Author(s)

Murad Al Qurishee¹, Weidong Wu², Babatunde Atolagbe³, Joseph Owino⁴, Ignatius Fomunung⁴, Said El Said¹, Sayed Mohammad Tareq²

¹Tennessee Department of Transportation, Nashville, Tennessee, USA.
²University of Tennessee at Chattanooga, Chattanooga, Tennessee, USA.
³Maryland Department of Transportation, Baltimore, Maryland, USA.
⁴Department of Civil & Chemical Engineering, The University of Tennessee at Chattanooga, Chattanooga, Tennessee, USA.

Manual inspections of infrastructures such as highway bridge, pavement, dam, and multistoried garage ceiling are time consuming, sometimes can be life threatening, and costly. An automated computerized system can reduce time, faulty inspection, and cost of inspection. In this study, we developed a computer model using deep learning Convolution Neural Network (CNN), which can be used to automatically detect the crack and non-crack type structure. The goal of this research is to allow application of state-of-the-art deep neural network and Unmanned Aerial Vehicle (UAV) technologies for highway bridge girder inspection. As a pilot study of implementing deep learning in Bridge Girder, we study the recognition, length, and location of crack in the structure of the UTC campus old garage concrete ceiling slab. A total of 2086 images of crack and non-crack were taken from UTC Old Library parking garage ceiling using handheld mobile phone and drone. After training the model shows 98% accuracy with crack and non-crack types of structures.

Bridge Girder, Convolution Neural Network, Crack Detection, Structural Health Monitoring, Infrared Thermography

Qurishee, M. , Wu, W. , Atolagbe, B. , Owino, J. , Fomunung, I. , Said, S. and Tareq, S. (2020) Bridge Girder Crack Assessment Using Faster RCNN Inception V2 and Infrared Thermography. Journal of Transportation Technologies, 10, 110-127. doi: 10.4236/jtts.2020.102007.