1. Pavement Image Data Set for Deep Learning: A Synthetic Approach This research aims to explore the viability of using synthetic pavement image data to train convolutional neural networks (CNNs) for automated pavement crack detection. A procedural approach of generating synthetic pavement crack image data is proposed. The results indicate that training a crack detection Read More…
Month: January 2023
Three-Dimensional Segmentation of Air-Void System in Hardened Concrete Using Photometric Stereo and Artificial Intelligence Methods
To assure the frost resistance of conventional concretes, it is necessary to quantify its air void structure. The most widely used method for measuring air void parameters in hardened concretes are the microscopy-based methods outlined in ASTM C457/457M-16. This standard sets out three test procedures to perform microscopical determinations of the air content of hardened Read More…
Dr. Wang and his team develop AI-based pavement condition evaluation system
https://tilab.wp.txstate.edu/2023/01/02/dr-wang-and-his-team-develop-ai-based-pavement-condition-evaluation-system/
Dr. Wang and his team develop AI-based pavement condition evaluation system
Recently, Texas State University was awarded with the research project “Artificial Intelligence for Pavement Condition Assessment from 2D/3D Surface Images” by Texas Department of Transportation (TxDOT). The proposal of this project was initiated by Dr. Feng Wang and his research team, along with Dr. Jelena Tešić from Department of Computer Science. The two doctoral students, Read More…
Texas State receives $250K grant to study how to use AI when collecting pavement condition data
by: Candy Rodriguez Posted: Aug 25, 2022 / 09:14 AM CDT Updated: Aug 25, 2022 / 12:19 PM CDT SAN MARCOS, Texas (KXAN) — For the next two years, these students will use machine learning, a type of artificial intelligence, to identify and measure any cracking on roads across the country. “We probably will travel Read More…