engineers

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Preface

It is our great pleasure to welcome you to the ASCE International Conference on Transportation and Development (ICTD 2018)! Organized by Transportation and Development Institute (T&DI), ICTD is ASCE’s flagship conference in transportation and development The

conference theme, Emerging Technologies: Impacts on Transportation and Development,

represents our vision and goal for future endeavors in transportation and development research, education, and practice ASCE ICTD 2018 awaits your active participation and contribution at the beautiful and scenic Wyndham Grand Pittsburgh Downtown Hotel from July 15 through 18,

2018

Pittsburgh is historically known as “the Steel City.” Now, about 1,600 technology firms, including Google, Apple, Bosch, Facebook, Uber, Nokia, Autodesk, and IBM, have landed in Pittsburgh, making it an important technology hub and one of the eleven most livable cities in the World Being the host city of ASCE ICTD 2018, Pittsburgh offers many unique real-world examples for transportation and development professionals to feel, think, and learn

ASCE ICTD 2018’s technical program is featured with four plenary sessions:

 Opening Plenary Session: Keynote Speeches from Federal, State, and Local Government Leaders

 Private Sector CEO Forum: Impacts of Connected & Autonomous Vehicles on Transportation & Development - Perspectives of Leaders from the Private Sector

 State DOT CEO Forum: Impacts of Connected & Autonomous Vehicles on Transportation & Development - Perspectives of Leaders from the Public Sector

 The Advent of CAVs - A Global Perspective: Current Status of Deployment and Future

of Connected and Autonomous Vehicles Around the World

The program covers deeper technical content on multiple modes and topics in transportation and development in eight (8) concurrent tracks It also includes a variety of special events such as the T&DI Board of Directors’ Town Hall Meeting, Younger Members’ “The Best Advice I Ever Received” session, icebreaker reception, and an Awards Banquet The conference is preceded with four (4) associated workshops:

 Mobility as a Service Workshop

 University Transportation Center Technology Transfer Workshop

 NSF Civil Infrastructure Systems Workshop

 ASCE Ethics Workshop

All these workshops are carefully designed to enhance fruitful experience of participants Last but not the least, conference attendees get the opportunity to attend over 15 technical committee meetings of ASCE as preconference event, covering all areas of transportation and development

In addition, partnering with Transportation Research Board (TRB), two TRB committees have chosen to host their mid-year meeting at ICTD 2018, giving conference attendees additional exposure to technical discussions and content

It is exciting to announce that ASCE ICTD 2018 attracted huge interests as indicated by the record high quality contributions and the rich technical program A total of 146 papers were accepted for publication in the proceedings These published papers went through a rigorous review and quality assurance process in the process of becoming a publication of ASCE – the world’s largest publisher of Civil Engineering content The proceedings for this conference have been organized in four (4) different volumes based on the topical distribution as follows:

 Volume I: Connected & Autonomous Vehicles and Transportation Safety

 Volume II: Traffic & Freight Operations and Rail & Public Transit

 Volume III: Airfield & Highway Pavements

 Volume IV: Planning, Sustainability, and Infrastructure Systems All these accomplishments are due to the excellent team efforts of our Conference Steering Committee, and the terrific support from ASCE-T&DI staff We would like to express our sincere gratitude to all the authors and conference participants for their solid contributions We are also grateful to all paper reviewers for their outstanding volunteer efforts Finally, our special thanks goes to the entire Conference Steering Committee, Local Organizing Committee, T&DI technical committee volunteers, ASCE-T&DI staff members, sponsors, exhibitors, invited speakers, and session chairs for their hard work and great efforts to help lead ASCE ICTD 2018

on track to a great success!

ASCE ICTD has been an excellent platform for information exchange, experience sharing, and professional networking since it was launched in 2011 We hope ASCE ICTD 2018 to be another wonderful and rewarding experience in your memory Wish you a very pleasant stay in Pittsburgh!

ASCE ICTD 2018 Co-Chairs & Proceedings Editors

Yinhai Wang, Ph.D., M.ASCE Michael T McNerney, Ph.D., P.E., M.ASCE

Acknowledgements

Conference Steering Committee

Yinhai Wang, Ph.D., M.ASCE (Co-Chair & Proceedings Editor) University of Washington

Michael T McNerney, Ph.D., M.ASCE (Co-Chair & Proceedings Editor) University of Texas at Arlington

Chris Hendrickson, Ph.D., Hon.M.ASCE (Chair, Local Organization Committee) Carnegie Mellon University

Randall (Randy) S Over, P.E., F.ASCE, Retd (Chair, Sponsorships & Exhibits)

2014 President of ASCE, Ohio DOT Brian McKeehan, P.E., F.ASCE (Past-Chair) Gresham, Smith and Partners

Katherine Kortum (Track Chair, Development) Transportation Research Board (TRB)

Robert Bryson, P.E., M.ASCE Retd (Track Chair, Roadways) City of Milwaukee

Walt Kulyk, P.E., M.ASCE, Retd (Track Chair, Rail & Public Transit) Federal Transit Administration

Rich Thuma, P.E., M.ASCE (Track Chair, Aviation) Crawford, Murphy & Tilly

Zhanmin Zhang, Ph.D., M.ASCE (Track Chair, Mode Spanning) University of Texas at Austin

Jianming Ma, P.E., M.ASCE (Track Chair, Connected & Autonomous Vehicles’

Impacts) Texas Department of Transportation

Local Organizing Committee

Chris Hendrickson, Ph.D., Hon.M.ASCE (Chair, Local Organization Committee) Carnegie Mellon University

David DiDiogia, P.E., M.ASCE McMahon Associates

Sean Qian, Ph.D., M.ASCE (Student & Younger Member Activities) Carnegie Mellon University

Stan Caldwell, Ph.D., M.ASCE Carnegie Mellon University Julie Vandenbossche, Ph.D., M.ASCE University of Pittsburgh

Paper Reviewers

Ahmed Abdeldayem Renju Abraham Burns & McDonnell Engineering Company, Inc

Emmanuel Adanu University of Alabama

Nithin Agarwal University of Florida Ricardo Aitken Ahmad Al-Akhras Public Transport Authority of Riyadh, Saudi Arabia

Majed Al-Ghandour North Carolina DOT Priyanka Alluri Florida International University Panagiotis Anastasopoulos University at Buffalo Michael Anderson University of Alabama in Huntsville Justice Appiah

Virginia DOT Ricardo Archilla University of Hawaii Warda Ashraf Purdue University

Baabak Ashuri Georgia Tech University Husain Abdul Aziz Oak Ridge National Laboratory Joel Barnett

Department of Transportation Geoff Baskir

Federal Aviation Administration Rahim Benekohal

University of Illinois at Urbana-Champaign Abhinav Bhattacharyya

University of California, Berkeley Richard Boudreau

Boudreau Engineering, Inc

Georges Bou-Saab Iowa State University David Brill

Federal Aviation Administration

Robert Bryson Ayres Associates Lei Bu

Jackson State University

Qing Cai University of Central Florida Samuel Cardoso

Consultant on Airports and Airfield Pavements

Silvia Caro Universidad de los Andes, Columbia Halil Ceylan

Iowa State University Karim Chatti

Michigan State University Ghassan Chehab

American University of Beirut Peter Chen

Santa Clara Valley Transportation Authority

Subeh Chowdbury University of Auckland Mashrur Chowdhury Clemson University Eleni Christofa University of Massachusetts, Amherst David Clarke

University of Tennessee, Knoxville Julius Codjoe

State of Louisiana Alison Conway City College of New York Seosamh Costello

University of Auckland Robert Costigan Qingbin Cui University of Maryland

Jordan Daniell HNTB Corporation Veronica Davis

Nspire Green Kakan Dey West Virginia University Sunanda Dissanayake Kansas State University

Kimberly Eccles VHB

Larry Emig Deogratias Eustace University of Dayton Ahmed Faheem Temple University Wei Fan

UNC Charlotte Muhammad Farhan Imam Abdulrahman Bin Faisal University Luis Ferreras

Velvet Fitzpatrick The National Academy of Sciences, Engineering, and Medicine

Scott Forbes

Mike Frabizzio Advanced Infrastructure Design, Inc

Jason Frank Garver

Ryan Fries Southern Illinois University Edwardsville James Gallagher

Resolution Management Consultants, Inc

Christopher Garlick Michael Garvin

Virginia Polytechnic Institute and State University

Nasir Gharaibeh Texas A&M University Scott Gibson

Regional Transportation Commission of Washoe County, Nevada

Konstantina Gkritza Purdue University Salil Gokhale Dynatest Nima Golshani University of Illinois at Chicago Yaobang Gong

University of Central Florida Jozef Grajek

EJG Aviation Feng Guo Virginia Polytechnic Institute and State University

Jim Hall Applied Research Associated, Inc

Thomas Hall Purdue University John Harvey

UC Davis David Hein Applied Research Associated, Inc

Brendon Hemily Hemily and Associates Chris Hendrickson Carnegie Mellon University Frank Hermann

Jungyeol Hong University of Seoul Kamal Hossain University of Illinois at Urbana-Champaign Mohammad Imran Hossain

Bradley University Mustaque Hossain Kansas State University Jill Hough

North Dakota State University Jia Hu

University of Virginia Hai Huang

Penn State University Mouyid Islam

Center for Urban Transportation Research, University of South Florida

Reza Jafari Road Safety and Transportation Solutions, Inc

Mohammad Jalayer Rutgers University Steven Jones University of Alabama Ganesh Karkee

City of Sunnyvale, California Kurt Keifer

Gorrondona & Associates, Inc

Vivek Khanna WSP

Myungseob Kim Western New England University Sonny Kim

University of Georgia

Ronald Knipling Safety for the Long Haul, Inc

Kristin Kolodge J.D Power Alexandra Kondyli University of Kansas Eleftheria Kontou National Renewable Energy Laboratory Katherine Kortum

Transportation Research Board Gregory Krueger

HNTB Corporation Emin Kutay

Michigan State University Samuel Labi

Purdue University Hyung Lee Applied Research Associated, Inc

Kang-Won Lee University of Rhode Island Matthew Lesh

Yingfeng Li Center for Infrastructure-Based Systems Zhenning Li

University of Hawaii

John Lieswyn ViaStrada Lei Lin University at Buffalo

Huiyuan Liu University of Nebraska-Lincoln Jun Liu

Min Liu

NC State University Cheryl Lowrance VHB

Jianming Ma Texas Department of Transportation Wanjing Ma

Matthew Mace Hill International Rajib Mallick Worcester Polytechnic Institute Angel Mateos

University of California, Berkeley Akhilesh Maurya

Indian Institute of Technology Guwahati Mehran Mazari

California State University, Los Angeles Leslie McCarthy

Villanova University Brian McKeehan Gresham Smith & Partners

Magaret McNamara University of Alabama Sue McNeil

University of Delaware

Mike McNerney University of Texas at Arlington Richard Meininger

Boise State University Lambros Mitropoulos University of Hawai'i, Manoa Amin Mohamadi Hezaveh University of Tennessee, Knoxville

Nadereh Moini New Jersey Sports and Exposition Authority

Ali Mokhtari University of Iowa Dan Murphy CDM Smith Mike Murphy University of Texas at Austin Scott Murrell

Applied Research Associated, Inc

Anusha Musunuru Kittelson & Associates Andrzej Nowak Auburn University Osama Osman Louisiana State University Aleli Osorio-Lird

Yanfeng Ouyang University of Illinois at Urbana-Champaign Hasan Ozer

University of Illinois at Urbana-Champaign Srikanth Panguluri

CH2M Aristeidis Pantelias University College London Tom Papagiannakis

University of Texas at San Antonio

Cody Parham HDR, Inc

Brian Park University of Virginia Ram Pendyala

Arizona State University Josh Peterman

Fehr & Peers Diniece Peters New York City Department of Transportation

Kelly Pitera Norwegian University of Science and Technology

Avinash Prasada New York City Transit Panos Prevedouros University of Hawaii Srinivas Pulugurtha UNC Charlotte

Yu Qian University of South Carolina

Zhen Qian Carnegie Mellon University Brian Reynolds

WSP

Laurence Rilett University of Nebraska-Lincoln Charles Rivasplata

San Jose State University

Dimitris Rizos University of South Carolina Stephen Romanoschi

University of Texas, Arlington Dean Rue

CH2M Eugene Russell Kansas State University

Tariq Saeed Purdue University Milad Saghebfar Louisiana State University

Mitsuru Saito Brigham Young University Robert Scancella

James Scherocman Consulting Engineer Wayne Seiler All About Pavements, Inc

Mohamadreza Shafieifar Florida International University Vikas Sharma

Kimley-Horn Samih Shilbayeh Washignton State Department of Transportation

Amit Kumar Singh Atkins

Sarbjeet Singh New York City Transit David Smith

Interlocking Concrete Pavement Institute Tai-Jin Song

Korea Transport Institute Reginald Souleyrette University of Kentucky

Jerry Spears Montana Association of Counties David Stanek

Fehr & Peers Aleksandar Stevanovic Florida Atlantic University Robert Stevens

Arcadis Xiaoduan Sun University of Louisiana, Lafayette Prajol Tamrakar

University of Texas at El Paso Shiraz Tayabji

Advanced Concrete Pavement Consultancy LLC

Athanasios Theofilatos National Technical University of Athens

Rich Thuma Crawford, Murphy & Tilly Raul Tiwari

School of Planning & Architecture Bhopal, India

Oscar Oviedo Trespalacios Erol Tutumluer

University of Illinois at Urbana-Champaign Majbah Uddin

University of South Carolina Avinash Unnikrishnan Portland State University Donald Uzarski

University of Illinois Amiy Varma

North Dakota State University

Eileen Velez-Vega Kimley-Horn Matthew Volovski Manhattan College Chao Wang University of California, Riverside Yinhai Wang

University of Washington Ziran Wang

University of California, Riverside Quintin Watkins

Michael Baker Internation Jim Wilde

Minnesota State University Mankato Billy Williams

NC State University Guoyuan Wu University of California, Riverside Mengqi Wu

Port of Seattle Shenghua Wu University of South Alabama Yina Wu

University of Central Florida

Zifeng Wu AECOM Hao Xu University of Nevada, Rio Guangchuan Yang

University of Wyoming Xianfeng Yang

University of Utah Anil Yazici Stony Brook University Mohamed Zaki

University of British Columbia Raymond Zee

Federal Aviation Administration Weibin Zhang

Nanjing University of Science and Technology

Zhanmin Zhang University of Texas at Austin Jiguang Zhao

CH2M

Mo Zhao Virginia DOT Zhuping Zhou Nanjing University of Science and

Technology

Workshop Organizers

Laurence Rilett, Ph.D., P.E., M.ASCE University of Nebraska at Lincoln Workshop: UTC Technology Transfer Cynthia Chen, Ph.D Irina Dolinskaya University of Washington National Science Foundation (NSF) Workshop: NSF Funding Opportunities in CMMI: CIS and OE Program

Guohui Zhang Wanjing Ma Meng Li University of Hawaii Tongji University Tsinghua University Workshop: Mobility as a Service (MaaS)

Tara Hoke, Aff.M.ASCE American Society of Civil Engineers (ASCE) Workshop: Ethics for the Practicing Engineer

Staff

Muhammad Amer, M.ASCE Director, Transportation & Development Institute (T&DI) of ASCE Debi Denney

Manager, Transportation & Development Institute (T&DI) of ASCE Rachel Hobbs

Administrator, T&DI and Construction Institute (CI) Conferences Neal Sweeney

Coordinator, Transportation & Development Institute (T&DI) of ASCE Donna Dickert

Senior Manager / Acquisitions Editor, ASCE Books Drew Caracciolo

Manager, Exhibit & Sponsorship Sales, ASCE

Contents

Airfield Pavements

Development of Artificial Neural Networks Based Predictive Models

for Dynamic Modulus of Airfield Pavement Asphalt Mixtures 1

Orhan Kaya, Navneet Garg, Halil Ceylan, and Sunghwan Kim

Use and Impact of Performance Management in Airfield Asset

Management Strategy 8

C Maggiore, G Fitch, and B Shaw

Hydronic Heated Pavement System Using Precast Concrete Pavement

for Airport Applications 16

Hesham Abdualla, Halil Ceylan, Sunghwan Kim, Peter C Taylor,

Kasthurirangan Gopalakrishnan, and Kristen Cetin

Implementing Advanced Wireless Sensing System for Airfield Pavement

Condition Monitoring 25

Shuo Yang, Halil Ceylan, Sunghwan Kim, and Hesham Abdulla

Temporary Construction of Ramps and Their Effect on Aircraft

Ride Quality 36

M A Gerardi and M T McNerney

Reliability Considerations of Airport Concrete Pavement Design Using

Variation of Backcalculated Modulus 45

Richard Ji and Biqing Sheng

Application of Beam Bridging Filter in the Processing of Airport Pavement

Longitudinal Profile Data 56

Qiang Wang and Albert Larkin

Full-Scale Tests of Aircraft Overloads on Airport Flexible Pavements 66

D R Brill and H Yin

Design and Reconstruction of Barranquilla Airport’s Concrete Runway

Using Rubberized Asphalt and Geogrid Fabric with Nighttime

Construction 78

Xavier Muñoz and Michael T McNerney

Strategy for a Concrete Overlay of a Commercial Service Runway

without Daytime Closure 88

Michael T McNerney and Eric P Bescher

End-Around Taxiways—A Win-Win-Win: Enhanced Safety, Reduced

Aircraft Delays and Emissions 99

William J Dunlay and Hui M Xu

Reliability Based Design Optimization of a MASH TL-3 Concrete Barrier 110

Erica Jarosch, Qian Wang, Hongbing Fang, and Hanfeng Yin

Highway Pavements

Flexural Behavior of Rubberized Concrete for Cold Regions Applications 119

Osama A Abaza and Zaid S Hussein

Part-Time Shoulder Use Partial-Depth Paved Shoulder Impact Study 129

S Coffey, S Park, and L McCarthy

Performance Evaluation of the Cement Stabilized Reclaimed Materials

for Use in Pavement Foundations 140

Mohammad Rashidi and Reza S Ashtiani

Characterization of the Moisture Susceptibility of Cement-Stabilized Base

Materials Using the Tube Suction Test 153

Mohammad Rashidi and Reza S Ashtiani

Investigation the Effect of Pavement Condition Characteristics on Bend

Segments Accident Frequency: Application of Fixed and Random

Parameters Negative Binomial Models 165

Hamid Ahmed Awad and Tony Parry

Otta Seal Construction for Asphalt Pavement Resurfacing 177

Sharif Y Gushgari, Yang Zhang, Ali Nahvi, Halil Ceylan,

and Sunghwan Kim

Evaluation of Resilient Modulus of Subgrade and Base Materials of

New Mexico and Its Implementation in ME-Design 185

Md Mehedi Hasan and Rafiqul A Tarefder

Impact of Coarse Aggregate Mineralogy on Coefficient of Thermal

Expansion of Paving Concrete in New Mexico 196

Gauhar Sabih and Rafiqul A Tarefder

Investigating the Heat Generation Efficiency of Electrically-Conductive

Asphalt Mastic Using Infrared Thermal Imaging 206

Ali Arabzadeh, Halil Ceylan, Sunghwan Kim, Alireza Sassani,

and Kasthurirangan Gopalakrishnan

Development of Low-Shrinkage Rapid Set Composite and Simulation

of Shrinkage Cracking in Concrete Patch Repair 215

Aseel S Mansi, Haider A Abdulhameed, and Yook-Kong Yong

Investigation of Mechanical Property of Polymer Modified Binder Using

Image Processing and Finite Element Method 227

Md Amanul Hasan, Zafrul Hakim Khan, Umme Amina Mannan,

and Rafiqul A Tarefder

Investigating Presence of Orthotropy in Asphalt Concrete through

Embedded Asphalt Strain Gages 235

Zafrul Khan, Mesbah Ahmed, and Rafiqul Tarefder

Effect of Foaming Water Contents on High-Temperature Rheological

Characteristics of Foamed Asphalt Binder 243

Biswajit K Bairgi and Rafiqul A Tarefder

Integrating Locally-Calibrated Material Characterization Models for

Design of Flexible Pavements: A Case Study 252

A U Afuberoh, A Shalaby, and L N Kavanagh

Assessment of Rutting Behavior of Warm-Mix Asphalt (WMA) with

Chemical WMA Additives towards Laboratory and Field Investigation 264

Biswajit K Bairgi, Rafiqul A Tarefder, Ivan Syed, Matias M Mendez,

Mesbah Ahmed, Umme A Mannan, and Md Tahmidur Rahman

Effects of Pores and Oxidative Aging on the Nanomechanical Behavior

of Asphalt Concrete 273

Hasan Faisal, Mohiuddin Ahmad, and Rafiqul Tarefder

Correlation of Automated Field Rut Measurements with HWTD Results 284

Ivan A Syed and Rafiqul A Tarefder

Geospatial Relationship of Intelligent Compaction Measurement

Values with In Situ Testing for Quality Assessment of Geomaterials 293

Luis Lemus, Aria Fathi, Jorge Beltran, Afshin Gholami, Cesar Tirado,

Mehran Mazari, and Soheil Nazarian

Effects of Extraction Solvent, Fine Particles, and Reclaimed Asphalt

Pavement Aggregate in Aging Determination of Asphalt Binder by

ATR-FTIRS 302

L Noor and N M Wasiuddin

Evaluation of a Full Scale Wheel Load Tester to Determine the Rutting

and Moisture Susceptibility of Asphalt Mix in Laboratory 311

S Arafat and N M Wasiuddin

Investigating the Prospect of Reclaimed Asphalt Pavement (RAP) as

Stabilized Base in the Context of Bangladesh 322

Mohammed Russedul Islam, Mohammad Imran Hossain,

and Md Rahman Tasfiqur

Effect of Nanomaterials on Binder Performance 332

B Karki, A Berg, R Saha, R S Melaku, and D S Gedafa

Design Factors Influencing Longitudinal Cracking Progression in

Doweled Jointed Plain Concrete Pavements 339

S Owusu-Ababio and R Schmitt

Temperature Susceptibility of Asphalt Binders for Climate Change 350

Lee P Leon and Kellesia Gittens

Application of Superpave Gyratory Compactor for Laboratory

Compaction of Unbound Granular Materials 359

Poura Arabali, Sang Ick Lee, Stephen Sebesta, Maryam S Sakhaeifar,

and Robert L Lytton

Performance and Effectiveness Evaluation of Pavement Maintenance

Treatments through Data Mining 371

Hui Du, Qiao Dong, and Fujian Ni

Influence of Aggregate Geometric Features on Permanent Deformation

of Asphalt Mixture Based on Image Processing and Data Mining 382

Song Li, Fujian Ni, Qiao Dong, Jiwang Jiang, Zili Zhao, and Hao Wu

Texture Measurement Based on 3D Pavement Surface Images at

Sub-mm Resolution 392

Shihai Ding, Enhui Yang, Kelvin C P Wang, and Guolong Wang

Increasing Compressive Strength of Recycled Aggregate Concrete

Using High Fineness Bottom Ash Blended Cement 401

Nicholas A Brake, Soheil Oruji, and Liv Haselbach

Mechanistic Evaluation of Effect of PPA on Moisture-Induced Damage

Using SFE and XRF 411

S A Ali, R Ghabchi, M Zaman, R Steger, S Rani, and M A Rahman

Development of Artificial Neural Networks Based Predictive Models for Dynamic Modulus

of Airfield Pavement Asphalt Mixtures

Orhan Kaya1; Navneet Garg2; Halil Ceylan3; and Sunghwan Kim4

1Dept of Civil, Construction and Environmental Engineering, Iowa State Univ., Ames, IA

E-mail: okaya@iastate.edu

2FAA Airport Technology R&D Branch, ANG-E262, William J Hughes Technical Center,

Atlantic City, NJ E-mail: navneet.garg@faa.gov

3Dept of Civil, Construction, and Environmental Engineering, Iowa State Univ., Ames, IA

Binder properties as well as laboratory dynamic modulus |E*| measurements for asphalt mixes

are performed for flexible airfield pavements research An artificial neural networks (ANN)

model was developed using collected volumetric properties, aggregate gradation, and binder

properties as well as laboratory |E*| measurements from seven hot-mix asphalt (HMA) and warm

mix asphalt (WMA) mixtures ANN model predictions were compared with the modified

Witczak predictive model calculations for the same mixtures, and it was found that the

developed ANN model successfully predicted |E*| for airfield pavement asphalt mixtures

INTRODUCTION

The dynamic modulus (|E*|) of an asphalt mixture is a fundamental property defining the structural response of asphalt layers in flexible pavement systems It is a complex number that

relates stress to strain in the frequency domain for linear viscoelastic materials subjected to

continuously-applied sinusoidal loading The complex modulus test is relatively expensive and

difficult to perform, and data analysis is fairly complicated, so several models have been

developed for predicting dynamic modulus values from asphalt mixture volumetric properties,

aggregate gradation, and binder properties The most widely used models are the Witczak

predictive models (Andrei et al 1999; Bari and Witczak 2006) based on conventional

multivariate regression analysis of laboratory test data Another dynamic modulus prediction

model is the Hirsh model (Christensen et al 2003)

The input variables for the 1999 version |E*| model (original Witczak equation) (Andrei et al

1999) include aggregate gradation, mixture volumetric properties, viscosity of the asphalt binder

(η), and loading frequency (f) With the introduction of the Superpave Performance Graded (PG)

binder specification to the asphalt community, the modified Witczak equation (Bari and Witczak

2006 that replaces binder viscosity (η) and loading frequency (f) in the original equation with the

binder dynamic shear modulus (|Gb*|) and phase angle (δb) (Bari and Witczak 2006) was

developed

Concerns regarding Witczak |E*| models include: they show significant scatter at low and/or high |E*| modulus extremes, they are dominated by the influence of temperature and they

understate the influence of other mixture parameters (Pellinen, 2001; Schwartz 2005; Dongre et

al 2005; Bari and Witczak 2006; Al-Khateeb et al 2006; Azari et al 2007) Ceylan at al (2009)

developed Artificial Neural Network (ANN) based models to predict dynamic moduli of HMA

in highway flexible pavement that used the same input parameters as the modified Witczak

equation and produced |E*| predictions with significantly higher accuracy than those from the

modified Witczak equation Kim et al (2011) also developed ANN based |E*| prediction models

to be used as part of the Long-Term Pavement Performance (LTPP) database That study also

compared the ANN models with closed-form models (Witczak and Hirsch), and found that ANN

models more successfully predicted |E*| than any of the closed-form solutions (Kim et al 2011)

They also stated that ANN models are more sensitive to input parameters and can consider

effects and interactions of many variables in predicting |E*| values

Table 1 Aggregate gradation variables and volumetric properties of seven mixes

Mixes

ρ19mm (0.75 in.)

and laboratory |E*| measurements for asphalt mixes have been performed for flexible airfield

pavement research In this study, an ANN model was developed using collected volumetric

properties, aggregate gradation, and binder properties as well as laboratory |E*| measurements

from seven airfield hot-mix asphalt (HMA) and warm mix asphalt (WMA) mixtures, and ANN

model predictions were compared with modified Witczak predictive model calculations for the

same mixtures Detailed procedures on ANN model development and accuracy of the developed

ANN model are also discussed

ANN MODEL DEVELOPMENT

An ANN model was developed using the same input parameters as the modified Witczak equation used to predict |E*| values The input variables for the 1999 version |E*| model (original

Witczak equation) (Andrei et al 1999) include aggregate gradation, mixture volumetric

properties, viscosity of the asphalt binder (η), and loading frequency (f) The aggregate gradation

variables are the percentage passing a #200 sieve (ρ#200), the percentage retained on a #4 sieve

(ρ#4), the percentage retained on a 9.5 mm sieve (ρ9.5mm), and the percentage retained on a 19

mm sieve (ρ19mm) The mixture volumetric properties include the air void percentage (Va) and

the effective binder percentage by volume (Vbeff) With the introduction of the Superpave

Performance Graded (PG) binder specification into the asphalt community, the modified Witczak

equation (Bari and Witczak 2006) was developed; it replaces binder viscosity (η) and loading

frequency (f) in the original equation with the binder dynamic shear modulus (|Gb*|) and phase

angle (δb) (Bari and Witczak 2006) (Equation 1) This equation is quite complex and was

obtained through regression analysis using the Witczak database (Bari and Witczak 2006)

ANNs have many advantages over regression because they do not have limitations such as

normality, linearity, and variable independence ANNs can capture complex linear and nonlinear

relationships between dependent and independent variables in a small fraction of the time

2

log | | 0.349 0.7546.65 0.032 ρ # 200 0.0027(ρ # 200)0.011(ρ # 4) 0.0001(ρ # 4) 0.006 ρ9.50.00014(ρ9.5) 0.08 1.06

2.558 0.032 0.7130.0124 ρ9.5 0.0

b

a beff

beff a

V V

V V

mixtures Mixes used in the model development were as follows:

Construction Cycle 7 (CC7) HMA mix with Superpave binder grade of PG 64-22

Center (NAPMRC) HMA mix with Superpave binder grade of PG 64-22

nominal maximum size of aggregate (NMSA) WMA, 20% recycled asphalt (RAP)) Volumetric properties, aggregate gradation, binder properties as well as laboratory |E*|

measurements of these seven mixes were used in ANN model development The same particular

input parameters used in the modified Witczak equation were also used in the model

development Details of model inputs and outputs used in the model development were as

- Aggregate gradation variables (percentage passing a #200 sieve (ρ#200), percentage retained on a #4 sieve (ρ#4), percentage retained on a 9.5 mm sieve (ρ9.5mm), and percentage retained on a 19 mm sieve (ρ19mm)

- Binder variables (dynamic shear modulus (|Gb*|) and phase angle (δb)) Model Outputs

- Measured |E*| values Table 1 summarizes aggregate gradation variables and volumetric properties of seven mixes used in the model development

ANN model development has three stages:

1 An excel spreadsheet was prepared that includes aggregate gradation variables, volumetric properties, binder properties and laboratory |E*| measurements from seven HMA and WMA mixtures

2 Aggregate gradation variables, volumetric properties, binder properties of the mixes were categorized as inputs, while laboratory |E*| measurements of the mixes were categorized

as outputs

3 ANN model relates these inputs to outputs through learning algorithms In the ANN model development, a two-layer feed-forward network was trained using a Levenberg-Marquardt algorithm (LMA) in the MATLAB environment

Figure 1 shows the ANN architecture used in the model development As can be seen in Figure 1, seven input parameters were used to predict an output parameter using fifteen hidden

neurons, with this number chosen because in similar earlier problems, this number of neurons

produced successful ANN models (Ceylan at al 2009)

Figure 1 ANN architecture used in the model development

RESULTS

Initially, ANN model E* predictions were compared with measured E* values to evaluate

success of ANN model in predicting E* values Figure 2 compares ANN predictions for |E*|

compared to laboratory measurements As can be seen in Figure 2, very successful correlation

between measured E* values and ANN predictions was achieved

Figure 2 ANN model predictions vs measured |E*|

Figure 3 Comparison of ANN model and Witczak equation

The model accuracies were expressed with the statistical terms; correlation coefficient (R2) and standard error (Equation 2)

2 1

n i i e

e s

Where,

se= standard error (i.e., standard deviation of errors);

E mi = measured dynamic modulus;

E pi = predicted dynamic modulus;

n = sample size;

p = number of model parameters

Figure 3 presents comparisons between ANN model predictions versus measured |E*| values and Witczak equation calculations versus measured |E*| values As can be seen in the figure, the

ANN model predicted E* values more accurately than the modified Witczak equation

CONCLUSIONS AND DISCUSSION

In this study, an ANN model was developed to predict dynamic moduli of airfield pavement asphalt mixtures In the model development, volumetric properties, aggregate gradation, and

binder properties as well as laboratory |E*| measurements from seven HMA and WMA mixtures

were used ANN model predictions were compared with modified Witczak predictive model

calculations for the same mixtures, and it was found that the developed ANN model successfully

predicted |E*| for airfield pavement asphalt mixtures Using this model, the dynamic modulus of

similar airfield pavement asphalt mixtures could be predicted by inputting required input

parameters to the model The developed ANN model could also be further revised and validated

using more mixes

ACKNOWLEDGEMENTS

This paper was prepared from a study conducted at Iowa State University under Project No

19: Center of Excellence Student Outreach, the Federal Aviation Administration (FAA) Air

Transportation Center of Excellence Cooperative Agreement 12-C-GA-ISU for the Partnership

to Enhance General Aviation Safety, Accessibility and Sustainability (PEGASAS) The authors

would like to thank FAA PEGASAS Program Manager, Mr Ryan King, and FAA Airport

Pavement R&D Section Manager, Mr Jeffrey S Gagnon The authors also would like to thank

PEGASAS Industry Advisory Board members Although the FAA has sponsored this project, it

neither endorses nor rejects the findings of this research The presentation of this information is

in the interest of invoking comments by the technical community on the results and conclusions

of the research

REFERENCES

Al-Khateeb, G., Shenoy, A., Gibson, N., and Harman, T (2006) “A new simplistic model for

dynamic modulus predictions of asphalt paving mixtures,” Journal of the Association of Asphalt Paving Technologists, 75, 1254–1293

Andrei, D., Witczak, M W., and Mirza, M W (1999) Development of a Revised Predictive

Model for the Dynamic (Complex) Modulus of Asphalt Mixtures, NCHRP 1-37 A Interim Report University of Maryland, College Park, MD

Azari, H., Al-Khateeb, G., Shenoy, A., and Gibson, N (2007) “Comparison of simple

performance test |E*| of accelerated loading facility mixtures and prediction |E*|: use of

NCHRP 1-37A and Witczak's mew equations,” Transportation Research Record, No.1998,

1–9

Bari, J and Witczak, M W (2006) “Development of a new revised version of the Witczak E*

predictive model for hot mix asphalt mixtures,” Journal of the Association of Asphalt Paving Technologists, 75, 381- 423

Ceylan, H., Schwartz, C W., Kim, S., and Gopalakrishnan, K (2009) “Accuracy of predictive

models for dynamic modulus of hot-mix asphalt,” ASCE Journal of Materials in Civil Engineering, Vol 21, No 6, 286–293

Christensen, D W., Pellinen, T., and Bonaquist, R F (2003) “Hirsch model for estimating the

modulus of asphalt concrete,” Journal of the Association of Asphalt Paving Technologists,

72, 97–121

Dongre, R., Myers, L., D’Angelo, J., Paugh, C., and Gudimettla, J (2005) “Field evaluation of

Witczak and Hirsch models for predicting dynamic modulus of hot-mix asphalt,” Journal of the Association of Asphalt Paving Technologists, 74, 381–442

Federal Aviation Administration (FAA.) (2014) Standards for Specifying Construction of

Airports FAA Advisory Circular (AC) No: 150/5370-10G, Washington, DC

Kim,Y R., Underwood, B., Far, M S., Jackson, N., and Puccinelli, J (2011) LTPP Computed

Parameter: Dynamic Modulus Publication no FHWA-HRT-10-035, Washington, DC.s Pellinen, T K (2001) Investigation of the Use of Dynamic Modulus as an Indicator of Hot-Mix

Asphalt Performance Ph.D dissertation, Arizona State University, Tempe, AZ

Schwartz, C W (2005) “Evaluation of the Witczak dynamic modulus prediction model,” Proc.,

the 84th Annual Meeting of the Transportation Research (CD-ROM), Transportation

Research Board, Washington, DC

Use and Impact of Performance Management in Airfield Asset Management Strategy

C Maggiore1; G Fitch2; and B Shaw3

1Airfield Pavement Engineer, Aviation Group, Jacobs U.K Ltd., Tower Bridge Ct., 226 Tower

Bridge Rd., London SE1 2UP E-mail: cinzia.maggiore@jacobs.com

2Divisional Director, Aviation Group, Jacobs U.K Ltd., Tower Bridge Ct., 226 Tower Bridge

Rd., London SE1 2UP E-mail: gary.fitch@jacobs.com

3Graduate Civil Engineer, Aviation Group, Jacobs U.K Ltd., Tower Bridge Ct., 226 Tower

Bridge Rd., London SE1 2UP E-mail: ben.shaw@jacobs.com

ABSTRACT

A new decision support tool (DST) was developed to model the performance of airfield pavements and plan potential capital expenditure (CapEx) interventions over a 20-year period for

London Heathrow Airport (LHR) The valuable contribution of DST for annual airfield

maintenance strategy planning is the control of the pavement performance of the total airfield

considering the asset criticality and the associated risk of each airfield segment The strength of

the model is in identifying the locations of potential failures and the necessary targeted

maintenance interventions The DST allows the user to plan in advance strategic, targeted

maintenance treatments, or replacement at section level This planned approach can generate

capital savings arising from optimising construction costs, minimising operational disruption,

and targeting appropriate treatments Savings of 50% on a 20-year maintenance plan at LHR

have been identified This performance-targeted, airfield pavement asset replacement strategy

derived from the DST has identified savings of approximately £70m in the next 5-year capital

investment period

INTRODUCTION

In general, asset management is the process that monitors, maintains and upgrades the level

of service of an infrastructure asset in the most cost-effective manner The asset management

process analyses the entire lifecycle of an infrastructure asset, from design stage to the end of life

and decommissioning

Theoretical design life is an estimated number of years of service after construction or rehabilitation for the pavement until it reaches failure (poor overall condition which requires

major planned maintenance or replacement works) Theoretical design life values are usually

fixed and depend mainly on the pavement type (rigid or flexible)

Airfield concrete pavements are usually more durable and, with the aid of minor maintenance works, they should be adequate for 25-35 years Asphalt pavements require surface maintenance

works after 7-10 years and more substantial maintenance works after 20-25 years In general,

concrete pavements have a higher initial construction cost than flexible pavements and take

longer to construct Therefore, the choice of one or another strongly depends on the location on

the airfield [DMG 27] There is a need for determining a systematic methodology to evaluate

pavement performance and plan the right maintenance decisions to maintain and/or upgrade the

quality of pavements, considering time, cost and future network developments

This paper describes the development of a Decision Support Tool (DST) to assist in determining a Capital Expenditure (CapEx) plan complying with maintenance intervention

thresholds (previously selected) based on theoretical knowledge and “local” experience This

paper explores the future cost of maintaining airfield pavements in their current condition,

against targeting slightly lower but acceptable performance to reflect asset criticality and

associated risk It identifies potential savings derived from managing the pavement to deliver

desired performance as set by intervention thresholds

DECISION SUPPORT TOOL

This DST has been developed by Jacobs to support airports to model the performance of airfield pavements and facilitate long-term financial planning determined by CapEx maintenance

interventions over a 20-year period London Heathrow Airport (LHR) has been selected as a case

study for this paper

The key features of the DST model are:

 Evaluation of pavement condition by means of visual inspection using Pavement Condition Index (PCI) data

 Calculation of a deterioration rate for each airfield section in order to forecast PCI data

 Identification of when the PCI of each section will reach the pre-determined threshold to trigger a CapEx intervention

 Identification of the most appropriate treatment based on location, existing pavement construction and access constraints

The first outcome is a Baseline Scenario, which automatically suggests CapEx interventions when the PCI reaches the intervention threshold specified by the user Three additional Scenarios

(Scenario 0, 1 and 2) enable CapEx treatments and intervention years to be manually adjusted by

the user based on local knowledge of factors such as future changes in airfield layout and/or

traffic and to reflect operational constraints The model has been set up with a number of

pre-defined, suggested treatments, which are automatically selected based on location, existing

construction and access constraints The model has also been set up to include five additional

treatment types to be manually specified by the user Typical cost rates have been provided for

each treatment, which can be adjusted by the user based on local knowledge The PCI value and

deterioration rate following the treatment depend on the treatment type selected

For the Baseline case and the three additional Scenarios, the following outputs are presented:

 A pivot chart summarising the cost of CapEx treatments needed for each airfield segment, year-by-year, for 20 years (see Figure 1)

 A pivot chart illustrating the impact of the selected CapEx strategy on the area-weighted PCI of each airfield segment, year-by-year, for 20 years (see Figure 2)

Figure 1 Example of pivot chart summarising the cost of CapEx treatments over a 20

year-plan

Figure 2 Example of pivot chart illustrating the impact of the selected CapEx strategy

The location of the proposed treatments can also be displayed graphically in GIS to facilitate the development of logical works programmes The DST flowchart is shown in Figure 4

Figure 3 DST Flowchart CASE STUDY: LONDON HEATHROW AIRPORT - LHR

London Heathrow Airport (LHR) is a major international airport in London, UK LHR is the busiest airport in Europe by passenger traffic and the seventh busiest airport in the world; with

over 75 million passengers in 2016 [ACI website]

LHR has approx 4.0 million m2 of airfield pavements comprising two parallel runways (23%), taxiways (49%) and stands spread across four operational terminals (28%) Most of these

are concrete pavements but some areas have composite construction (asphalt overlaid on

concrete) e.g runways Limited areas also have block-paved pavements

The airfield pavement network at LHR has been divided into distinct areas, defined as Branches, Sections and Sample Units A Branch is an identifiable part of the airfield / pavement

network and the Branches may be based on the Blocks (runway, taxiway, apron, shoulder, link,

hold, etc.) that are used in the operation of the airport Sections divide these Blocks into areas

according to pavement construction (rigid, flexible, composite), age, material (asphalt, concrete,

block paving, etc.) and traffic A total number of 1935 airfield sections have been identified for

full dataset covering the whole airfield Populated with this powerful data providing the

foundation for analysis, the DST can be used to help calculate performance levels and costs

based on a maintenance intervention strategy for each segment of the airfield The calculation of

cost and performance levels, over a 20-year period, makes the DST a powerful instrument in

performance management and development of a pavement asset management strategy

PERFORMANCE LEVELS AND ASSET CRITICALITY

Performance level can be defined as the level at which the asset can perform its desired function It is an essential way to manage risk; if the performance level of an asset becomes too

low there is the threat that the asset will not be able to perform its desired function As a result,

there should be different performance levels to reflect how critical an asset’s performance is in

meeting the organization’s objectives If the asset is essential to the operation of a network its

performance levels should be kept high If an asset is rarely used or can be closed without undue

effect for a short period of time it can have a lower performance level Varying performance

levels can therefore be set based on asset criticality and managed through maintenance

intervention strategies

Heathrow’s intensive operational use requires high levels of reliability To classify the condition of each airfield pavement section and the criticality depending on the location of the

section, a Red/Amber/Green (RAG) system was developed

Figure 4 RAG Classification Levels of each airfield section

The levels are defined as:

 Green (Adequate): Pavement is in an acceptable state of repair generally requiring

minimal planned maintenance

 Amber (Degraded): Significant deterioration is apparent which should be investigated

and monitored for trends to determine the optimum time for planned maintenance treatment or asset replacement

 Red (Unsatisfactory): Pavement in poor overall condition which is likely to require

major planned maintenance or asset replacement soon

ASSESSMENT OF CURRENT PERFORMANCE

The airfield at LHR is a dynamic asset and Figure 5 shows there has been a slight decline in airfield pavement performance over the last 4 years with overall average PCI values falling from

87 to 83 Figure 6 adds more detail by showing change in pavement performance by airfield

segment Each segment has been in a state of managed decline over the last 4 years except for

the recent intervention in Taxiways and Runway Holds

Figure 5 Comparison of Baseline Airfield PCI Over Time

Figure 6 Comparison of PCI Baselines over a 4 Year Period

As shown in Figure 6, Taxiways and Runway Holds have been subject to substantial maintenance intervention in 2015 whereas Runway Links, Runway Shoulders and Stands are in a

state of managed decline

Assuming that the performance of Runway Links and Stands were to be maintained at their current, 2016 performance levels, they would require intervention thresholds higher than those

published, as shown in Table 1 and Figure 7 Runway shoulders have been maintained using

lower intervention thresholds than published Table 1 also shows the intervention thresholds that

have been applied for Taxiways and Runway Holds in 2015

Table 1 Comparison of published intervention thresholds against current intervention

thresholds

Segment Current

Performance Level

Current Intervention Thresholds

Published Intervention Thresholds (Red/Amber)

The “Current” Intervention Thresholds are the intervention values required to maintain current performance or to achieve the performance level at which intervention took place Table

1 shows airfield pavements are currently maintained at performance levels above, on and below

those that are generated by the published (red/amber) intervention thresholds, set depending on

the asset criticality There is the opportunity therefore to identify the potential costs and savings

associated with applying the published intervention thresholds compared with those identified in

Current Performance Levels

Published Intervention Thresholds

Desired Performance Levels Runway

Hold

Runway Link

Runway Shoulder

TARGETING DESIRED PERFORMANCE

In targeting desired performance, it is important to find optimum intervention thresholds that minimize risk and cost without compromising the function of the asset in meeting organizational

objectives It is believed that for Heathrow’s airfield pavements this ‘optimum zone’ lies at the

published red/amber thresholds

It should be noted that intervening at the published red/amber threshold delivers an average performance level above the threshold due to interventions resetting the treated areas’ PCI values

to 100 Intervening at the published red/amber thresholds maintains three airfield segments in the

green range of performance with two in the amber range Although there is a small drop in

performance for some assets, risk is still well managed and the pavement condition can be

considered optimal Table 2 below shows, for each airfield segment excluding runways, their

current performance levels delivered by intervening at their respective, current intervention

thresholds The table goes on to show the “Desired” level of performance that would be achieved

if the published (red/amber) intervention thresholds were applied:

POTENTIAL SAVING

Table 2 shows there is only small drop in performance levels when the published (red/amber) intervention thresholds are applied compared to applying the current intervention thresholds

Therefore, potential costs and savings can be identified from optimizing cost, risk and

performance The effect of changing from current performance levels to those achieved by

applying published (red/amber) intervention thresholds is summarized in Table 3 below:

Table 3 Summary of performance levels and potential savings

Year Investment

Typical 5 Year Investment Period

Intervention Threshold Performance Resultant

Total £950,496,000 £237,624,000

Year Investment

Typical 5 Year Investment Period

Saving in Typical 5 Year Investment Period

(Red/Amber) Intervention Threshold

Resultant Performance

Total £661,081,000 £165,270,250 Saving [£] £289,415,000 £72,353,750 Saving in [$] $401,280,000 $100,320,000

It should be noted that the Runway Shoulders incur a cost (rather than a saving) when using the Red/Amber intervention thresholds compared to the current intervention thresholds This is

because the Runway Shoulders are currently subjected to maintenance intervention below the

published threshold Additional investment in this pavement segment would help to better

manage performance and reduce any undue risk

CONCLUSION

A new Decision Support Tool was developed to model the performance of the airfield

pavements and plan potential CapEx interventions for 1935 sections of pavement over a 20-year

period for London Heathrow

This paper explores the future cost of maintaining airfield pavements in their current condition, against targeting slightly lower but acceptable performance which reflects asset

criticality and associated risk It identifies potential savings derived from managing the pavement

to deliver slightly lower but acceptable levels of performance

It has been shown that by adjusting the intervention thresholds, to align with a targeted performance strategy, savings can be made with only a slight drop in airfield performance These

savings are comparable to those experienced in the highways industry where adopting

preventative treatment strategies yielded savings of around 50% over a 5-year investment period

In summary, a significant saving of up to £290m can be made over twenty years or in the order of £70m over a typical five-year investment period by moving from the current

interventions thresholds towards a more targeted, desired performance using published

intervention thresholds

REFERENCES

DMG 27, 2011 – A Guide to Airfield Pavement Design and Evaluation Design and Maintenance

Guide 27 Ministry of Defence, 3rd Edition February 2011 ACI – Airport Council International Website (LINK)

Hydronic Heated Pavement System Using Precast Concrete Pavement for Airport

Applications

Hesham Abdualla1; Halil Ceylan2; Sunghwan Kim3; Peter C Taylor4; Kasthurirangan

Gopalakrishnan5; and Kristen Cetin6

1Graduate Research Student, Iowa State Univ E-mail: abdualla@iastate.edu

2Professor, Iowa State Univ (corresponding author) E-mail: hceylan@iastate.edu

3Research Scientist, Iowa State Univ E-mail: sunghwan@iastate.edu

4Director, National Concrete Pavement Technology Center E-mail: ptaylor@iastate.edu

5Research Associate Professor, Iowa State Univ E-mail: rangan@iastate.edu

6Assistant Professor, Iowa State Univ E-mail: kcetin@iastate.edu

ABSTRACT

The use of deicing chemical has the potential to cause environmental and safety concerns and pavement deterioration Hydronic heated pavement systems (HHPS) have been widely used to

melt or prevent ice and snow accumulation on paved surfaces HHPS uses heated fluid circulated

through pipes embedded in the concrete pavement to warm the surface of the concrete The

objective of this study is to develop a conceptual design framework and construction guidance

for large-scale HHPS using precast concrete pavement (PCP) technology to expedite

construction work and minimize air travel disruption The detailed design and 3-D visualization

of construction procedures has been developed for HHPS using PCP technology The outcome of

this study will help contractors and transportation agencies to envision the constructability of

different components in HHPS, including tubing patterns and construction procedures

INTRODUCTION

Ice and snow accumulation on paved surfaces has the potential to reduce pavement surface’s skid resistance and thereby cause hazardous conditions that may lead to aircraft incidents and

accidents (McCartney 2014; FAA 2008) The use of deicing chemical agents and deployment of

snow removal equipment (SRE) to remove snow/ice has the potential to cause foreign object

damage (FOD) to aircraft engines, cause corrosion to the overall airplane structure, and lead to

undesirable environmental issues (Xi and Patricia 2000) The use of these methods is also

typically costly and time-consuming (Anand et al 2016)

Hydronic heated pavement systems (HHPS) have been used to melt or prevent ice and snow accumulation on paved surfaces by using heated fluid circulated through pipes embedded in

concrete pavement to warm the surface of the concrete and thereby melt ice and snow The

cooled fluid is recirculated through a heat source that reheats the fluid for each cycle (ASHRAE

2015) A HHPS using a geothermal well as a heat source was constructed for the aprons at the

Greater Binghamton Airport (BGM) located in Johnson City, New York The total surface area

of that project was 297 m2 at a construction cost of $ 1,600,000 (Guney and Bowers 2016) The

Oslo International Airport at Gardermoen in Norway implemented HHPS using an Aquifer

Thermal Energy Storage (ATES) system able to heat the aircraft parking with a total area of 700

m2 (Barbagallo 2013) The system was also supported by an electric and oil-fired boiler to help

increase the design energy density to 248 W/m2 since the ATES alone was not capable of

providing sufficient heating energy

Precast concrete pavement (PCP) has demonstrated satisfactory performance in bridges,

pavements, buildings, and airfield construction It provides high strength, low permeability, and

low cracking potential; these features are consequences of preparing the panels off-site where

quality control can be more effectively implemented Using a PCP technique instead of

cast-in-place for construction of pavements can expedite the construction process by eliminating the

need for concrete strength-gaining time from the on-site construction procedure (Merritt et al

2004; Priddly et al 2013) PCP technology enables rapid repair of pavement facilities and can be

beneficially applied in situations where extended road closures could increase road congestion

and result in increased lost work time, fuel consumption, and user-delay costs (Kohler at al

2004) A study has shown that estimated daily user-delay costs for a four-lane divided facility

carrying 50,000 vehicles per day can be as high as $383,000 per day for 24-hour lane closure,

compared to only $1,800 per day for nighttime lane closure only (Priddly et al 2013) Since it is

important that flight operations be resumed in the shortest time possible, often allowing only 4 to

6 hours to complete repairs, the PCP technique can also be a good choice for minimizing airport

pavement facility downtime (Bly et al 2013)

While PCP construction in the U.S began between 50 and 80 years ago, it did not appear to

be a cost-effective technique at that time because of a lack of technical information which

resulted in an increased labor requirements for installation While many US highway agencies

did not implement PCP technology for a long time, during the last 10 years, several U.S

highway agencies began implementing the technology and consequently the installation price has

dropped by more than 50% The Strategic Highway Research Program 2 (SHRP2) project R05

provided a guideline for design and construction of different PCP applications, and developed

specific guidelines for project selection, design, fabrication, installation, and rehabilitation of

PCP systems (Tayabji et al 2013) PCP can be used to repair distressed areas of existing

pavement that represent either small areas of localized distress or extended long-distance

distressed areas in the pavement

Because of increased use of PCP implementation, several agencies have participated in developing specifications and guidelines for such systems The American Association of State

Highway and Transportation Officials (AASHTO) established a Technology Implementation

Group (TIG) during 2006 that developed a specification for fabrication and construction of PCP

and guidelines for the design of PCP systems (Tayabji et al., 2009) Various PCP types have

been assessed and compared to conventional concrete pavement systems in terms of design

concepts, field installation procedures, advantages, limitations, and costs (Chang et al 2004)

The specific PCP types evaluated include Super-slab, Full-depth repair, Stitch-in-time, and

Uretek methods (Chang et al 2004) While PCP has been used both in Europe and in the US for

rapid repair, rehabilitation, and reconstruction of asphalt and concrete pavements, no guidance

has been established for using PCP for large-scale heated airport pavements

OVERALL CONCEPTUAL DESIGN OF HHPS

HHPS are typically closed-loop systems, as shown in Figure 1 After the fluid releases heat into the pavement, it returns to the heat source to be sent back through the pipes (Barbagallo

2013) The fluid can be heated by different types of fluid heaters, including geothermal hot

water, underground thermal energy storage (UTES), boilers, and heat exchangers (FAA 2011),

and the selection is based on availability at the project site Geothermal water would be

considered efficient in locations with good geothermal potential (Joerger and Martinez 2006)

Geothermally heated hydronic systems often incorporate heat pumps to obtain greater heating

capacity, because in many places ground temperatures are not high enough to heat the concrete

sufficiently to melt the snow (Minsk 1999) The efficiency of HHPS in melting ice and snow

depends on different factors, including fluid temperature, pavement conductivity, pipe depth, and

pipe spacing (Ceylan et al 2014)

Figure 1 Schematic of HHPS

Figure 2 Hydronic pipe pattern

Construction materials: The components of HHPS include heat transfer fluid, piping, a

fluid heater, pumps, and controls (ASHRAE 2015) Pipes can be made of metal, plastic, or

rubber, but a drawback of steel pipes is their susceptibility to rusting, so the use of steel

embedded in pavement is not a common practice An attractive alternative to steel pipe is plastic

pipe such as polyethylene (PE) or cross-linked polyethylene (PEX) because it is

resistant and has a lower material cost Polyethylene and cross-linked polyethylene withstand

fluid temperatures up to 60oC and 93oC, respectively (ASHRAE 2015) A common practice is to

use propylene glycol-water as a heat transfer fluid because of its moderate cost, high specific

heat, and low viscosity

Figure 3 Work sequence

Piping design pattern: A hydronic slab’s piping can be arranged into different patterns – for

example, serpentine or slinky – to provide uniform heat on the hydronic slab surface and prevent

ice and snow accumulation A serpentine pattern (Figure 2) is commonly used in melting snow

and ice on paved surfaces (Spitler and Ramamoorthy 2000) In the serpentine pattern, straight

pipes are evenly spaced and connected to a manifold that uses U-shaped pipes The slinky pattern

is formed by configuring pipes into a circular shape, with each circle overlapping the next

adjacent one An HHPS was constructed into a 44.5 m long and a 17.7 m wide bridge deck in

Amarillo, Texas using a serpentine pipe pattern (Minsk 1999) The detailed pipe pattern can be

designed using industrial software such as LoopCAD 2016 (Avenir Software Inc 2017) to

generate the circuit layout drawings and zones for the HHPS project site, as well as perform

detailed calculations such as energy density based on ASHRAE methods (ASHRAE 2015)

LoopCAD software offers flexible tools for adjusting pipe layout drawings and generating loop

lengths

Heat requirements: The minimum design requirement of a heated pavement system (HPS)

is that it must be capable of keeping a surface condition of “no worse than wet” and maintain a

surface temperature above the freezing point both before and during snow accumulation (FAA

2011) The heating requirement for snow melting depends on the rate of snowfall, the air

temperature, the relative humidity, and the wind speed Equation 1 is the steady-state

energy-balance equation for required heat flux (q ), expressed in (W/m O 2)

whereq , s q , m A , r q , h q are sensible heat flux (W/m e 2), latent heat flux (W/m2), snow-free area

ratio, convective and radiative heat flux from a snow-free surface (W/m2), and heat flux of

evaporation (W/m2), respectively The detailed equation definition and parameters are available

in the ASHRAE 2015 HVAC Applications Handbook (ASHRAE 2015) and the FAA advisory

Circular AC 150/5370-17 (FAA 2011) Equation (1) does not account for back and edge of the

slab heat losses that increase the total heat slab output (q ); these can vary from 4 to 20% o

depending on factors such as pavement construction, operating temperature, ground temperature,

or back exposure (Abdualla, et al 2016) A finite-element (FE) method can also be used as a tool

for estimation of the required heat flux and the snow/ice melting time for HHPS (Mallick et al

2012)

The heat requirements for a snow-melting installation are based on system classifications I, II

or III Class I (minimum): residential walks or driveways, class II (moderate): commercial

sidewalks and driveways, and class III (maximum): toll plazas of highways and bridges; aprons

and loading areas of airports; hospital emergency entrances (FAA 2011) These classifications

are correlated with snow-free area (A values Class I has a snow-free area ratio of 0 and the r)

surface and can be covered with a sufficient thickness of snow before beginning to melt the snow Class II has a snow-free area ratio of 0.5, the surface must be kept clear of snow accumulation,

and a wet surface is acceptable Class III has a snow-free area ratio of 1, the surface must melt

falling snow quickly, and the surface must remain dry

Figure 4 Hydronic heated slab fabrication using PCP

SYSTEMATIC DESIGN AND CONSTRUCTION CONSIDERATION FOR

LARGE-SCALE HHPS USING PCP

Figure 3 shows the construction steps required for constructing a HHPS using PCP The major difference between the construction of HHPS using PCP and typical PCP installation is

that a HHPS using PCP requires installation that allows hot fluids to run through the pipes and

thereby release heat to warm the paved surfaces and melt ice and snow The construction

sequence for a HHPS using PCP involves the following three major steps:

Step 1: Fabricate hydronic heated slab off-site Figure 4 presents a 3D visualization for

hydronic heated slab fabrication using PCP with connected pipes placed inside each panel A

hydronic heated slab can be fabricated off-site using PCP (Figure 4) with formwork designed to

facilitate placement of the pipes, wire mesh, dowel bars, and slots (Figure 4a and Figure 4b) The

Figure 5 Hydronic heated slabs assembly

formwork has open areas to permit inlet and outlet pipes to be connected to other hydronic slabs

The pipe is placed on top of wire mesh to elevate it closer to the surface and hold it there while

the concrete is poured A minimum of 50 mm of concrete cover extending above the top of the

pipe is typically required (ASHRAE 2015) The pipe pattern can be designed for a particular job

site to provide sufficient heat for melting ice and snow After securing the pipe, concrete is

poured into the formwork and then is screeded and cured before transferring the final product to

a construction site (Figure 4c and Figure 4d)

Step 2: Prepare the base layer and place hydronic heated slabs Prepare and compact

both the subgrade and base layers to satisfy density requirements and identify the location of

manifolds to define pipe circuit length and pattern The pipe pattern and pipe spacing can be

adjusted based on the project site, geometry, size, required energy, and locations The slab has

dowel bars and slots to provide load transfer like a traditional PCP structure, and can be

transported and placed into position at the project site The pipes can be connected to each other

at the joints after placing the hydronic slabs and filling the voids of the dowel slots and ensuring

that the desired panel elevation is achieved Figure 5a and Figure 5b show the pipe pattern and

the connections between hydronic-heated slabs, respectively

Step 3: Connect pipes to energy source After identifying and connecting the manifold to

the pipes, the manifold should be connected to the heat source to permit fluid to circulate in the

embedded pipe through a heat source (Figure 6) A HHPS can be operated automatically using a

control system to turn the system on and off based on a set-point temperature value that can be

measured by temperature sensors embedded in the concrete To provide satisfactory operation

the HHPS should be warmed up before snow and ice accumulates on the surface (ASHRAE

technology The results of this work can be listed as follows:

 Construction considerations and 3D visualization for HHPS using PCP technology were developed to ensure that the system would perform as desired and to develop more robust construction schemes, high-performance heated airport pavement systems, and good construction practices

 A HHPS using PCP is a viable option for accelerating construction procedures, reducing labor costs, and minimizing traffic disruptions HHPS PCP technology also enhances heat distribution to the pavement surface since the HHPS slabs are fabricated offsite where