A novel use of frequency-banded synthetic aperture focusing technique for reconstructions of alkali-silica reaction in thick-reinforced concrete structures

Ezell, N. Dianne Bull; Albright, Austin; Floyd, Dan; Clayton, Dwight A; Khazanovich, Lev

doi:10.1063/1.5099840

AIP Conference Proceedings

2019

DOI: 10.1063/1.5099840

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A novel use of frequency-banded synthetic aperture focusing technique for reconstructions of alkali-silica reaction in thick-reinforced concrete structures

N. Dianne Bull Ezell

Austin Albright

Dan Floyd

et al.

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2024

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Transfer learning for probabilistic localization of hidden cracks in concrete structures

Miele,

Karve,

Mahadevan

2024

J Civil Struct Health Monit

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The utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.

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Transfer learning for probabilistic localization of hidden cracks in concrete structures

Miele,

Karve,

Mahadevan

2024

J Civil Struct Health Monit

View full text Add to dashboard Cite

show abstract

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A novel use of frequency-banded synthetic aperture focusing technique for reconstructions of alkali-silica reaction in thick-reinforced concrete structures

Cited by 1 publication

References 4 publications

Transfer learning for probabilistic localization of hidden cracks in concrete structures

Transfer learning for probabilistic localization of hidden cracks in concrete structures

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