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Soft computing modeling on air-cured slag-fly ash-glass powder-based alkali activated masonry elements developed using different industrial waste aggregates

Soft computing modeling on air-cured slag-fly ash-glass powder-based alkali activated masonry... Many studies have proven that the employ of alkali activated concrete (AAC) will efficiently furnish eco-friendly and sustainable solutions for the hitches coupled with Portland cement-based cementitious concretes. There are limited studies on the development of soft computing models on the strength characteristics of AAC structural masonry applications. This paper presents an experimental and soft computing modeling result on the strength performances (compression, split tension and flexure) on the AAC structural masonry block elements. The AAC mixes were developed incorporating the Ground Granulated Blast Furnace slag (75%), finely ground waste glass (15%), and fly-ash (10%) as cementing ingredients and the liquid sodium silicate and sodium hydroxide solution (activator modulus = 1.25) were used as alkaline activator solution. A total of 18 different mix proportion designs were developed at different combinations of aggregates, namely Natural Coarse Aggregates (NCA), Recycled Coarse Aggregates (RCA), River Sand Fine Aggregates (RSFA), Crusher Dust Fine Aggregates (CDFA) and for every mix 6 individual sample results were obtained, respectively, for compression, split tension and flexure tests, i.e., a total of 108 results from laboratory tests; out of which 70% of sample results are utilized for training, 25% for testing and 5% for validation of the artificial neural network (ANN) models obtained using MATLAB environment. The model performances were evaluated through the statistical indicators such as RMSE, R2, CC, SI and NSE to choose the efficient/best model. The outcome from this research work effectively proposes a novel type of soft computing model to select the optimum ingredients in producing the desired quality alkali-activated structural masonry block mixes by the use of non-congenital waste aggregates through reduced manual labor efforts and to conserve precious time spent otherwise in the testing laboratories. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

Soft computing modeling on air-cured slag-fly ash-glass powder-based alkali activated masonry elements developed using different industrial waste aggregates

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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/s42107-023-00584-7
Publisher site
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Abstract

Many studies have proven that the employ of alkali activated concrete (AAC) will efficiently furnish eco-friendly and sustainable solutions for the hitches coupled with Portland cement-based cementitious concretes. There are limited studies on the development of soft computing models on the strength characteristics of AAC structural masonry applications. This paper presents an experimental and soft computing modeling result on the strength performances (compression, split tension and flexure) on the AAC structural masonry block elements. The AAC mixes were developed incorporating the Ground Granulated Blast Furnace slag (75%), finely ground waste glass (15%), and fly-ash (10%) as cementing ingredients and the liquid sodium silicate and sodium hydroxide solution (activator modulus = 1.25) were used as alkaline activator solution. A total of 18 different mix proportion designs were developed at different combinations of aggregates, namely Natural Coarse Aggregates (NCA), Recycled Coarse Aggregates (RCA), River Sand Fine Aggregates (RSFA), Crusher Dust Fine Aggregates (CDFA) and for every mix 6 individual sample results were obtained, respectively, for compression, split tension and flexure tests, i.e., a total of 108 results from laboratory tests; out of which 70% of sample results are utilized for training, 25% for testing and 5% for validation of the artificial neural network (ANN) models obtained using MATLAB environment. The model performances were evaluated through the statistical indicators such as RMSE, R2, CC, SI and NSE to choose the efficient/best model. The outcome from this research work effectively proposes a novel type of soft computing model to select the optimum ingredients in producing the desired quality alkali-activated structural masonry block mixes by the use of non-congenital waste aggregates through reduced manual labor efforts and to conserve precious time spent otherwise in the testing laboratories.

Journal

Asian Journal of Civil Engineering – Springer Journals

Published: Sep 1, 2023

Keywords: Alkali activation; Masonry blocks; Strength; Soft computing; MATLAB; Industrial wastes; Artificial neural network (ANN)

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