2020
DOI: 10.1016/j.asoc.2020.106552
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Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques

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Cited by 150 publications
(37 citation statements)
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“…It can be observed that training the models with synthetic data was performed successfully, such that low MAE and RMSE values were achieved. Such error values were lower than the errors reported in similar studies [30,51]. This demonstrates the significant potential of TGAN to generate credible data for training powerful and generalized ML models.…”
Section: Machine Learning Modelingmentioning
confidence: 42%
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“…It can be observed that training the models with synthetic data was performed successfully, such that low MAE and RMSE values were achieved. Such error values were lower than the errors reported in similar studies [30,51]. This demonstrates the significant potential of TGAN to generate credible data for training powerful and generalized ML models.…”
Section: Machine Learning Modelingmentioning
confidence: 42%
“…For instance, the physical properties of steel fibers such as diameter and length were not included as input features due to their confirmed insignificant effect on compressive strength [14,15]. For instance, Abuodeh et al [30] used the absolute mass of steel fibers alone in their predictive model. Table 1 presents the variables of the dataset along with their designations.…”
Section: Data Collectionmentioning
confidence: 99%
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