Light weight concrete mix design through fuzzy logic with Cosine similarity aided optimized rules

Abstract

In this paper, a novel model of mix design built on fuzzy concepts is elaborated. For the development of the model, around 300 lightweight mix design data which is a blend of manually generated data and the data garnered through standard research publications reporting laboratory research and field mix designs have been used. The manual designs are based on ACI guidelines and were generated over spread sheet. have been considered. The model has five inputs viz., density of course aggregate, nominal size of course aggregate, water-cement ratio, target strength and the slump. The outputs are the quantities of cement, water, lightweight aggregate and fine aggregate. This data driven model consisted of four phases, i. Fuzzification of inputs and outputs with appropriate granulation using linguistic terms, ii. Framing of conjunctive rules based on approximate reasoning mapping the antecedent space to consequent space, iii. Selection of similar rules using cosine similarity measure and selection of the rules that generated outputs with minimum error, and iv. testing and validation of the system using error metrics such as root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The so developed model with compact and optimized rule set was tested with test and validation data (15% of the data for each). The results of the system are encouraging with low values of error metrics such as RMSE (10%- 13%), MAE(7–14), and MAPE(7%-14%) over the predicted outputs. The system has also shown high values of coefficient of determination R2 (0.87–0.96) across the predicted outputs. The correlation coefficient (R) authenticating the closeness of predicted values of the outputs with actual values stood in the range of 0.93 – 0.96.