No abstract

No abstract

The effects of recycled crumb rubber up to 25% replacement of fine and coarse aggregates in two discreet gradations combined with 0% to 25% substitution of cement by silica fume were studied on the mechanical and fracture properties of self‐compacting concrete (SCC). For acquiring a diverse means of comparison, toughness index and energy absorbency of different mixtures were tested, and stress intensity factors of different mixtures were obtained. Additionally, statistical analyses were conducted to verify the validity of the results, and also, regression analyses were conducted to predict the values of flexural strength, toughness index, energy absorption, and fracture toughness of SCC mixtures. The results show that the addition of crumb rubber reduces the modulus of rupture, energy absorbency, and fracture toughness values, but the incorporation of silica fume can compensate for the losses. Conversely, there was a large rise in toughness index when adding crumb rubber.

No abstract

The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.