STUDENT SPOTLIGHT: Each month, or every other month, a student will provide a 1-page illustrated abstract of the research they are currently conducting. This is a wonderful opportunity for the student, for our International Society for Concrete Pavements (ISCP) Members, and for the transferring and sharing technology/research through our concrete paving industry.
The ISCP “STUDENT RESEARCH SPOTLIGHT” for May 2023 is Christian A. Sabillon, a Ph.D. student at The University of Texas at Austin (Austin, Texas, USA).
BIO:
Christian A. Sabillon is a graduate research assistant at The University of Texas at Austin’s Department of Civil, Architectural and Environmental Engineering (CAEE). He holds a Bachelor’s and Master’s degree in civil engineering from the same university, which he earned in 2019 and 2021, respectively. Currently, he is pursuing a Master’s degree in Statistics and Data Science, and a Ph.D. in transportation engineering.
Since 2017, Christian has been involved in transportation engineering research, working with Dr. Jorge Prozzi. His expertise includes database management, statistical analysis, feature engineering, and the application of machine learning and artificial intelligence models. He has two years of experience as a teaching assistant in engineering statistics courses for both undergraduate and graduate students and has also trained undergraduate research assistants on coding and applying machine learning models.
His research focus is on pavement performance modeling, pavement design and management, and pavement surface properties. Recently, he worked on project 0-7031 for the Texas Department of Transportation (TxDOT) as part of a team that developed an innovative way of predicting pavement friction using only the pavement’s texture information. He also contributed to other TxDOT projects, including 0-6988, where he developed an algorithm that can determine when and where maintenance work is needed on a roadway by analyzing pavement distresses over time.
TITLE:
Network-Level Determination of Rigid Pavement Texturing Techniques using Field Measurements from Line Laser Scanners or Cameras
It is widely known that strong correlations exist between pavement texture and other surface characteristics like skid resistance. However, those relations are not straightforward and have a strong dependency on the type of pavement (e.g., rigid vs flexible vs surface treatment), the type of mix used (e.g., dense-graded vs gap-graded) and the type of surface texturing technique used (e.g., diamond grinding vs transverse tining). This implies that having accurate and detailed surface information for the pavements across the highway network has significant potential to enhance the performance of both performance models for pavement distresses and prediction models for skid resistance, noise, and water splash to name a few. Nonetheless, in practice, detailed pavement surface information is often unknown and must be guessed based on an expert’s opinion. Thus, to address this issue, this project seeks to develop an objective classification model capable of identifying different concrete pavement texturing methods with a high degree of accuracy using dynamically collected measurements from either a line laser scanner or high-speed cameras all measured with data collection prototypes developed in-house, as shown in the image below.
The research team developed a prototype that incorporates a high-power line-laser scanner oriented at a 45-degree angle to capture both transverse and longitudinal features on rigid pavements along either wheel path. In parallel, the research team tested a prototype consisting of a machine vision camera was installed on a vehicle. The prototype was installed on the side of a vehicle with strong magnetic camera mounts alongside two high-power spot lights to simulate midday sunlight, whenever the vehicle travels past dark or shadowed out places along the road. The two-dimensional surface profiles and images obtained from the laser scanner and camera, respectively, are then used to train, test, and validate a supervised machine learning classifier.
Preliminary results from classification methods like K nearest neighbor, decision trees, and generalized linear models are showing a performance of at least 90% when texture statistics computed from the surface profiles are used, whereas using images on a convolutional neural network results in accuracies that are nearly flawless (i.e., 100% accuracy). The research team is now currently determining which method is the best to use in terms of efficiency, intuitiveness, simplicity, and accuracy. The preliminary results of this research indicate that soon, it will be possible to add either a laser sensor or a camera to the survey vans used to measure pavement distress, and have them also predict detailed pavement surface information with high accuracy and at a network level. The following image shows a sample of the field pictures that were taken to classify rigid pavement texturing techniques using image recognition. From left to right, starting at the top, there is conventional diamond grinding, diamond grooving, new generation diamond grinding, transverse tining, artificial turf drag and burlap drag.
ISCP would like to feature a “STUDENT RESEARCH SPOTLIGHT” each month, or every other month. If you would like to nominate a student, or if you are a student and would like to nominate yourself or a colleague, please send ISCP an email to: newsletter@concretepavements.org
ALL SPOTLIGHTS:
DECEMBER 2021—Inaugural: Katelyn Kosar, Phd Student-Department of Civil and Environmental Engineering, University of Pittsburgh (Pitt): www.concretepavements.org/2021/12/14/new-at-iscp-student-research-spotlight/
JANUARY 2022: Aniruddha Baral, Ph.D. Candidate-Department of Civil and Environmental Engineering, University of Illinois-Urbana-Champaign: www.concretepavements.org/2022/01/15/student-research-spotlight-jan-2022/
FEBRUARY 2022: Jordan Ouellet, Tech, BEng, MASc, PhD Candidate, Teaching and Research Assistant, University of Illinois at Urbana-Champaign: www.concretepavements.org/2022/02/26/student-research-spotlight-february-2022/
MARCH 2022: Sampath Kumar Pasupunuri, Ph.D. candidate, Pavement Engineering-School of Civil Engineering, University of Nottingham, UK: https://www.concretepavements.org/2022/03/31/student-research-spotlight-march-2022/
APRIL 2022: Anupam B R, Pursuing his doctorate-Indian Institute of Technology Bhubaneswar, India: https://www.concretepavements.org/2022/04/15/student-research-spotlight-april-2022
MAY 2022: Andréia Posser Cargnin, Ph.D. Candidate, Polytechnic School, University of São Paulo (São Paulo, Brazil): https://www.concretepavements.org/2022/05/09/student-research-spotlight-may-2022/
JUNE 2022: Charles Donnelly, Ph.D. Candidate, Department of Civil and Environmental Engineering, University of Pittsburgh (Pittsburgh, USA): https://www.concretepavements.org/2022/06/24/student-research-spotlight-june-2022/
JULY 2022: Amir Malakooti, Ph.D. Candidate, Department of Civil and Environmental Engineering, Iowa State University (Ames, Iowa, USA): https://www.concretepavements.org/2022/07/30/student-research-spotlight-july-2022/
AUGUST 2022: Haoran Li, Ph.D. Candidate, Department of Civil and Environmental Engineering, University of Pittsburgh (Pittsburgh, USA): https://www.concretepavements.org/2022/08/24/student-research-spotlight-august-2022/
SEPTEMBER 2022: Sumit Nandi, Ph.D. Candidate, Department of Civil Engineering, Indian Institute of Technology Roorkee (Roorkee, India): https://www.concretepavements.org/2022/09/18/student-research-spotlight-september-2022/
OCTOBER 2022: Eric Ribeiro da Silva, Ph.D. Candidate, Department of Civil Engineering, Polytechnic School of the University of São Paulo (São Paulo, Brazil): https://www.concretepavements.org/2022/10/20/student-research-spotlight-october-2022/
NOVEMBER 2022: Zachary Brody, Ph.D. Candidate, Department of Civil and Environmental Engineering, University of Pittsburgh (Pittsburgh, USA): https://www.concretepavements.org/2022/11/18/student-research-spotlight-november-2022/
DECEMBER 2022: Jesús Castro Pérez, Ph.D. Candidate, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign (Urbana, USA): https://www.concretepavements.org/2022/12/17/student-research-spotlight-december-2022/
JANUARY 2023: Dan King, Ph.D. Candidate, Department of Civil and Environmental Engineering, Iowa State University (Ames, USA): https://www.concretepavements.org/2023/01/26/student-research-spotlight-january-2023/
FEBRUARY 2023: Kathryn Kennebeck, Ph.D. Candidate, Department of Civil and Environmental Engineering, University of Pittsburgh (Pittsburgh, USA): https://www.concretepavements.org/2023/02/28/student-research-spotlight-february-2023/
MARCH 2023: Sinan Kefeli, Ph.D. Candidate, Department of Civil and Environmental Engineering, Iowa State University (Ames, USA): https://www.concretepavements.org/2023/03/13/student-research-spotlight-march-2023/
APRIL 2023: Niwesh Koirala, Ph.D. Candidate, Department of Civil and Environmental Engineering, Texas Tech University (Lubbock, USA): https://www.concretepavements.org/2023/04/07/student-research-spotlight-april-2023/