A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Rebar detection and localization for bridge deck inspection and evaluation using deep residual networks
https://orcid.org/0000-0001-8556-7310
Abstract Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) of civil infrastructure has been an active area of research for the past few decades. Due to rising costs, safety issues and error of human inspection methods, automated methods for bridge inspection and maintenance are being proposed. The purpose of this research is to develop an automated rebar detection and localization system utilizing supervised (Deep Residual Networks) and unsupervised (K- means clustering) techniques. Data has been collected from nine bridges using Ground Penetrating Radar (GPR) sensors. The performance of the proposed rebar detection and localization system has been evaluated on a wide-range of performance metrics, which emphasize the superior performance of the proposed technique over existing methods. The results reveal positive correlation between number of layers of networks, training time and other performance metrics. The overall performance of the proposed system is also dataset-dependent with factors such as noise artefacts, reflections and visual quality of rebar profiles.
Highlights A novel rebar detection and localization system has been proposed for non-destructive evaluation of bridge decks GPR sensors were used to extract data from nine bridges Data from four out of total of nine bridges have not been used in any previous study The performance of rebar detection and localization has been examined using a wide-array of performance evaluation metrics The performance of the proposed rebar detection and localization system is at par with the state-of-the-art A number of challenges affecting performance of the rebar detection and localization have also been discussed
Rebar detection and localization for bridge deck inspection and evaluation using deep residual networks
https://orcid.org/0000-0001-8556-7310
Abstract Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) of civil infrastructure has been an active area of research for the past few decades. Due to rising costs, safety issues and error of human inspection methods, automated methods for bridge inspection and maintenance are being proposed. The purpose of this research is to develop an automated rebar detection and localization system utilizing supervised (Deep Residual Networks) and unsupervised (K- means clustering) techniques. Data has been collected from nine bridges using Ground Penetrating Radar (GPR) sensors. The performance of the proposed rebar detection and localization system has been evaluated on a wide-range of performance metrics, which emphasize the superior performance of the proposed technique over existing methods. The results reveal positive correlation between number of layers of networks, training time and other performance metrics. The overall performance of the proposed system is also dataset-dependent with factors such as noise artefacts, reflections and visual quality of rebar profiles.
Highlights A novel rebar detection and localization system has been proposed for non-destructive evaluation of bridge decks GPR sensors were used to extract data from nine bridges Data from four out of total of nine bridges have not been used in any previous study The performance of rebar detection and localization has been examined using a wide-array of performance evaluation metrics The performance of the proposed rebar detection and localization system is at par with the state-of-the-art A number of challenges affecting performance of the rebar detection and localization have also been discussed
Rebar detection and localization for bridge deck inspection and evaluation using deep residual networks
https://orcid.org/0000-0001-8556-7310
Abstract Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) of civil infrastructure has been an active area of research for the past few decades. Due to rising costs, safety issues and error of human inspection methods, automated methods for bridge inspection and maintenance are being proposed. The purpose of this research is to develop an automated rebar detection and localization system utilizing supervised (Deep Residual Networks) and unsupervised (K- means clustering) techniques. Data has been collected from nine bridges using Ground Penetrating Radar (GPR) sensors. The performance of the proposed rebar detection and localization system has been evaluated on a wide-range of performance metrics, which emphasize the superior performance of the proposed technique over existing methods. The results reveal positive correlation between number of layers of networks, training time and other performance metrics. The overall performance of the proposed system is also dataset-dependent with factors such as noise artefacts, reflections and visual quality of rebar profiles.
Highlights A novel rebar detection and localization system has been proposed for non-destructive evaluation of bridge decks GPR sensors were used to extract data from nine bridges Data from four out of total of nine bridges have not been used in any previous study The performance of rebar detection and localization has been examined using a wide-array of performance evaluation metrics The performance of the proposed rebar detection and localization system is at par with the state-of-the-art A number of challenges affecting performance of the rebar detection and localization have also been discussed
Rebar detection and localization for bridge deck inspection and evaluation using deep residual networks
Ahmed, Habib (author) / La, Hung Manh (author) / Tran, Khiem (author)
2020-08-29
Article (Journal)
Electronic Resource
English
Bridge Deck Rebar Corrosion Decision System Using Inductive Learning
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Extending Bridge Deck Life with Epoxy-coated Rebar
| British Library Online Contents | 2005