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

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.

Creating a Dataset to Boost Civil Engineering Deep Learning Research and Application

With cutting edge deep learning breakthrough,numerous innovations in many fields including civil engineering are stimulated.However,a fundamental issue that civil engineering research community currently facing is lack of a publicly available,free,quality-controlled and human-annotated large dataset that supports and drives civil engineering deep learning research and applications on such as intelligent transportation including connected vehicle,structural health monitoring,and bridge inspection.This paper is a general discussion about demanding needs and construction of a long-anticipated dataset for researchers and engineers in civil engineering and beyond for providing critical training,testing and benchmarking data.The establishment of such a free dataset will remove a major hurdle and boost deep learning research in civil engineering and we hope this work will urge researchers,engineers,government agencies and even computer scientists to work together to start building such datasets.A framework has been developed for the proposed database.Also,some pilot study databases were developed for concrete crack detection,pavement crack detection using normal and infrared thermography,as well as pedestrian and bicyclist detection.A convolution neural network model called Faster RCNN was deployed to check the detection accuracy and a 98%detection accuracy of the proposed datasets was obtained.

Analysis of cost effective pavement treatment and budget optimization for arterial roads in the city of Chattanooga

Many transportation agencies lack sufficient funds to maintain and repair roads, which results into increased pavement maintenance cost. Pavement Management System （PMS） has demonstrated to be an essential tool for proper management of infrastructure and proper utilization of available funds. The University of Tennessee at Chattanooga utilized Micropaver software as PMS tool to conduct a pavement management analysis of principal arterials in the City of Chattanooga. The study used the City of Chattanooga pavement database to create the current and future pavement conditions. Maintenance and repair （M＆R） planning analysis was also performed in order to determine the most cost-effective treatment and suggest the optimum utilization of funds for the city. An analysis of five budget scenarios was conducted for a five-year plan using the critical pavement condition index （PCI） method （ASTM D6433）. Results show that the backlog elimination budget would be the best scenario because it increases the pavement condition and eliminates the backlog of major maintenance and repairs over the five-year period. The unlimited budget seems though ideal, it does not improve pavement condition. Maintaining current condition and limited budget scenarios would increase both the backlog and the total cost of maintenance and repairs over the analysis period.