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Podium Presentation at the Conference

Highway & Airfield Pavement Numerical Modeling and Analytics

Session: AIRFIELDS - Data Collection, Organization, and Machine Learning Applications

1373542 - Data Organization and Analysis of Flexible Pavement Test Sections at the National Airport Pavement Test Facility

Friday, June 16, 2023

2:00 PM – 1:45 PM CST

Room: 400

TRACK: I

Primary Author(s)

Lara Diab (she/her/hers)

Graduate Research Assistant
University of Illinois at Urbana-Champaign
Urbana, IL, United States

Presenter(s)

Qingwen Zhou

Research Assistant
University of Illinois Urbana-Champaign
Champaign, IL, United States
Lara Diab (she/her/hers)

Graduate Research Assistant
University of Illinois at Urbana-Champaign
Urbana, IL, United States

Co-Author(s)

Egemen Okte

Research Assistant Professor & Lecturer
University of Massachusetts at Amherst
Rantoul, Illinois, United States

Co-Author(s)

Imad L. Al-Qadi, PhD, PE, Disti.M.ASCE

Grainger Distinguished Chair in Engineering and ICT Director
University of Illinois at Urbana-Champaign
Urbana, Illinois, United States
Navneet Garg, PhD

Program Manager, National Airport Pavement and Materials Research Center
Federal Aviation Administration
Atlantic City, New Jersey, United States

The National Airport Pavement Test Facility (NAPTF) is a state-of-the-art, full-scale testing facility for airport pavements operated by the Federal Aviation Administration (FAA). The primary purpose of this program is to advance airfield pavement design methodologies by understanding pavement behavior under varying aircraft loadings. This paper presents the findings of an exploratory data analysis, visualization, and summary of flexible pavements tested in construction cycles 1, 3, 5 and 7. Each construction cycle serves a different specific objective and is split into multiple test sections. Both dynamic (i.e., stress, strain and deflection) data and static (i.e., temperature and moisture) data were analyzed. Visualization of the data was performed for single pass responses in addition to multiple pass responses aiming at comparing different testing conditions. Determining critical track locations for each sensor allowed summarizing the dataset for each construction cycle into an organized, clean and relatively smaller-size dataset. For a given sensor, a critical track is the track on top of or is the closest to the top of the sensor. For example, the size of the database for Construction Cycle 7 was reduced from 7GB to 40MB. The generation of a summarized database is essential for developing machine learning prediction models that can ultimately be used for efficient pavement analysis.

Learning Objectives:

Attendees can expect to learn the following from this session:

Upon completion, participants will be able to explore, visualize and summarize the structural response, loading, and environmental data of different construction cycles built by Federal Highway Aviation
Upon completion, participants will be able to determine critical track locations of different sensors and critical responses of each construction cycle under varying aircraft loading
Upon completion, participants will be able to reduce the original database (in GB) collected from sensors in each construction cycle to a condensed database with only useful information (in MB).