Faculty Profile for Dr. Xiaohua Luo

profile photo for Dr. Xiaohua Luo
Dr. Xiaohua Luo
Lecturer — Ingram School of Engineering
IGRM 2203
phone: (512) 245-3726
Scholarly/Creative Work
Awards
Grants
Service

Biography Section

Biography and Education

 2016-2019 Ph.D. in Civil Engineering
Jackson State University, Jackson, Mississippi
2014-2017 M.S. in Intelligent Traffic Technology
Zhejiang Normal University, Zhejiang, China
2010-2014 B.S. in Transportation Engineering
Zhejiang Normal University, Zhejiang, China

Research Interests

 Data Analysis for Transportation Asset
Pavement Management Systems
Automated Pavement Condition Survey
Machine Learning enhanced 3D Image Processing for Pavement Condition Evaluation
Alternative Delivery Projects (Design Build/Warranty Projects)
Life-cycle Cost Analysis of Pavement

Selected Scholarly/Creative Work

  • Gong, H., Luo, X., Tao, J., & Wang, F. (n.d.). Study of Key Factors in Pavement Distress Segmentation Using Deep Learning Techniques.
  • Tao, J., Luo, X., Wang, F., Faieq, A., Gong, H., & Qiu, X. (n.d.). Achieving Quality Assurance in Receiving Automated Pavement Condition Data: Threshold Development and Error Pattern Analysis. Transportation Research Record.
  • Luo, X., Tao, J., Wang, F., Faieq, A., Gong, H., & Hong, F. (n.d.). Enhancing Reliability in Automated Pavement Condition Data: A Data Quality Check Approach for Highway Agencies. Transportation Research Record.
  • Tao, J., Wang, F., Luo, X., Gong, H., Qiu, X., & Faieq, A. (2023). Developing the Quality Assurance Thresholds for Screening Automated Pavement Condition Data Using Prediction Interval Methods. International Journal of Pavement Engineering, 24(1). https://doi.org/https://doi.org/10.1080/10298436.2023.2222335
  • Gong, H., Tesic, J., Tao, J., Luo, X., & Wang, F. (2023). Automated Pavement Crack Detection with Deep Learning Methods: What Are Main Factors and How to Improve the Performance? Transportation Research Record. https://doi.org/https://doi.org/10.1177/036119812311613

Selected Awards

  • Award / Honor Recipient: Transportation Research Board. August 2020 - 2020
  • Award / Honor Recipient: AASHTO. 2017

Selected Grants

  • Wang, Feng (Principal), Jimenez, Jesus (Co-Principal), Luo, Xiaohua (Co-Principal), Das, Subasish (Supporting), Hong, Feng (Supporting). Administration of the Dwight David Eisenhower Transportation Fellowship Program (DDETFP) Local Competition at Texas State University, Federal Highway Administration, Federal, $25500. (Funded: December 2022 - August 31, 2023). Grant.
  • Wang, Feng (Principal), Jimenez, Jesus (Co-Principal), Luo, Xiaohua (Co-Principal). Administration of the Dwight David Eisenhower Transportation Fellowship Program (DDETFP) Local Competition at Texas State University, Federal Highway Administration, Federal, $25500. (Funded: December 2021 - August 31, 2022). Grant.
  • Wang, Feng (Principal), Luo, Xiaohua (Co-Principal), Minifie, Jana Roberta (Co-Principal), Gong, Haitao (Supporting), Tao, Jueqiang (Supporting). StarImage: cost-effectiveness improved automated pavement condition survey system, NSF, Federal, $50000. (Submitted: August 27, 2020, Funded: January 2021 - March 2023). Grant.
  • Wang, Feng (Principal), Luo, Xiaohua (Co-Principal). Improve Data Quality for Automated Pavement Distress Data Collection, Texas Department of Transportation, State, $449720. (Funded: September 2020 - August 2024). Grant.
  • Wang, Feng (Principal), Luo, Xiaohua (Co-Principal), Gong, Haitao (Co-Principal). Development of deep learning based automated data collection technology for coastal highway pavements, CREATE UTC, Texas State University, $115136. Grant.

Selected Service Activities

University Mentor
Dwight David Eisenhower Transportation Fellowship Program (DDETFP)
2023-Present
University Mentor
TXST Faculty Innovation Accelerator Program
2023-Present
University Mentor
Dwight David Eisenhower Transportation Fellowship Program (DDETFP)
2022-Present
University Mentor
Dwight David Eisenhower Transportation Fellowship Program (DDETFP)
2021-Present
Reviewer / Referee
Applied Sciences
2021-Present