A Machine Learning Based Approach for Automatic Rebar Detection and Quantification of Deterioration in Concrete Bridge Deck Ground Penetrating Radar B-scan Images

Asadi, Pouria; Gindy, Mayrai; Álvarez, Marco

doi:10.1007/s12205-019-2012-z

Cited by 44 publications

(17 citation statements)

References 13 publications

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“…There is a trend in using artificial intelligence (AI) in automatic processing and detection instead of the common detailed trace-to-trace processing and subjective data interpretation. During the last decade, artificial neural networks associated with machine learning techniques have been successfully applied to improve the GPR processing [342], namely, the inversion approach to locate subsurface targets [343,344], reconstructing high-quality relative permittivity maps of tunnel lining [345], rebar detection [346], and railway ballast diagnosis through unsupervised processing [347,348].…”

Section: Final Remarks and Future Perspectivesmentioning

confidence: 99%

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

2021

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The non-destructive testing and diagnosis of transport infrastructures is essential because of the need to protect these facilities for mobility, and for economic and social development. The effective and timely assessment of structural health conditions becomes crucial in order to assure the safety of the transportation system and time saver protocols, as well as to reduce excessive repair and maintenance costs. Ground penetrating radar (GPR) is one of the most recommended non-destructive methods for routine subsurface inspections. This paper focuses on the on-site use of GPR applied to transport infrastructures, namely pavements, railways, retaining walls, bridges and tunnels. The methodologies, advantages and disadvantages, along with up-to-date research results on GPR in infrastructure inspection are presented herein. Hence, through the review of the published literature, the potential of using GPR is demonstrated, while the main limitations of the method are discussed and some practical recommendations are made.

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Section: Final Remarks and Future Perspectivesmentioning

confidence: 99%

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

2021

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“…Conventionally, counting the number of various objects is based on hand-crafted counting or manual calculation by humans. However, this practice accompanies several problems: it is slow, human-resource-intensive, time-consuming, error-prone, and not very accurate [13][14][15][16][17]. Recently, machine learning (ML), a type of artificial intelligence, has been emerging as an alternative method to deal with these challenges [18][19][20].…”

Section: Related Workmentioning

confidence: 99%

An Image-Based Steel Rebar Size Estimation and Counting Method Using a Convolutional Neural Network Combined with Homography

Shin¹,

Heo²,

Han³

et al. 2021

Buildings

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Conventionally, the number of steel rebars at construction sites is manually counted by workers. However, this practice gives rise to several problems: it is slow, human-resource-intensive, time-consuming, error-prone, and not very accurate. Consequently, a new method of quickly and accurately counting steel rebars with a minimal number of workers needs to be developed to enhance work efficiency and reduce labor costs at construction sites. In this study, the authors developed an automated system to estimate the size and count the number of steel rebars in bale packing using computer vision techniques based on a convolutional neural network (CNN). A dataset containing 622 images of rebars with a total of 186,522 rebar cross sections and 409 poly tags was established for segmentation rebars and poly tags in images. The images were collected in a full HD resolution of 1920×1080 pixels and then center-cropped to 512 × 512 pixels. Moreover, data augmentation was carried out to create 4668 images for the training dataset. Based on the training dataset, YOLACT-based steel bar size estimation and a counting model with a Box and Mask of over 30 mAP was generated to satisfy the aim of this study. The proposed method, which is a CNN model combined with homography, can estimate the size and count the number of steel rebars in an image quickly and accurately, and the developed method can be applied to real construction sites to efficiently manage the stock of steel rebars.

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“…Concrete detector for rebar localization, depth measurement, and size estimation are necessary for the proper estimation of steel reinforcements in the existing old concrete bridge. New technologies, methods, and systems are still developed for the detection of steel rebars in concrete structures [ 61 , 62 , 63 , 64 ].…”

Section: Structural Analysismentioning

confidence: 99%

A 95-Year-Old Concrete Arch Bridge: From Materials Characterization to Structural Analysis

Ambroziak

Malinowski

2021

Materials

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The structural analysis of a 95-year-old concrete arch bridge located in Jagodnik (Poland) is performed in this paper, in order to check its behavior under today’s traffic loads. The mechanical properties of both the concrete and the reinforcement are investigated by testing cores and bar stubs extracted from the bridge. Structural analysis confirms that the bridge meets today’s load requirements in terms of bearing capacity, serviceability state, and that the adopted structural improvements (a new deck slab on top of the existing structure and a layer of mortar to protect the surface of the old concrete) are effective. In this way, the 95-year-old arch bridge was given a new life. The structural improvements show how combining numerical modelling and laboratory tests can contribute to the preservation of an old—though fairly simple—and valuable structure, otherwise destined to demolition, with both environmental and economic benefits.

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A Machine Learning Based Approach for Automatic Rebar Detection and Quantification of Deterioration in Concrete Bridge Deck Ground Penetrating Radar B-scan Images

Cited by 44 publications

References 13 publications

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

An Image-Based Steel Rebar Size Estimation and Counting Method Using a Convolutional Neural Network Combined with Homography

A 95-Year-Old Concrete Arch Bridge: From Materials Characterization to Structural Analysis

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