Estimation of the thermal conductivity of cement composites using bayesian statistical approach

Woo, Byeong Hun; Yoo, Dong-Ho; Jeon, In-Kyu; Kim, Jee-Sang; Yoo, Kyung-Suk; Kim, Hong Gi

doi:10.1016/j.compositesb.2022.110073

Cited by 12 publications

(5 citation statements)

References 53 publications

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“…In addition, in As expected, the R 2 values in all cases were above 0.95. Above an R 2 value of 0.8, it is considered that the predicted data fits well with the raw data and the prediction performance is quite high [17]. Furthermore, the lowest R 2 value was 0.9708 in DI ao .…”

Section: Training and Test Resultsmentioning

confidence: 87%

“…In general, the normal distribution is frequently used because it is easy to apply to simulations involving the use of trends and data [17]. However, there are many probability density functions that fit well with particular measured/prepared data, such as the Cauchy distribution and the gamma distribution.…”

Section: Bt Of the Dismentioning

confidence: 99%

“…Some studies have also focused on the function of materials used in aggregates. Studies examining thermal properties have addressed issues such as enhancing thermal properties [15][16][17], improving insulation properties [18,19], improving electrical resistivity [20], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…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]. The principles of Bayesian statistics underpin machine learning and neural network processes.…”

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: Training and Test Resultsmentioning

confidence: 87%

Section: Bt Of the Dismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Jang,

Kim,

Woo

2024

Materials

Self Cite

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“…Bayesian techniques have been widely used in pavement studies for various applications such as estimating the correlation of pavement friction with the risk of traffic crashes at intersections [ 19 ], recovering missing traffic data from toll data and video surveillance data [ 20 ], predicting thermal conductivity in cement composites in pavements [ 21 ] and many others. The uncertainties associated with factors that have a detrimental impact on the long-term performance of road networks and the existence of significant heterogeneity among various locations and climate conditions have motivated researchers to deeply investigate the strength of BBN methods for better pavement management solutions [ 22 ].…”

Section: Bayesian Methods In Pavement Management Strategiesmentioning

confidence: 99%