Forecasting the Shear Strength of Binary Blended Concrete Containing Hydrated Lime Using Artificial Intelligence

Document Type : Regular Article

Authors

1 Senior Lecturer, Department of Civil and Environmental Engineering, University of Port-Harcourt, Nigeria

2 Professor, Department of Civil Engineering, Federal University of Technology, Owerri, Nigeria

Abstract

In this exploratory study, the shear strength of blended cement concrete made using the hydrated lime (HL) as an admixture was studied. 120 shear strength values were experimentally obtained for several mix ratios at 7, 14, 21, and 28 days. This concrete was put together from water, portland cement (PC), HL, river sand (RS), and granite chippings (GC). 96 of the results were utilized to formulate a Levernberg-Marquardt backpropagation artificial neural network (ANN) for determining the shear strength of the concrete. The effectiveness of the forecast was tested using the unused 24 results. The model had 6 input variables namely; proportions of PC, HL, RS, GC, water, and curing age. While the output variable was the shear strength value. 1 hidden layer of 20 neurons was adopted. Uppermost 28 days shear strength value of 1.257N/mm2 was observed at 13.75% replacement of PC with HL for 0.58 water-cement ratio. The performance of the ANN proved that the model was acceptably executed. Root mean square errors (RMSE) obtained between network forecast and experimental values ranged from 0.0278 to 0.06536. These are close to 0. In addition, the factor of agreement (IA) determined were within the limits of 0.0475 and 0.1747. These are between the stipulated range of 0 to 1 for consistency between variables. The highest average percentage error recorded between model predictions and experimental values was 2.5066%. Lastly, the ANN created can be convincingly used to predict the shear strength of hydrated lime cement concrete and eliminate the need for try-out laboratory research.

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