Corrosion state assessment of the rebar via Bayesian inference

Woo, Byeong Hun; Lee, Jeong-Bae; Kim, Jee-Sang; Kim, Hong Gi

doi:10.1016/j.conbuildmat.2023.131791

Cited by 3 publications

(8 citation statements)

References 27 publications

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“…Over the period of one month, a massive amount of data were collected, with readings taken every 10 min. Usually, Bayesian inference performs best when using massive datasets [28,30]. In this study, data were collected from 28 July 2018 to 14 August 2018.…”

Section: Data Collectionmentioning

confidence: 99%

“…Using these indices, a BT could be calculated. Woo et al [28] used only a voltage signal but calculated a BT following Baye's rule of Equation ( 4) [42].…”

Section: Bt Through the Baye's Rulementioning

confidence: 99%

“…Furthermore, in the cases of the Cauchy and logistic distributions, these two distributions exhibit thicknesses in their lines. In short, this may reflect a kind of noise when performing the thresholding work [28]. It is better to choose smoother lines in PP plots.…”

Section: Bt Of the Dismentioning

confidence: 99%

“…The first is statistical simulation. An example of this approach is that of Woo et al [28] who performed a Bayesian statistical simulation to assess the corrosion state of rebars. The Bayesian simulation indicated that the corrosion state could be indexed using statistical values, and a probability-based prediction of corrosion level could be used.…”

Section: Introductionmentioning

confidence: 99%

“…The Bayesian simulation indicated that the corrosion state could be indexed using statistical values, and a probability-based prediction of corrosion level could be used. The Bayesian indexing method produced quite accurate results, and Woo et al [28] suggested that this process can be applied to real-time monitoring systems. It is possible to simulate time series data in a statistical manner and other Bayesian simulations can be found in various studies [17,29,30].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Simulation of the Air Conditioner Operating Time in Concrete Structures with Bayesian Thresholding

Jang,

Kim,

Woo

2024

Materials

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Efficient energy use is crucial for achieving carbon neutrality and reduction. As part of these efforts, research is being carried out to apply a phase change material (PCM) to a concrete structure together with an aggregate. In this study, an energy consumption simulation was performed using data from concrete mock-up structures. To perform the simulation, the threshold investigation was performed through the Bayesian approach. Furthermore, the spiking part of the spiking neural network was modularized and integrated into a recurrent neural network (RNN) to find accurate energy consumption. From the training-test results of the trained neural network, it was possible to predict data with an R2 value of 0.95 or higher through data prediction with high accuracy for the RNN. In addition, the spiked parts were obtained; it was found that PCM-containing concrete could consume 32% less energy than normal concrete. This result suggests that the use of PCM can be a key to reducing the energy consumption of concrete structures. Furthermore, the approach of this study is considered to be easily applicable in energy-related institutions and the like for predicting energy consumption during the summer.

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Section: Data Collectionmentioning

confidence: 99%

“…Using these indices, a BT could be calculated. Woo et al [28] used only a voltage signal but calculated a BT following Baye's rule of Equation ( 4) [42].…”

Section: Bt Through the Baye's Rulementioning

confidence: 99%

Section: Bt Of the Dismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%