Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN

This paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. The artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigat...

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Bibliographic Details
Published in:Advances in civil engineering 2021-01, Vol.2021 (1)
Main Authors: Prakash A, Krishna, Helena H, Jane, Oluwaseun Awoyera, Paul
Format: Article
Language:English
Subjects:
Affordable housing
Artificial neural networks
Back propagation
Bias
Blast furnace components
Blast furnace practice
Civil engineering
Compressive strength
Concrete
Concrete blocks
Concrete mixing
Crushed stone
Fly ash
GGBS
Granulation
Learning theory
Locking
Masonry
Neural networks
Optimization
Seismic engineering
Slag
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Summary:This paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. The artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigation. The ANN model is based on the Levenberg–Marquardt principle which is executed using MATLAB. The inputs are given in the percentage ratio of cement: fly ash: crushed stone aggregate (FA): coarse aggregate (CA) for the process of learning, testing, and validation. The selected model is subjected to several trials in terms of mean square error, containing 4 input, 2 sets of 10 hidden layers, and one output components. In this study, a total of 2600 blocks of different mixes were tested as per IS 2185-1 (2005) to assess 3, 7, 14, 21, and 28 days’ strength. The experimental investigations were carried out in two phases. In the first phase, experimental investigations to identify the optimum mix proportions of cement, aggregate, fly ash, and ground granulated blast furnace slag to achieve desired compressive strength was carried out. In the second phase, the identified mix proportions were analysed using ANN to predict the compressive strength of interlocking blocks. The results indicate that the proposed ANN model developed to determine the mechanical strength and cost of interlocking blocks has excellent prediction ability.
ISSN:1687-8086
1687-8094
DOI:10.1155/2021/9952781