Rebar Detection and Localization for Non-destructive Infrastructure Evaluation of Bridges Using Deep Residual Networks

Ahmed, Habib; La, Hung Manh; Pekcan, Gökhan

doi:10.1007/978-3-030-33720-9_49

Cited by 3 publications

(8 citation statements)

References 20 publications

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“…Another widely utilized sensor is the ground-penetrating radar (GPR), which has been the focus of a number of recent studies related to the infrastructure evaluation and SHM of civil infrastructures and bridges [8,37,47,[135][136][137][138][139][140][141][142][143][144][145][146]. Within civil engineering, GPR has been utilized for diverse applications, which include, but are not limited to, detecting and measuring pipes, mines, other underground utilities, the health monitoring of bridges, railway tracks, tunnels, roads and pavements, as well as for the detection and localization of underground rebar [8,37,47,[136][137][138][139][140][141][142][143][144][145][146][147][148]. The B-scan data from GPR sensors provide a visual transformation of the radar waves reflected from different parts of the underground infrastructure (e.g., concrete, steel rebars, void spaces), which can be used to highlight the corrosion, delamination, presence of void spaces and structural damage to rebars [37,135,137,138].…”

Section: Single Sensor Systemsmentioning

confidence: 99%

“…Within civil engineering, GPR has been utilized for diverse applications, which include, but are not limited to, detecting and measuring pipes, mines, other underground utilities, the health monitoring of bridges, railway tracks, tunnels, roads and pavements, as well as for the detection and localization of underground rebar [8,37,47,[136][137][138][139][140][141][142][143][144][145][146][147][148]. The B-scan data from GPR sensors provide a visual transformation of the radar waves reflected from different parts of the underground infrastructure (e.g., concrete, steel rebars, void spaces), which can be used to highlight the corrosion, delamination, presence of void spaces and structural damage to rebars [37,135,137,138]. The details regarding different data analysis techniques will be discussed in the following sub-section.…”

Section: Single Sensor Systemsmentioning

confidence: 99%

“…Different hand-crafted features were employed by many of the earlier studies, such as the edge-based features [146][147][148][225][226][227], texture-based features [222,228], template matching [221,229], histogram of oriented gradients (HOG) [47,135,140], feature transformation methods, e.g., Radon transform [142], and Hough transform [230][231][232][233] and statistics-based methods, such as clustering-based approaches [223,234], least-square methods [235], and higher-order moments [235,236]. These features were trained using a wide range of different learning-based techniques towards developing effective rebar detection and localization algorithms in the past [47,[135][136][137][138]227,237]. Research by Gibb and La [135] trained a Naïve Bayesian classifier using HOG features.…”

Section: Sub-surface-level Analysis: Rebar Detection and Localizationmentioning

confidence: 99%

“…Many of the earlier methods failed to effectively leverage the capabilities of NN models using edge-based features [147,148,227], which are not suitable for real-world systems dealing with GPR data that contain varying rebar signatures and fluctuating noise levels. Some of the recent studies have made use of convolutional neural networks for rebar detection [136][137][138]238]. A study by Dinh et al [136] proposed the usage of a 24-layer deep CNN model for rebar classification.…”

Section: Sub-surface-level Analysis: Rebar Detection and Localizationmentioning

confidence: 99%

“…A study by Dinh et al [136] proposed the usage of a 24-layer deep CNN model for rebar classification. The use of residual neural networks (ResNet-50) has also been proposed in recent studies related to rebar detection and localization [137,138]. The preliminary examination of the results using GPR data from real-world bridges has shown that different ResNet models (i.e., ResNet-18, ResNet-32, ResNet-50, ResNet-101, ResNet-152) provide increased accuracy and generalizability [137,138,239].…”

Section: Sub-surface-level Analysis: Rebar Detection and Localizationmentioning

confidence: 99%

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Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

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Section: Single Sensor Systemsmentioning

confidence: 99%

Section: Single Sensor Systemsmentioning

confidence: 99%

Section: Sub-surface-level Analysis: Rebar Detection and Localizationmentioning

confidence: 99%

Section: Sub-surface-level Analysis: Rebar Detection and Localizationmentioning

confidence: 99%

Section: Sub-surface-level Analysis: Rebar Detection and Localizationmentioning

confidence: 99%

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Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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Deep learning for the security of software-defined networks: a review

Taheri,

Ahmed,

Arslan

2023

Cluster Comput

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Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Azimi

Eslamlou

Pekcan

2020

Sensors

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381

158

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Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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Rebar Detection and Localization for Non-destructive Infrastructure Evaluation of Bridges Using Deep Residual Networks

Cited by 3 publications

References 20 publications

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Deep learning for the security of software-defined networks: a review

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

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