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In this study, the effects of nano-CuO (NC) on engineering properties of fibre-reinforced mortars incorporating metakaolin (MK) were investigated. The effects of polypropylene fibre (PP) were also examined. A total of twenty-six mixtures were prepared. The experimental results were compared with numerical results obtained by adaptive neuro-fuzzy inference system (ANFIS) and Primal Estimated sub-GrAdient Solver for SVM (Pegasos) algorithm. Scanning Electron Microscope (SEM) was also employed to investigate the microstructure of the cement matrix. The mechanical test results showed that both compressive and flexural strengths of cement mortars decreased with the increase of MK content, however the strength values increased significantly with increasing NC content in the mixture. The water absorption of samples decreased remarkably with increasing NC particles in the mixture. When PP fibres were added, the strengths of cement mortars were further enhanced accompanied with lower water absorption values. The addition of 2 wt % and 3 wt % nanoparticles in cement mortar led to a positive contribution to strength and resistance to water absorption. Mixture of PP-MK10NC3 indicated the best results for both compressive and flexural strengths at 28 and 90 days. SEM images illustrated that the morphology of cement matrix became more porous with increasing MK content, but the porosity reduced with the inclusion of NC. In addition, it is evident from the SEM images that more cement hydration products adhered onto the surface of fibres, which would improve the fibre–matrix interface. The numerical results obtained by ANFIS and Pegasos were close to the experimental results. The value of R2 obtained for each data set (validate, test and train) was higher than 0.90 and the values of mean absolute percentage error (MAPE) and the relative root mean squared error (PRMSE) were near zero. The ANFIS and Pegasos models can be used to predict the mechanical properties and water absorptions of fibre-reinforced mortars with MK and NC.

Use of composite steel shear walls (CSSW) in earthquake-resistant structures has grown in recent years. However, no thorough information exists on their performance, especially in cases where openings are present. In the present study, in order to first validate the analysis method, ABAQUS was used to model the studied composite shear wall with gap at UC-Berkeley, according to the results of which, a good agreement between the experimental and analytical models was observed. Then, the effect of the position and number of the openings on the performance of the walls was addressed. To this end, models with various openings, including openings close to the beam/column, horizontal/vertical openings and distributing opening, were prepared and analyzed. The results indicate that the maximum reduction in stiffness and strength occurred in walls with single openings. The size of opening and the opening’s area significantly affect shear wall performance. Ultimately, artificial neural network and fitness function tools were employed to obtain predictive models for shear wall performance. A neural network has proven an appropriate alternative method for predicting the displacement, stress, and strength of the composite shear wall.

In the recent decades, different kinds of fiber materials are used for improving the asphalt mixture performance. Meanwhile, different kinds of fiber are used excessively due to their desirable physical and chemical properties and their easier application. The main purpose of this research is to evaluate the characteristics of the asphalt mixture while using basalt fiber and glass fiber. In order to provide asphalt samples, these two types of fibers are used in different percentages. In this way, 42 samples (with different percentages of fiber and bitumen) were made using Marshal Hammer. In the next step, while constructing 63 asphalt samples using a gyratory device, then mix asphalt conventional tests include the determination of indirect tensile strength, moisture sensitivity test, and resilient modulus and creep tests performed. The results of this research indicate that using these two types of fibers increased the percentage of optimum bitumen and marshal resistance. At best, adding 0.1% glass fiber resulted in 13% increase in marshal resistance. Finally, ANFIS-GUI was used to estimate the experimental result and the feasibility of employing neural fuzzy network to predict the laboratory data have been evaluated.