Benalcázar-Rojas, V.R.; Yambay-Vallejo, W.J.; Herrera-Granda, E.P. Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks. Appl. Sci.2023, 13, 10956.
Benalcázar-Rojas, V.R.; Yambay-Vallejo, W.J.; Herrera-Granda, E.P. Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks. Appl. Sci. 2023, 13, 10956.
Benalcázar-Rojas, V.R.; Yambay-Vallejo, W.J.; Herrera-Granda, E.P. Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks. Appl. Sci.2023, 13, 10956.
Benalcázar-Rojas, V.R.; Yambay-Vallejo, W.J.; Herrera-Granda, E.P. Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks. Appl. Sci. 2023, 13, 10956.
Abstract
Paving blocks are concrete pieces placed in exposed places to the weather, which are sub-jected to loads and wear. Hence, quality control in the manufacture of paving blocks is es-sential to guarantee the properties and durability of the product in construction projects. In Ecuador, the requirements are described in the Ecuadorian technical standard "NTE INEN 3040", and tensile splitting strength is a fundamental requirement to guarantee product quality. It is analyzed using quality control measurements such as dimensions, the weight of the fresh paving block in the vibro-compacted process, and the percentage of water absorption in order to know how the variables influence and manage to predict the tensile splitting strength to avoid product non-conformity in advance, having a timely and better control of the manufacturing process. The data was obtained from a company that can produce 30 000 units per day of rectangular paving blocks with 6 cm thickness. Mul-tivariate models such as multiple linear regression, regression trees, random forests and neural networks are performed to predict the tensile splitting strength variable through two groups of predictors; the first group is the thickness mm, width mm, length mm, mass of fresh paving block g and percentage of water absorption %. The second group of pre-dictor variables is the density of the fresh paving block kg/m3 and the percentage of water absorption %. It is concluded that the multiple linear regression method performs better in predicting the first group of predictor variables with a mean square error (MSE) of 0.110086, followed by the neural network without hidden layers resulting in an MSE of 0.112198. The best method for the second set of predictors was the neural network with-out hidden layers with a mean square error ( MSE ) of 0.112402, closely followed by the multiple linear regression model with an MSE of 0.115044.
Keywords
Prediction of tensile splitting strength; quality in concrete paving blocks; density of the fresh paving block; water absorption of concrete paving blocks; weight of the fresh paving blocks
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
