Automated detection and segmentation of internal defects in reinforced concrete using deep learning on ultrasonic images

Kuchipudi, Sai Teja; Ghosh, Debdutta

doi:10.1016/j.conbuildmat.2023.134491

Cited by 6 publications

(2 citation statements)

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“…Based on the appearance, size, and orientation of cracks, these models can be trained to recognize and categorize different types of cracks. Additionally, it is possible to develop systems that can analyze image data in order to measure the expansion of concrete structures over time [34]. This type of system can provide quantitative measurements of structural changes by training AI models to identify specific markers of expansion, such as crack patterns and deformations in concrete.…”

Section: Introductionmentioning

confidence: 99%

New Methods for Assessing External Sulfate Attack on Cement-Based Specimens

Omikrine Metalssi,

Quiertant,

Jabbour

et al. 2024

Applied Sciences

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This paper presents two original methods for monitoring and evaluating concrete specimens/structures affected by external sulfate attack (ESA). The first is a drying method developed to assess the penetration depth of sulfate ions in a concrete structure, as this parameter is a relevant indicator of the progress of the ESA. This method has been specifically designed for on-site investigations. The second experimental method involves the use of optical fibers capable of measuring the swelling response of specimens to ESA in real time. According to the results obtained, these two new methods seem likely to be used to complement or replace traditional methods such as inductively coupled plasma (ICP) for determining the penetration depth of sulfate ions or as extensometers for measuring swelling. These traditional methods (ICP and extensometers) are generally considered painful and time-consuming, whereas, because of its simplicity, the proposed drying method will enable experts to regularly inspect concrete structures and make informed decisions on the measures to be taken to repair or prevent further damage induced by ESA, while the second method appears promising for experimental studies involving the monitoring of a large number of ESA-affected specimens.

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Section: Introductionmentioning

confidence: 99%

New Methods for Assessing External Sulfate Attack on Cement-Based Specimens

Omikrine Metalssi,

Quiertant,

Jabbour

et al. 2024

Applied Sciences

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“…Yin et al (2023) proposed a nonlinear ultrasonic technique for in situ monitoring of the cracks and defects of ultra-high-performance fiberreinforced concrete structures under tensile loads [2]. Kuchipudi and Ghosh (2024) suggested an enhanced method for detecting defects in reinforced concrete using an automated two-stage convolutional neural network [3]. Therefore, Alavi et al (2024) investigated reinforced concrete specimens' strength by using the SonReb method combined with a machine learning algorithm [4].…”

Section: Introductionmentioning

confidence: 99%

Contactless X-band Detection of Steel Bars in Concrete: A Preliminary Numerical and Experimental Analysis

Brancaccio,

Palladino

2024

Preprint

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This work presents preliminary experimental results advancing non-destructive testing methods for detecting steel bars in concrete structures via contactless investigation in the X-band spectrum. The study reveals the field’s penetration into concrete, extracting insights into embedded bars through scattered data. Applying a quasi-quadratic inverse scattering technique to numerically simulated data yields promising results, confirming the effectiveness and reliability of the proposed approach. In this realm, using a higher frequency allows the use of lighter equipment and smaller antennas. Identified areas for improvement include accounting for antenna behavior and establishing undeformed target morphology and precise orientation. Transitioning from powder-based and sand specimens to real solid reinforced concrete structures is expected to alleviate laboratory challenges. Although accurately determining concrete properties such as relative permittivity and conductivity is essential, it remains beyond the scope of this study. Finally, overcoming these challenges could significantly enhance non-invasive testing, improving structural health monitoring and disaster prevention.

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