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


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


Ahmad, S., & Umar, A. (2018). Rheological and mechanical properties of self-compacting concrete with glass and polyvinyl alcohol fibres. Journal of Building Engineering, 17, 65–74. doi:10.1016/j.jobe.2018.02.002.

Ahmad, S., Umar, A., Masood, A., & Nayeem, M. (2019). Performance of self-compacting concrete at room and after elevated temperature incorporating Silica fume. Advances in Concrete Construction, 7(1), 31–37. doi:10.12989/acc.2019.7.1.031.

Loukili, A. (2013). Self-compacting concrete. John Wiley & Sons, Hoboken, United States.

Sayahi, F., Emborg, M., & Hedlund, H. (2017). Effect of water-cement ratio on plastic shrinkage cracking in self-compacting concrete. 23th Symposium on Nordic Concrete Research & Development, 21-23 August, 2017, Aalborg, Denmark.

Alaj, A., Krelani, V., & Numao, T. (2023). Effect of Class F Fly Ash on Strength Properties of Concrete. Civil Engineering Journal (Iran), 9(9), 2249–2258. doi:10.28991/CEJ-2023-09-09-011.

Almohammad-albakkar, M., Behfarnia, K., & Mousavi, H. (2022). Estimation of drying shrinkage in self-compacting concrete containing micro- and nano-silica using appropriate models. Innovative Infrastructure Solutions, 7(5), 324. doi:10.1007/s41062-022-00914-9.

Khoa, H. N., & Cường, N. H. (2011). Specification of effective methods to well-maintain concrete in hot-and-humid climate. Journal of Construction Science and Technology (KHCNXD) - University of Social Sciences and Humanities, 5(1), 33-39. (In Vietnamese).

Khoa, H. N., Vu, N. T. (2015). Curing monolithic concrete by membrane in the climate condition of Quang Nam - Da Nang region. Journal of Structural Engineering and Construction Technology, 17, 30–42.

Uno, P. J. (1998). Plastic shrinkage cracking and evaporation formulas. ACI Materials Journal, 95(4), 365–375. doi:10.14359/379.

Nguyen, D.-B., Wu, C.-J., & Liao, W.-C. (2023). Shrinkage Behavior and Prediction Model of Self-Compacting Concrete. Journal of Materials in Civil Engineering, 35(12), 4023454. doi:10.1061/jmcee7.mteng-15808.

Li, Y., & Li, J. (2014). Capillary tension theory for prediction of early autogenous shrinkage of self-consolidating concrete. Construction and Building Materials, 53, 511–516. doi:10.1016/j.conbuildmat.2013.12.010.

Turcry, P., & Loukili., A. (2006). Evaluation of Plastic Shrinkage Cracking of Self-Consolidating Concrete. ACI Materials Journal, 103(4), 272-280. doi:10.14359/16611.

Onyelowe, K. C., Gnananandarao, T., Ebid, A. M., Mahdi, H. A., Razzaghian Ghadikolaee, M., & Al-Ajamee, M. (2022). Evaluating the Compressive Strength of Recycled Aggregate Concrete Using Novel Artificial Neural Network. Civil Engineering Journal (Iran), 8(8), 1679–1693. doi:10.28991/CEJ-2022-08-08-011.

Vakhshouri, B., & Nejadi, S. (2018). Prediction of compressive strength of self-compacting concrete by ANFIS models. Neurocomputing, 280, 13-22. doi:10.1016/j.neucom.2017.09.099.

Faraj, R. H., Mohammed, A. A., Mohammed, A., Omer, K. M., & Ahmed, H. U. (2022). Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages. Engineering with Computers, 38(3), 2365–2388. doi:10.1007/s00366-021-01385-9.

Chang, W., & Zheng, W. (2022). Compressive strength evaluation of concrete confined with spiral stirrups by using adaptive neuro-fuzzy inference system (ANFIS). Soft Computing, 26(21), 11873–11889. doi:10.1007/s00500-022-07001-2.

Wang, K., Shah, S. P., & Phuaksuk, P. (2002). Plastic Shrinkage Cracking in Concrete Materials—Influence of Fly Ash and Fibers. ACI Materials Journal, 98(6). doi:10.14359/10846.

Erten, E., Yalçınkaya, Ç., Beglarigale, A., Yiğiter, H., & Yazıcı, H. (2017). Effect of early age shrinkage cracks on corrosion of embedded reinforcement in ultra-high performance concrete with/without fibres. Journal of Gazi University Faculty of Engineering and Architecture, 32(4), 1347–1364. doi:10.17341/gazimmfd.369857. (In Turkish).

Boshoff, W. P., & Combrinck, R. (2013). Modelling the severity of plastic shrinkage cracking in concrete. Cement and Concrete Research, 48, 34–39. doi:10.1016/j.cemconres.2013.02.003.

Ghoddousi, P., Abbasi, A. M., Shahrokhinasab, E., & Abedin, M. (2019). Prediction of Plastic Shrinkage Cracking of Self-Compacting Concrete. Advances in Civil Engineering, 2019, 1–7. doi:10.1155/2019/1296248.

Qi, C., Weiss, J., & Olek, J. (2003). Characterization of plastic shrinkage cracking in fiber reinforced concrete using image analysis and a modified Weibull function. Materials and Structures, 36(6), 386–395. doi:10.1007/bf02481064.

Nguyen, C. H., & Tran, L. H. (2023). Applications of Neural Network and Neuro-Fuzzy Network to Estimate the Parameters of Self-Compacting Concrete. International Journal of GEOMATE, 24(106), 120–129. doi:10.21660/2023.106.3656.

Zhang, X., Dai, C., Li, W., & Chen, Y. (2023). Prediction of compressive strength of recycled aggregate concrete using machine learning and Bayesian optimization methods. Frontiers in Earth Science, 11. doi:10.3389/feart.2023.1112105.

Thirumalai Raja, K., Jayanthi, N., Leta Tesfaye, J., Nagaprasad, N., Krishnaraj, R., & Kaushik, V. S. (2022). Using an Artificial Neural Network to Validate and Predict the Physical Properties of Self-Compacting Concrete. Advances in Materials Science and Engineering, 2022, 1–10. doi:10.1155/2022/1206512.

Haykin, S. (2009). Neural Networks and Learning Machines (3rd Ed.). Pearson Education India, Bengaluru, India.

Ojeda, J. M. P., Cayatopa-Calderón, B. A., Huatangari, L. Q., Tineo, J. L. P., Pino, M. E. M., & Pintado, W. R. (2023). Convolutional Neural Network for Predicting Failure Type in Concrete Cylinders During Compression Testing. Civil Engineering Journal (Iran), 9(9), 2105–2119. doi:10.28991/CEJ-2023-09-09-01.

Linh, T. H. (2002). The modification of TSK network in neuro-fuzzy systems. XXV SPETO, Ustron, 525-528.

el Asri, Y., Benaicha, M., Zaher, M., & Hafidi Alaoui, A. (2022). Prediction of the compressive strength of self-compacting concrete using artificial neural networks based on rheological parameters. Structural Concrete, 23(6), 3864–3876. doi:10.1002/suco.202100796.

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


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