Predicting the Durability of Solid Fired Bricks Using NDT Electroacoustic Methods

Bartoň, Vojtěch; Dvořák, Richard; Cikrle, Petr; Šnédar, J

doi:10.3390/ma15175882

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Of great importance for this study was the already available information on the research and application of various methods for detecting, measuring, and monitoring defects in brick and brick masonry structures [32][33][34][35][36][37]. A sensor technology based on smart bricks-piezoresistive brick sensors for a structural health monitoring system-has been used to monitor deformations and early detection of cracks in shear brickwork [32].…”

Section: Introductionmentioning

confidence: 99%

“…The most successful model of the resonant pulse method had a success rate of up to 85% [33]. Using a break of ordinary quartz optical fibers (6-12 pieces) attached to the surface of the masonry with epoxy adhesive to detect and monitor structural cracks in brickwork, with a detection probability tending to one, allowed us to determine the residual life of structural elements of a building exposed to natural and man-made anomalous phenomena [34].…”

Section: Introductionmentioning

confidence: 99%

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Discovery and Classification of Defects on Facing Brick Specimens Using a Convolutional Neural Network

et al. 2023

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In recent years, visual automatic non-destructive testing using machine vision algorithms has been widely used in industry. This approach for detecting, classifying, and segmenting defects in building materials and structures can be effectively implemented using convolutional neural networks. Using intelligent systems in the initial stages of manufacturing can eliminate defective building materials, prevent the spread of defective products, and detect the cause of specific damage. In this article, the solution to the problem of building elements flaw detection using the computer vision method was considered. Using the YOLOv5s convolutional neural network for the detection and classification of various defects of the structure, the appearance of finished products of facing bricks that take place at the production stage is shown during technological processing, packaging, transportation, or storage. The algorithm allows for the detection of foreign inclusions, broken corners, cracks, and color unevenness, including the presence of rust spots. To train the detector, our own empirical database of images of facing brick samples was obtained. The set of training data for the neural network algorithm for discovering defects and classifying images was expanded by using our own augmentation algorithm. The results show that the developed YOLOv5s model has a high accuracy in solving the problems of defect detection: mAP0.50 = 87% and mAP0.50:0.95 = 72%. It should be noted that the use of synthetic data obtained by augmentation makes it possible to achieve a good generalizing ability from the algorithm, it has the potential to expand visual variability and practical applicability in various shooting conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discovery and Classification of Defects on Facing Brick Specimens Using a Convolutional Neural Network

et al. 2023

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“…However, in the past five years a strong trend toward the usage of machine learning (ML) algorithms has arisen [19][20][21]. In 2022, Hatem et al [21] presented a study of comparison of ultrasound pulse velocity measurement, Schmidt rebound hammer, and compressive strength.…”

Section: Introductionmentioning

confidence: 99%

Classification of Thermally Degraded Concrete by Acoustic Resonance Method and Image Analysis via Machine Learning

et al. 2023

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The study of the resistance of plain concrete to high temperatures is a current topic across the field of civil engineering diagnostics. It is a type of damage that affects all components in a complex way, and there are many ways to describe and diagnose this degradation process and the resulting condition of the concrete. With regard to resistance to high temperatures, phenomena such as explosive spalling or partial creep of the material may occur. The resulting condition of thermally degraded concrete can be assessed by a number of destructive and nondestructive methods based on either physical or chemical principles. The aim of this paper is to present a comparison of nondestructive testing of selected concrete mixtures and the subsequent classification of the condition after thermal degradation. In this sense, a classification model based on supervised machine learning principles is proposed, in which the thermal degradation of the selected test specimens are known classes. The whole test set was divided into five mixtures, each with seven temperature classes in 200 °C steps from 200 °C up to 1200 °C. The output of the paper is a comparison of the different settings of the classification model and validation algorithm in relation to the observed parameters and the resulting model accuracy. The classification is done by using parameters obtained by the acoustic NDT Impact-Echo method and image-processing tools.

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“…For reconstruction purposes, bricks with well-identified properties should be used. Before application, defects in material not visible on the surface can be detected using nondestructive methods [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Hygric Properties and Compressive Strength of Ceramic Bricks

2022

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This article analyzes the results of capillary rise, compressive strength and water absorption tests on solid ceramic bricks from existing structures and demolition materials taken from 11 different structures. In addition (for more extensive interpretation and evaluation of porosity), tests were performed for the selected series of bricks using a mercury porosimeter (MIP) and a micro computed tomography (micro-CT). Contemporary bricks (2 series) were also evaluated for comparison purposes. The conducted tests indicate that bricks obtained from different sources are characterized by “individual” relation of compressive strength and porosity, and “individual” relation of water absorption coefficient and porosity. In addition, on the basis of the results obtained in the study, compared with the literature data, it can be deduced that ceramic bricks with a water absorption coefficient of less than 50 g/m2s0.5 are characterized by a compressive strength of more than 80 MPa. As the research shows, the properties of bricks even from a single building can differ one from another, which can result in varying durability even within a single building. When choosing a material during the renovation or restoration of facilities, it is important to perform tests on the physical and mechanical properties of the original material, which will be reused.

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Predicting the Durability of Solid Fired Bricks Using NDT Electroacoustic Methods

Cited by 7 publications

References 17 publications

Discovery and Classification of Defects on Facing Brick Specimens Using a Convolutional Neural Network

Discovery and Classification of Defects on Facing Brick Specimens Using a Convolutional Neural Network

Classification of Thermally Degraded Concrete by Acoustic Resonance Method and Image Analysis via Machine Learning

A Comparative Study on Hygric Properties and Compressive Strength of Ceramic Bricks

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