The main objective of the present work is to utilize feedforward multi-layer perceptron (MLP) type of artificial neural networks (ANN) to find the combined effect of nano-silica and different fibers (steel, polypropylene, glass) on the toughness, flexural strength and fracture energy of concrete is evaluated.For this purpose, 40 mix plot including 4 series A and B and C and D, which contain, respectively, 0, 2, 4 and 6% weight of cement, nano-silica particles were used as a substitute for cement. Each of series includes three types of fibers (metal: 0.2, 0.3 and 0.5% volume and polypropylene: 0.1, 0.15 and 0.2 % volume and glass 0.15 and 0.2 and 0.3% by volume) were tested. The obtained results from the experimental data are used to train the MLP type artificial neural network. The Results of this study show that fibers conjugate presence and optimal percent of nano-silica improved toughness, flexural strength and fracture energy of concrete of Self-compacting concrete (SCC). Results of this study show that fibers conjugate presence and optimal per-cent of nano-silica improved toughness, toughness, fracture ener-gy and flexural strength of SCC.

In this research, the combined effect of nano-silica particles and three fiber types (steel, polypropylene and glass) on the mechanical properties (compressive, tensile and flexural strength) of reinforced self-compacting concrete(SCC) is evaluated. For this purpose, 70 mixtures in A, B, C, D, E, F and G series representing 0, 1, 2, 3, 4, 5 and 6 percent of nano-silica particles in replacing cement content are cast. Each series involves three different fiber types and content; 0.2, 0.3 and 0.5% volume for steel fiber, 0.1, 0.15 and 0.2% of volume for polypropylene fiber and finally 0.15, 0.2 and 0.3% of volume for glass fiber. The results show that the simultaneous usage of an optimum percentage of fiber and nano-silica particles will improve the mechanical properties of SCC. Moreover, the obtained results from the experimental data are used to train a multi-layer perception (MLP)type artificial neural network(ANN). The trained network is then used to predict the effect of various parameters on the desired output namely the flexural tensile strength, tensile strength behavior and compressive strength.

Ground motions at a point on the ground surface can be decomposed to six components, namely three translational components and three rotational components; translational components include two components in the horizontal plane, and one in the vertical direction. Rotation about horizontal axes leads to rising of rocking, while the rotational component about a vertical axis generates torsional effects even in symmetrical buildings. Due to evident and significant contribution of ground shakings to the overall response of structures, rocking and torsional components of these motions resulted by strong earthquakes are recently subjected to widespread researches by engineering and research communities. In this study, first rotational components of ground motion are determined using a method developed by Hong-Nan Li and et al (2004). This method is based on frequency dependence on the angle of incidence and the wave velocity. In consequence, aboveground steel storage tanks (ASSTs) with different water elevations have been analyzed with the effects of these six components of earthquake. Three translational components of six important earthquakes have been adopted to generate relevant rotational components based on SV and SH wave incidence by the Fast Fourier Transform (FFT) with the discrete frequencies of time histories of translational motion. Using finite element method, linear properties of tank material including steel for cylindrical tanks have been taken into with considering fluid-structure interaction. Numerical linear dynamic analysis of these structures considering six components of earthquake motions is presented; results are compared with cases in which three translational components are considered.