Bridge Girder Crack Assessment Using Faster RCNN Inception V2 and Infrared Thermography

Murad Al Qurishee,Weidong Wu,Babatunde Atolagbe,Joseph Owino,Ignatius Fomunung,Sayed Mohammad Tareq,Said El Said

doi:10.4236/jtts.2020.102007

Murad Al Qurishee, Weidong Wu + Show 5 more

Open Access

https://doi.org/10.4236/jtts.2020.102007

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Abstract

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.

Highlights

The bridge girder health assessment using deep learning with infrared thermography and drone is pushing the technology of traditional health monitoring
First we developed a 22-layer model and used an open source model called faster RCNN with Inception V2
The faster RCNN with Inception V2 model can successfully detect the crack and non-crack image based on the training output

Summary

Introduction

Their integrity, safety, sustainability, reliability and maintenance are as important as the initial construction. These factors, are often impeded by deteriorating effects due to age and long-term service and exposure to harsh environmental conditions such as wind and earthquakes. In order to mitigate this fast deteriorating effect of bridges, the science of health monitoring emerged. Modern challenges in infrastructure development transcend merely building of roads, bridges, and other social facilities, but rather exploring means to mitigate the deterioration. According to the ASCE 2017 report card, the US infrastructure received a cumulative grade of D+ (i.e. fair condition) with the bridges averaging a grade of C+ (i.e. good) [1] [2]

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