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The Application of Neural Networks to Predict the Water Evaporation Percentage and the Plastic Shrinkage Size of Self-Compacting Concrete Structure

Cuong H. Nguyen, Linh H. Tran

Abstract


This article presents a solution using an artificial neural network and a neuro-fuzzy network to predict the rate of water evaporation and the size of the shrinkage of a self-compacting concrete mixture based on the concrete mixture parameters and the environment parameters. The concrete samples were mixed and measured at four different environmental conditions (i.e., humid, dry, hot with high humidity, and hot with low humidity), and two curing styles for the self-compacting concrete were measured. Data were collected for each sample at the time of mixing and pouring and every 60 minutes for the next ten hours to help create prediction models for the required parameters. A total of 528 samples were collected to create the training and testing data sets. The study proposed to use the classic Multi-Layer Perceptron and the modified Takaga-Sugeno-Kang neuro-fuzzy network to estimate the water evaporation rate and the shrinkage size of the concrete sample when using four inputs: the concrete water-to-binder ratio, environment temperature, relative humidity, and the time after pouring the concrete into the mold. Real-field experiments and numerical computations have shown that both of the models are good as parameter predictors, where low errors can be achieved. Both proposed networks achieved for testing results R2 bigger than 0.98, the mean of squared errors for water evaporation percentage was less than 1.43%, and the mean of squared errors for shrinkage sizes was less than 0.105 mm/m. The computation requirements of the two models in testing mode are also low, which can allow their easy use in practical applications.

 

Doi: 10.28991/CEJ-2024-010-01-07

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Keywords


Concrete Dehydration; Plastic Shrinkage; Neuro-Fuzzy Networks; Water Evaporation; Concrete Curing.

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Full Text: PDF

DOI: 10.28991/CEJ-2024-010-01-07

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Copyright (c) 2024 Linh Hoai Tran, Cuong Hung Nguyen

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