• Title/Summary/Keyword: concrete damage detection

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Detection of flaw in steel anchor-concrete composite using high-frequency wave characteristics

  • Rao, Rajanikant;Sasmal, Saptarshi
    • Steel and Composite Structures
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    • v.31 no.4
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    • pp.341-359
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    • 2019
  • Non-monolithic concrete structural connections are commonly used both in new constructions and retrofitted structures where anchors are used for connections. Often, flaws are present in anchor system due to poor workmanship and deterioration; and methods available to check the quality of the composite system afterward are very limited. In case of presence of flaw, load transfer mechanism inside the anchor system is severely disturbed, and the load carrying capacity drops drastically. This raises the question of safety of the entire structural system. The present study proposes a wave propagation technique to assess the integrity of the anchor system. A chemical anchor (embedded in concrete) composite system comprising of three materials viz., steel (anchor), polymer (adhesive) and concrete (base) is considered for carrying out the wave propagation studies. Piezoelectric transducers (PZTs) affixed to the anchor head is used for actuation and the PZTs affixed to the surrounding concrete surface of the concrete-anchor system are used for sensing the propagated wave through the anchor interface to concrete. Experimentally validated finite element model is used to investigate three types of composite chemical anchor systems. Studies on the influence of geometry, material properties of the medium and their distribution, and the flaw types on the wave signals are carried out. Temporal energy of through time domain differentiation is found as a promising technique for identifying the flaws in the multi-layered composite system. The present study shows a unique procedure for monitoring of inaccessible but crucial locations of structures by using wave signals without baseline information.

Interface monitoring of steel-concrete-steel sandwich structures using piezoelectric transducers

  • Yan, Jiachuan;Zhou, Wensong;Zhang, Xin;Lin, Youzhu
    • Nuclear Engineering and Technology
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    • v.51 no.4
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    • pp.1132-1141
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    • 2019
  • Steel-concrete-steel (SCS) sandwich structures have important advantages over conventional concrete structures, however, bond-slip between the steel plate and concrete may lead to a loss of composite action, resulting in a reduction of stiffness and fatigue life of SCS sandwich structures. Due to the inaccessibility and invisibility of the interface, the interfacial performance monitoring and debonding detection using traditional measurement methods, such as relative displacement between the steel plate and core concrete, have proved challenging. In this work, two methods using piezoelectric transducers are proposed to detect the bond-slip between steel plate and core concrete during the test of the beam. The first one is acoustic emission (AE) method, which can detect the dynamic process of bond-slip. AE signals can be detected when initial micro cracks form and indicate the damage severity, types and locations. The second is electromechanical impedance (EMI) method, which can be used to evaluate the damage due to bond-slip through comparing with the reference data in static state, even if the bond-slip is invisible and suspends. In this work, the experiment is implemented to demonstrate the bond-slip monitoring using above methods. Experimental results and further analysis show the validity and unique advantage of the proposed methods.

Crack Detection of Concrete Using Fiber Optic Cables (Fiber Optic Cable을 이용한 콘크리트 균열탐사)

  • Cho, Nam-So;Kim, Nam-Sik
    • Journal of the Korean Society for Nondestructive Testing
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    • v.27 no.2
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    • pp.157-163
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    • 2007
  • Crack detection technique for concrete structures has been developed in this study. Experimental tests were carried out to detect a surface and internal crack, employing common fiber optic cables and OTDR(optical time domain reflectometry), an optical signal analyzer which is widely used to detect damages at fiber optic cables in the field of optical engineering. While initial concrete crack is ready to occur under cracking force, the occurrence and location of the crack are simultaneously detected to give the same damage to fiber optic cables which have been attached to and/or embedded into concrete in advance. It is obtained through successive tests that the principal factors for crack detection is the covering state of fiber optic cable, and total 4 tests including a preliminary test were conducted and the crack detection technique was verified. The practical usefulness would be expected at crack management and maintenance of concrete structures.

A hierarchical semantic segmentation framework for computer vision-based bridge damage detection

  • Jingxiao Liu;Yujie Wei ;Bingqing Chen;Hae Young Noh
    • Smart Structures and Systems
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    • v.31 no.4
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    • pp.325-334
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    • 2023
  • Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

A computer vision-based approach for crack detection in ultra high performance concrete beams

  • Roya Solhmirzaei;Hadi Salehi;Venkatesh Kodur
    • Computers and Concrete
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    • v.33 no.4
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    • pp.341-348
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    • 2024
  • Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

Damage detection on output-only monitoring of dynamic curvature in composite decks

  • Domaneschi, M.;Sigurdardottir, D.;Glisic, B.
    • Structural Monitoring and Maintenance
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    • v.4 no.1
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    • pp.1-15
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    • 2017
  • Installation of sensors networks for continuous in-service monitoring of structures and their efficiency conditions is a current research trend of paramount interest. On-line monitoring systems could be strategically useful for road infrastructures, which are expected to perform efficiently and be self-diagnostic, also in emergency scenarios. This work researches damage detection in composite concrete-steel structures that are typical for highway overpasses and bridges. The techniques herein proposed assume that typical damage in the deck occurs in form of delamination and cracking, and that it affects the peak power spectral density of dynamic curvature. The investigation is performed by combining results of measurements collected by long-gauge fiber optic strain sensors installed on monitored structure and a statistic approach. A finite element model has been also prepared and validated for deepening peculiar aspects of the investigation and the availability of the method. The proposed method for real time applications is able to detect a documented unusual behavior (e.g., damage or deterioration) through long-gauge fiber optic strain sensors measurements and a probabilistic study of the dynamic curvature power spectral density.

A method for concrete crack detection using U-Net based image inpainting technique

  • Kim, Su-Min;Sohn, Jung-Mo;Kim, Do-Soo
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.10
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    • pp.35-42
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    • 2020
  • In this study, we propose a crack detection method using limited data with a U-Net based image inpainting technique that is a modified unsupervised anomaly detection method. Concrete cracking occurs due to a variety of causes and is a factor that can cause serious damage to the structure in the long term. In general, crack investigation uses an inspector's visual inspection on the concrete surfaces, which is less objective in judgment and has a high possibility of human error. Therefore, a method with objective and accurate image analysis processing is required. In recent years, the methods using deep learning have been studied to detect cracks quickly and accurately. However, when the amount of crack data on the building or infrastructure to be inspected is small, existing crack detection models using it often show a limited performance. Therefore, in this study, an unsupervised anomaly detection method was used to augment the data on the object to be inspected, and as a result of learning using the data, we confirmed the performance of 98.78% of accuracy and 82.67% of harmonic average (F1_Score).

Edge Detection and ROI-Based Concrete Crack Detection (Edge 분석과 ROI 기법을 활용한 콘크리트 균열 분석 - Edge와 ROI를 적용한 콘크리트 균열 분석 및 검사 -)

  • Park, Heewon;Lee, Dong-Eun
    • Korean Journal of Construction Engineering and Management
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    • v.25 no.2
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    • pp.36-44
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    • 2024
  • This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

A numerical study on vibration-based interface debonding detection of CFST columns using an effective wavelet-based feature extraction technique

  • Majid Gholhaki;Borhan Mirzaei;Mohtasham Khanahmadi;Gholamreza Ghodrati Amiri;Omid Rezaifar
    • Steel and Composite Structures
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    • v.53 no.1
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    • pp.45-59
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    • 2024
  • This paper aims to investigate the impact of interfacial debonding on modal dynamic properties such as frequencies and vibration mode shapes. Furthermore, it seeks to identify the specific locations of debonding in rectangular concrete-filled steel tubular (CFST) columns during the subsequent stage of the study. In this study, debonding is defined as a reduction in the elasticity modulus of concrete by a depth of 3 mm at the connection point with the steel tube. Debonding leads to a lack of correlation between primary and secondary shapes of vibration modes and causes a reduction in the natural frequency in all modes. However, directly comparing changes in vibration responses does not allow for the identification of debonding locations. In this study, a novel irregularity detection index (IDI) is proposed based on modal signal processing via the 2D wavelet transform. The suggested index effectively reveals relative irregularity peaks in the form of elevations at the debonding locations. As the severity of damage increases at a specific debonding location, the relative irregularity peaks would increase only at that specific point; in other words, the detection or non-detection of a debonding location using IDI has minimal effects on the identification of other debonding locations.

Application of Acoustic Emission Technique for Detection of Crack in Mortar and Concrete (모르터와 콘크리트의 균열검출을 위한 음향방출기법의 적용)

  • 진치섭;신동익;장종철
    • Proceedings of the Korea Concrete Institute Conference
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    • 2000.10a
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    • pp.739-744
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    • 2000
  • Concrete structures generally have cracks, so for the safety and durability of structures, studies to detect cracks using nondestructive tests have been treated in great deal. In order to assure the reliability of concrete structure, microscopic fracture behavior and internal damage progress of concrete under the loading should be fully understood. The purpose of this study predicts location of initial crack and measures direction of crack propagation for on-line monitoring before the crack really grows in structures by using two-dimensional Acoustic Emission(AE) source location based on rectangular method with three-point bending test. This will allow efficient maintenance of concrete structure through monitoring of internal cracking based on AE method.

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