Calcite formation on steamed concrete surfaces and its potential for sealing cracks

Zemann, Moritz; Herrmann, Nico; Dehn, Frank

doi:10.1016/j.conbuildmat.2019.01.091

Cited by 16 publications

(3 citation statements)

References 21 publications

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“…Im weiteren Verlauf ging diese Schichtdicke wieder leicht zurück, was auf eine Stabilisierung des Aufwachsens hindeuten könnte, dem abrasive Effekte entgegenwirken. Es könnte auch zu einer Einstellung eines Gleichgewichts der kinetischen Reaktionen kommen .…”

Section: Kleinskalige Laborexperimenteunclassified

Leckageverhalten von gerissenem Beton – eine mehrskalige Betrachtung

Zemann

Herrmann

Dehn

2019

Beton und Stahlbetonbau

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Bei der Betrachtung von Sicherheitsaspekten in Kernkraftwerken über ihre gesamte Betriebs‐ und Stilllegungsdauer spielt der Aspekt der Dichtheit des Containments eine bedeutende Rolle. Selbstheilende Effekte im Beton können hierbei entscheidend dazu beitragen, die Kontamination der Umwelt zu vermeiden. An der Materialprüfungs‐ und Forschungsanstalt (MPA Karlsruhe) des Karlsruher Instituts für Technologie (KIT) wurden in den vergangenen Jahrzehnten Durchströmungsexperimente mit Luft und Dampf an Betonprobekörpern sowohl im Labormaßstab als auch im Realmaßstab durchgeführt. Die Versuchsbedingungen entsprachen realistischen Temperaturen und Drücken, die in anzunehmenden Unfallszenarien auftreten können. Der vorliegende Artikel gibt einen Überblick über Versuchsergebnisse, die im Rahmen mehrerer Forschungsvorhaben gewonnen wurden, und soll darüber hinaus Erklärungsansätze für bisher inkonsistente Ergebnisse der Großversuche liefern, die unter Berücksichtigung der Ergebnisse der kleinskaligen Versuche neu diskutiert werden.

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Section: Kleinskalige Laborexperimenteunclassified

Leckageverhalten von gerissenem Beton – eine mehrskalige Betrachtung

Zemann

Herrmann

Dehn

2019

Beton und Stahlbetonbau

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“…Indeed, these cracks cause many problems for the reinforcement, such as corrosion and chemical attack [2]. Consequently, structural damage identification is shown to be inevitable to reduce the risks [3].…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

Nguyen

2021

Applied Computational Intelligence and Soft Computing

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This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fractures on concrete surfaces. The developed model for the classification of images was based on a DL Convolutional Neural Network (CNN). To train and validate the CNN model, a database containing 40,000 images of concrete surfaces (with and without cracks) was collected from the available literature. Several conditions on the concrete surfaces were taken into account such as illumination and surface finish (i.e., exposed, plastering, and paint). Various error measurement criteria such as accuracy, precision, recall, specificity, and F1-score were employed for accessing the quality of the developed model. Results showed that for the training dataset (50% of the database), the precision, recall, specificity, F1-score, and accuracy were 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively. On the other hand, for the validating dataset, the precision, recall, specificity, F1-score, and accuracy are 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. Thus, the developed CNN model may be considered valid because it performs the classification of cracks well using the testing data. It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. Finally, in further work, such model could be combined with Unmanned Aerial Vehicles (UAVs) to increase the productivity of concrete infrastructure inspection.

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“…However, other phenomena could be of interest in such ageing structures with uncertainties both experimentally and numerically. For instance, there is (a) the effect of steel-concrete bond on the resulting cracking patterns [88] (b) the effect of cracking on the concrete's hydric diffusivity [89](c) the effect of self-healing in concrete on the evolution of early age crack's opening and ultimately on the leakage rate [90] (d) etc. These topics are considered out of the scope in the present work but are of interest within the VeRCoRs benchmark and within the forthcoming research efforts.…”

mentioning

confidence: 99%