Alvarez, JK, Sutjipto, S & Kodagoda, S 2017, 'Validated ground penetrating radar simulation model for estimating rebar location in infrastructure monitoring', Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, IEEE Conference on Industrial Electronics and Applications, IEEE, Siem Reap, Cambodia, pp. 1460-1465.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. Biogenic sulphide corrosion of reinforced concrete sewer pipes is an ongoing problem for wastewater governing bodies. Ensuring Workplace Health and Safety (WHS) is also an issue due to the harsh nature of sewer environments. As such, research into technologies that allow for automatic unmanned site assessments are of major priority to wastewater managing utilities. The use of Ground Penetrating Radar (GPR) is currently being investigated for it's ability to provide subsurface images. However, the GPR technology has not been tested and validated in harsh sewer environments. It is anticipated that the GPR interpretation can be hindered by low signal to noise ratio. As data driven machine learning techniques have proven to work in higly challenging data, our intenetion is to apply such techniques in GPR data processing. However, this is hindered by the lack of large amount of training data as it is prohibitively hard to collect such real experimental testing data. Thus, the aim of this study is to validate a ground penetrating radar simulation software, gprMax, and test it for suitability in generating realistic, big data sets with which to train the aforementioned data driven machine learning models supplemented with actual sewer crown data. The results of the study is the validation of the GPR simulator, tuned and able to generate reasonably realistic data. A novel concrete analog was also developed to allow for ease of testing of various parameters such as rebar cover depths and rebar spacing.
Alvarez, JK & Kodagoda, S 2018, 'Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring', IEEE Conference on Industrial Electronics and Applications, Wuhan, China, pp. 611-611.View/Download from: UTS OPUS or Publisher's site
The corrosion of reinforced concrete sewer pipes in aging infrastructure is a serious ongoing issue and as such, research into technologies that allow for autonomous site assessments are of major priority to wastewater managing utilities. The use of Ground Penetrating Radar (GPR) is being investigated for providing sub-surface images of sewer crowns. Due to the nature of GPRs, the analysis of a radargram for identifying sub-surface features is non-intuitive and usually require the use of an expert. Traditional methods to help ease analysis involve the use of Synthetic Aperture Radar (SAR) and migration techniques. These techniques refocus dipping and point reflectors to be closer to their true shape but require an accurate velocity model to be effective. This is not always readily available and difficult to estimate especially in regards to sewer conditions. We instead provide an alternative and present a deep learning framework for transforming ground penetrating radargrams into sub-surface permittivity maps. An evaluation of various state-of-the-art deep learning architectures is also conducted, comparing the performance of different objective functions and identifying current limitations. This work provides the base for further exploration of the application of deep learning for use in infrastructure monitoring.
Alvarez, JK, Sutjipto, S & Kodagoda, S 2017, 'Validated Ground Penetrating Radar Simulation Model for Estimating Rebar Location in Infrastructure Monitoring', 2017 IEEE 12th Conference on Industrial Electronics and Applications, Siem Reap, pp. 1457-1462.View/Download from: UTS OPUS
Thiyagarajan, K, Kodagoda, S & Alvarez, JK 2016, 'An Instrumentation System for Smart Monitoring of Surface Temperature', 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.View/Download from: UTS OPUS or Publisher's site
Alvarez, JK, Abeywardena, DA, Shi, LS & Kodagoda, SK 2015, 'Using Hidden Markov Models to Improve Floor Level Localization', Website Proceedings of the Australasian Conference on Robotics and Automation 2015, Australasian Conference on Robotics and Automation, ARAA, Canberra.View/Download from: UTS OPUS