Modelling the effects of petroleum product contaminated sand on the compressive strength of concretes using fuzzy logic and artificial neural networks: A case study of diesel

Chidozie Chukwuemeka Nwobi-Okoye and Ikechukwu Etienne Umeonyiagu

African Journal of Science, Technology, Innovation and Development, 2016, vol. 8, issue 3, 264-274

Abstract: In this study an artificial neural network (ANN) and fuzzy logic (FL) were used to predict the compressive strength of concrete produced with diesel contaminated sand. Concrete was produced using sand contaminated with diesel at 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5 and 10 percent, and each batch cured for 7, 14, 28, 58, 90 and 118 days. The compressive strength of the concretes was measured for each percentage contamination and curing time. Subsequently, an ANN and FL models were developed used to predict the compressive strength of the concrete. The ANN model predictions with a correlation coefficient (R) of 0.99316 predicted better than two FL models which predicted with correlation coefficient (R) values of 0.9086 and 0.8038 respectively. The results show that ANN and FL models could be used to predict the compressive strength of concretes produced with diesel contaminated sand.

Date: 2016
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DOI: 10.1080/20421338.2016.1156840

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