A dynamic behaviour of a cylindirical wind tower with variable cross section is investigated under environmental and earthquake forces.e ground acceleration term is represented by a simple cosine function to investigate both normal and parallel components of the earthquake motions located near ground surface. e function of earthquake force is simplified to apply Rayleigh's energy method. Wind forces acting on above the water level and wave forces acting on below this level are utilized in computations considering earthquake effect for entire structure. e wind force is divided into two groups: the force acting on the tower and the forces acting on the rotor nacelle assembly (RNA). e drag and the inertial wave forces are calculated with water particle velocities and accelerations due to linear wave theory. e resulting hydrodynamic wave force on the tower in an unsteady viscous flow is determined using the Morison equation. e displacement function of the physical system in which dynamic analysis is performed by Rayleigh's energy method is obtained by the single degree of freedom (SDOF) model. e equation of motion is solved by the fourth-order Runge-Kutta method. e two-way FSI (fluid-structure interaction) technique was used to determine the accuracy of the numerical analysis. e results of computational fluid dynamics and structural mechanics are coupled in FSI analysis by using ANSYS software. Time-varying lateral displacements and the first natural frequency values which are obtained from Rayleigh's energy method and FSI technique are compared. e results are presented by graphs. It is observed from these graphs that the Rayleigh model can be an alternative way at the prelimanary stage of the structural analysis with acceptable accuracy.

Site exploration, characterization and prediction of soil properties by in-situ test are key parts of a geotechnical preliminary process. In-situ testing is progressively essential in geotechnical engineering to recognize soil characteristics alongside. In this study, radial basis neural network (RBNN) model was developed for estimating standard penetration resistance (SPT-N) value. In order to develop the RBNN model, 121 SPT-N values collected from 13 boreholes spread over an area of 17 km 2 of Izmir was used. While developing the model, borehole location coordinates and soil component percentages were used as input parameters. The results obtained from the model were compared with those obtained from the field tests. To examine the accuracy of the RBNN model constructed, several performance indices, such as determination coefficient, relative root mean square error, and scaled percent error were calculated. The obtained indices make it clear that the RBNN model has a high prediction capacity to estimate SPT-N.

The California Bearing Ratio (CBR) value of the soils is very important for geotechnical engineering and earth structures. A CBR value is a ected by the soil type and di erent soil properties. With this in view, in this paper, an attempt has been made for investigating the factors that a ect the CBR values of some Aegean sands collected from nine di erent locations in Manisa (Turkey). The sand samples were tested for mineralogy, particle shape and size, and speci c gravity. The CBR tests were then performed on these samples at di erent dry densities to examine the in uence of dry density, relative density, water content, and particle shape and size on the CBR value. Multiple Regression Analysis (MRA) was performed to predict the CBR value of the sands by using the experimental results. Moreover, several performance indices, such as coe cient of correlation and variance account for mean absolute error and root mean square error, were calculated to check the prediction capacity of the proposed MR equation. The obtained indices make it clear that the equation derived from the samples used in this study applies well, with an acceptable accuracy, to the CBR estimation at the preliminary stage of site investigations.

In this paper, the factor of safety values of soil against liquefaction (FS) were investigated by mean of artificial neural network (ANN) and multiple regression (MR). To achieve this, two earthquake parameters, namely, earthquake magnitude (M w ) and horizontal peak ground acceleration (a max ), and six soil properties, namely, standard penetration test number (SPT-N), saturated unit weight (γ sat ), natural unit weight (γ n ), fines content (FC), the depth of ground water level (GWL), and the depth of the soil (d) varied in the liquefaction analysis and then the FS value was calculated from the simplified method for each case by using the Excel program developed and utilized in the simulation of the feed forward ANN model with back propagation algorithm and the MR model. The FS values predicted from both ANN and MR models were contrasted with those calculated from the simplified method.In additionally, five different performance indices were used to evaluate the predictabilities of the models developed. These performance indices indicated that the ANN models is superior to the MR model in predicting the FS value of the soil.

is research focuses on the use of adaptive arti cial neural network system for evaluating the skid resistance value (British Pendulum Number; BPN) of the glass ber-reinforced tiling materials. During the creation of the neural model, four main factors were considered: ber, calcium carbonate content, sand blasting, and polishing properties of the specimens. e model was trained, tested, and compared with the on-site test results. As per the comparison of the outcomes of the study, the analysis and on-site test results showed that there is a great potential for the prediction of BPN of glass ber-reinforced tiling materials by using developed neural system.