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Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar

Numerical modeling to predict the impact of granular glass replacement on mechanical properties... Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar

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References (38)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1563-0854
eISSN
2522-011X
DOI
10.1007/s42107-023-00753-8
Publisher site
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Abstract

Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76.

Journal

Asian Journal of Civil Engineering – Springer Journals

Published: Jun 16, 2023

Keywords: Compressive strength; Flexural strength; Modelling; Mortar; Waste glass

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