Available online at www.CivileJournal.org Civil Engineering Journal (E-ISSN: 2476-3055; ISSN: 2676-6957) Vol. 10, No. 01, January, 2024 117 The Application of Neural Networks to Predict the Water Evaporation Percentage and the Plastic Shrinkage Size of Self-Compacting Concrete Structure Cuong H. Nguyen 1 , Linh H. Tran 2* 1 Hanoi University of Civil Engineering, 55 Giai Phong str., Hanoi, Vietnam. 2 Hanoi University of Science and Technology, 1 Dai Co Viet str., Hanoi, Vietnam. Received 11 August 2023; Revised 03 December 2023; Accepted 19 December 2023; Published 01 January 2024 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. Keywords: Concrete Dehydration; Plastic Shrinkage; Neuro-Fuzzy Networks; Water Evaporation; Concrete Curing. 1. Introduction Self-Compacting Concrete (SCC) is a type of concrete with the ability to self-flow and self-compact, filling formwork under its own weight while still ensuring uniformity, even in cases of dense reinforcement [1, 2]. The composition of the concrete mix has some differences compared to ordinary concrete, such as a higher level of fine filler content, a higher superplasticizer admixture, a larger cement paste volume, and a lower W/B (water-to-binder) ratio. Therefore, the behavior of concrete in the early curing stage, specifically water evaporation and plastic shrinkage, will be different from that of conventional concrete, leading to a different curing process. According to Loukili [3], because SCC has a larger fine binder ratio, a low W/B ratio, and a larger dosage of superplasticizers, surface water drainage is usually lower compared with conventional concrete. Simultaneously, cracks due to plastic shrinkage in SCC are more serious than in conventional concrete [4, 5]. The evaporation rate is an essential parameter that directly affects the * Corresponding author: linh.tranhoai@hust.edu.vn http://dx.doi.org/10.28991/CEJ-2024-010-01-07 © 2024 by the authors. Licensee C.E.J, Tehran, Iran. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).