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In structural analysis, P-Delta effect refers to the changes in internal forces due to P-Delta moment which can be found by multiplying gravity load (P) by the lateral displacement of structure for earthquake lateral load (Delta). The influence of P-Delta in elastic response of structures is ignorable, but P-Delta effect should be given more attention when the structure responds in inelastic range. On the other hand, the influence of ground motion duration is magnified when P-Delta effect is considered in the structural analysis. In this paper, to consider the simultaneous effect of ground motion duration and P-Delta effect, an algorithm is implemented in MATLAB which employs OPENSEES for linear and nonlinear dynamic analysis of elasto-plastic systems subjected to long- and short-duration records. The analysis results indicate that the frequency of structural collapse for long-duration ground motions is about twice of short-duration ground motions. The frequency of collapse also increases when ductility and stability index increase, but decreases when the period of structure increases. Furthermore, the increase of ground motion duration has no influence on the residual displacement of structures, but nonlinear behavior of structures and gravity load effect increase the residual displacements.

In order to predict changes in coastal profile, it is necessary to investigate the sediment transport rate along the coast. Sediment transport is important in the areas of sedimentology, geomorphology, civil engineering and environmental engineering. The study of sediment transport is often performed to determine the location of erosion or deposition, the amount, timing and distance of its occurrence. Forecasting future coastline changes are as a result of the marine structural development. It provides the conditions for appropriate engineering decision-making. It also makes the grounds for sustainable use of the coast. When spot and local forecasts are necessary, models based on time series like support vector regression and Artificial Neural Network as new solutions are taken into consideration. These methods are one of the ways of machine learning. This study was investigated the importance of the offshore sediment transport rate in this research for the desired beach on the west coast of Hormozgan province. The littoral drift (LITDRIFT) model is used to get this type of transfer rate. To estimate evaluation of the longshore sediment transport rate by support vector machine (SVM); it will be needed two categories of data; one for data training and the other to check the machine for testing. The results of estimation of sediment transport rate using support vector regression and artificial neural network method showed the superiority of support vector regression over the neural network in both training and experimental groups of data.

One of the most important structures in the ports is the wharf. The most common one is the pile-supported wharf. This type of wharf is consisted of a number of piles and one deck which placed on the piles. In addition to the conventional loads that this structure should withstand, in seismic areas, pile-supported wharfs should have the necessary capacity and strength against seismic excitations. There are some approaches to increase the seismic capacity of the berth. One of these methods is to control the vibrations of the pile-supported wharf against earthquake loads using a damper. In this research, for the first time, a new semi-active damper called the semi active liquid column gas damper (SALCGD), was used to reduce the response of pile supported wharf under seismic loads. In the first step by applying different records of the earthquake, the most important parameter of this damper - the optimal opening ratio of the horizontal column- was obtained for this particular structure. In the following, the performances of this damper and its comparison with the tuned liquid column gas damper (TLCGD) were discussed. This study showed that the use of this semi-active damper (SALCGD) reduces the displacement of the pile-supported wharf by 35% and reduces the acceleration of the structure by 50% on average. In contrast, the passive damper (TLCGD) reduces the displacement of about 20 percent and the acceleration of about 30 percent. Therefore, it was observed that the semi-activation of the damper (SALCGD) had a significant improvement in its performance in controlling the vibrations of pile-supported wharf.

In recent years, the use of offshore wind turbines has been considered on the agenda of the countries which have a significant maritime boundary due to more speed and stability of wind at sea. The aim of this study is to investigate the effect of wind turbulence on the aero-hydrodynamic behavior of offshore wind turbines with a monopile platform. Since in the sea, the wind turbine structures are under water and structures interactions, the dynamic analysis has been conducted under combined wind and wave loadings. The offshore wind turbines have been investigated under two models of normal and severe wind turbulence, and the results of this study show that the amplitude of fluctuation of dynamic response is increased with increasing amount of wind turbulence, and this increase is not necessarily observed in the mean values of responses. Therefore, conducting the dynamic analysis is inevitable in order to observe the effect of wind turbulence on the structures response.

This paper presents the effect of hydrodynamic parameters of the two-body converters of a point wave absorber on the amount of power output. This converter includes two submerged and floating bodies which are connected to the spring-damper system. The whole of the converter is connected to the sea bed by mooring catenary. The relative displacement of the floating body and the submerged body is the main factor in generating electrical energy. Since the calculation of hydrodynamic coefficients has a significant effect on the solution of dynamic equations, this study focused on the calculation of added mass and hydrodynamic damping by boundary element method using the ANSYS-AQWA software. Also, this paper investigates the effect of floating borehole geometry on the hydrodynamic parameters and the extracted power of the converter using complementary analysis on the domain of time and frequency. Comparison of numerical simulation outputs and the results from the laboratory work which had carried out by Sandiego researchers in 2011, shows the suitable accuracy of the simulation. According to the results, with the two meters increase in buoy diameter, the power output will increase by 20%, and the output power for the half- conical is 17% more than the hemisphere.

Tuned liquid column gas damper is a new type of energy absorber that can mitigate the vibrations of structures if their frequency and mass parameters are well tuned. Since this damper has recently been introduced and its behaviour in certain structures such as offshore oil platforms and wind turbines has already been tested, a suitable and accurate method is required to identify these optimal parameters. Therefore, considering the complexity of loads exerted on wind turbines in seas (wave and wind loads), in present study attempts are made to use a new artificial neural network approach to obtain optimal tuned liquid column–gas damper (TLCGD) parameters for mitigation of wind turbine vibrations. First fixed offshore wind turbines at various depths are designed in the MATLAB coding environment. After obtaining the stiffness, damping and mass matrices of the structures, the program enters the Simulink, and the wind turbine structure along with the TLCGD is exposed to different wave-wind load combinations within reasonable range of damper parameters. The neural network training is launched based on available statistical data of the offshore wind turbine with different heights as well as different frequency and mass ratios of the damper. According to this method, the percentage of errors found in the neural network outputs was negligible compared to the actual results obtained from the analysis in Simulink (even for inputs that stood outside the training range of the neural network). The mean error percentage, the standard deviation and the effective value of the neural network with actual values are below 10% for all three types of the structure. Finally, the method presented in this study can be used to obtain optimal parameters of the TLCGD for all kinds of offshore wind turbines at different depths of the sea, which leads to the optimal design of this damper to reduce the vibrations of wind turbines under wave and wind load pressures.