Abstract
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.
Highlights
In the current scenario, the construction industry utilises the natural resources which is a threat for the environment
Bheel et al [12] carried out mechanical examination on fly ash, and the results indicated that fly ash improves the microstructure in concrete as the breaking of fly ash plerospheres changes the behaviour of mortar
E research findings include a list of various mix proportions and the mechanical strength of the blocks at various ages. e main objective of this study is to investigate and predict the performance of interlocking blocks with various dosages of fly ash, cement, and crushed stone aggregate. is study highlights the effective replacement of cement with pozzolanic materials such as fly ash and GGBS. is research work addresses the optimum and costeffective proportions for the manufacture of interlocking blocks which can be used for affordable housing. ese research data will be useful for researchers and entrepreneurs who are interested in manufacturing vibro compacted hollow concrete blocks for affordable housing
Summary
Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN. Is paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. E artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigation. E experimental investigations were carried out in two phases. 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. The identified mix proportions were analysed using ANN to predict the compressive strength of interlocking blocks. E results indicate that the proposed ANN model developed to determine the mechanical strength and cost of interlocking blocks has excellent prediction ability The identified mix proportions were analysed using ANN to predict the compressive strength of interlocking blocks. e results indicate that the proposed ANN model developed to determine the mechanical strength and cost of interlocking blocks has excellent prediction ability
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