Neural prediction of mechanical properties of fiber-reinforced lightweight concrete containing silica fume and nano-silica

Experimenting to acquire the optimum result for producing a product in a real environment takes a long time and has various costs. Numerical simulations help save time and improve accuracy in implementing numerous complex tests. The present study exploits neural networks in MATLAB to calculate the mechanical properties of fiber-reinforced lightweight concrete under different fractions of silica fume and Nano silica, steel and polypropylene fibers, cement, and scoria. Concrete specimens were constructed under different mix designs and subjected to 7- and 28-day compressive, tensile, flexural, and initial and ultimate water absorption tests. Then, a multilayer perceptron (MLP) was used as the neural network. Furthermore, 70 % of the specimens were utilized as the training data samples, 15 % were exploited as the validation data samples, and the remaining 15 % were employed as the testing data samples. The MLP was trained for seven inputs, one hidden layer, and 20 neurons. The model training, testing, and overall accuracy were 100 %, 97.3 %, and 99.5 %, respectively, indicating the model is efficient and effective. Experimenting to acquire the optimum result for producing a product in a real environment takes a long time and has various costs. Numerical simulations help save time and improve accuracy in implementing numerous complex tests. The present study exploits neural networks in MATLAB to calculate the mechanical properties of fiber-reinforced lightweight concrete under different fractions of silica fume and Nano silica, steel and polypropylene fibers, cement, and scoria. Concrete specimens were constructed under different mix designs and subjected to 7- and 28-day compressive, tensile, flexural, and initial and ultimate water absorption tests. Then, a multilayer perceptron (MLP) was used as the neural network. Furthermore, 70 % of the specimens were utilized as the training data samples, 15 % were exploited as the validation data samples, and the remaining 15 % were employed as the testing data samples. The MLP was trained for seven inputs, one hidden layer, and 20 neurons. The model training, testing, and overall accuracy were 100 %, 97.3 %, and 99.5 %, respectively, indicating the model is efficient and effective.