Artificial neural network (ANN), M5P-tree, and regression analyses to predict the early age compression strength of concrete modified with DBC-21 and VK-98 polymers

• Published in: Neural computing & applications Vol. 33; no. 13; pp. 7851 - 7873
• Main Authors: Mohammed, Ahmed, Burhan, Lajan, Ghafor, Kawan, Sarwar, Warzer, Mahmood, Wael
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2021; Springer Nature B.V
• Subjects: Age; Artificial Intelligence; Artificial neural networks; Cement; Compressive strength; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Curing; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Mathematical models; Neural networks; Original Article; Polymers; Probability and Statistics in Computer Science; Process parameters; Regression; Water-cement ratio; Workability; Workability; Concrete; Polymer contents; Statistical assessment; Modeling; Compression strength
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-05525-y

In this study, the effect of two water-reducer polymers with smooth and rough surfaces on the workability, and the compression strength of concrete from an early age (1 day) up to 28 days of curing was investigated. The polymer contents used in this study varied from 0 to 0.25% (wt%). The initial ratio between water and cement ( w c ) was 60%, and it slowly reduced to 0.46 by increasing the polymer contents. The compression strength of concrete was increased significantly with increasing the polymer contents by 24–95% depending on the polymer type, polymer content, w c , and curing age. Because of a fiber net (netting) in the concrete when the polymers were added which leads to a decrease void between the particles, binding the cement particles, therefore, increased rapidly the viscosity for the fresh concrete and the compression strength of the hardened concrete. This study also aims to establish systematic multiscale models to predict the compression strength of concrete containing polymers and to be used by the construction projects with no theoretical restrictions. For that purpose, 88 concrete samples modified with two types of polymer (44 samples for each modification) has been tested, analyzed, and modeled. Linear, nonlinear regression, M5P-tree, and artificial neural network (ANN) approaches were used for the qualifications. In the modeling process, the most relevant parameters affect the strength of concrete, i.e., polymer incorporation ratio (0–0.25% of cement’s mass), water-to-cement ratio (0.46–0.6), and curing ages (1–28 days). Among the used approaches and based on the training data set, the model made based on the nonlinear regression, ANN, and M5P-tree models seem to be the most reliable models. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the maximum stress (compression strength) of concrete with this dataset.