In this study, vibration characteristics of offshore wind turbine tower (WTT) with gravity-based foundation (GBF) are identified from dynamic responses under wave-induced excitations. The following approaches are implemented to achieve the objective. Firstly, the operational modal analysis methods such as frequency domain decomposition (FDD) and stochastic subspace identification (SSI) are selected to estimate modal parameters from output-only dynamic responses. Secondly, a GBF WTT model composed of superstructure, substructure and foundation is simulated as a case study by using a structural analysis program, MIDAS FEA. Thirdly, wave pressures acting on the WTT structure are established by nonlinear regular waves which are simulated from a computational fluid software, Flow 3D. Wave-induced acceleration responses of the target structure are analyzed by applying the simulated wave pressures to the GBF WTT model. Finally, modal parameters such as natural frequencies and mode shapes are estimated from the output-only acceleration responses and compared with the results from free vibration analysis. The effect of wave height and period on modal parameter extraction is also investigated for the mode identification of the GBF WTT.

The development of the low power application such as wireless sensors and health monitoring systems, attract a great attention to low power vibration energy harvesters. The recent vibration energy harvesters use smart materials in their structures to convert ambient mechanical energy into electricity. The frequent model of this harvesters is cantilevered beam. In the literature, the base excitation cantilevered harvesters are mainly investigated, and the related models are presented. This paper investigates a tip excitation cantilevered beam energy harvester with permendur. In the first section, the mechanical model of the harvester and magneto-mechanical model of the permendur are presented. Later, to find the maximum output of the harvester, based on the response surface method (RSM), some experiments are done, and the results are analyzed. Finally, to verify the results of RSM, a harvester with optimum design variables is made, and its output power is compared. The last comparison verifies the estimation of the RSM method which was about $381{\mu}W/cm^3$.

The present study analyzed free vibration of the three-layered micro annular/circular plate which its core and face sheets are made of saturated porous materials and FG-CNTRCs, respectively. The structure is subjected to magneto-electric fields and magneto-electro-mechanical pre loads. Mechanical properties of the porous core and also FG-CNTRC face sheets are varied through the thickness direction. Using dynamic Hamilton's principle, the motion equations based on MCS and FSD theories are derived and solved via GDQ as an efficient numerical method. Effect of different parameters such as pores distributions, porosity coefficient, pores compressibility, CNTs distribution, elastic foundation, multi-physical pre loads, small scale parameter and aspect ratio of the plate are investigated. The findings of this study can be useful for designing smart structures such as sensor and actuator.

Cable-stayed bridges are attractive due to their beauty, reducing material consumption, less harm to the environment and so on, in comparison with other kinds of bridges. As a massive structure with long period and low damping (0.3 to 2%) under many dynamic loads, these bridges are susceptible to fatigue, serviceability disorder, damage or even collapse. Tuned Mass Damper (TMD) is a suitable controlling system to reduce the vibrations and prevent the threats in such bridges. In this paper, Multi Tuned Mass Damper (MTMD) system is added to the Ahvaz cable stayed Bridge in Iran, to reduce its seismic vibrations. First, the bridge is modeled in SAP2000 followed with result verification. Dead and live loads and the moving loads have been assigned to the bridge. Then the finite element model is developed in OpenSees, with the goal of running a nonlinear time-history analysis. Three far-field and three near-field earthquake records are imposed to the model after scaling to the PGA of 0.25 g, 0.4 g, 0.55 g and 0.7 g. Two MTMD systems, passive and active, with the number of TMDs from 1 to 8, are placed in specific points of the main span of bridge, adding a total mass ratio of 1 to 10% to the bridge. The parameters of the TMDs are optimized using Genetic Algorithm (GA). Also, the optimum force for active control is achieved by Fuzzy Logic Control (FLC). The results showed that the maximum displacement of the center of the bridge main span reduced 33% and 48% respectively by adding passive and active MTMD systems. The RMS of displacement reduced 37% and 47%, the velocity 36% and 42% and also the base shear in pylons, 27% and 47%, respectively by adding passive and active systems, in the best cases.

Composite floor structures formed by continuous slab panels may be susceptible to excessive vibrations, even when properly designed in terms of ultimate limit state criteria. This is due to the inherent vibration characteristics of continuous floor slabs composed by precast orthotropic reinforced concrete panels supported by steel beams. These floor structures display close spaced multimode vibration frequencies and this dynamic characteristic results in a non-trivial vibration problem. Structural stiffening and/or insertion of struts between floors are the usual tentative solution applied to existing vibrating floor structures. Such structural alterations are in general expensive and unsuitable. In this paper, this vibration problem is analyzed on the basis of results obtained from experimental measurements in typical composite floors and their theoretical counterpart obtained with computational modeling simulations. A passive control system composed by multiple synchronized dynamic attenuators (MSDA) was designed and installed in these floor structures and its efficiency was evaluated both experimentally and through numerical simulations. The results obtained from experimental tests of the continuous slab panels under human walking dynamic action proved the effectiveness of this control system in reducing vibrations amplitudes.

In this paper, the interaction between notch and micro pore under uniaxial compression has been performed experimentally and numerically. Firstly calibration of PFC2D was performed using Brazilian tensile strength, uniaxial tensile strength and biaxial tensile strength. Secondly uniaxial compression test consisting internal notch and micro pore was performed experimentally and numerically. 9 models consisting notch and micro pore were built, experimentally and numerically. Dimension of these models are 10 cm*1 cm*5 cm. the length of joint is 2 cm. the angularities of joint are $30^{\circ}$, $45^{\circ}$ and $60^{\circ}$. For each joint angularity, micro pore was situated 2 cm above the lower tip of the joint, 2 cm above the middle of the joint and 2 cm above the upper of the joint, separately. Dimension of numerical models are 5.4 cm*10.8 cm. The size of the cracks was 2 cm and its orientation was $30^{\circ}$, $45^{\circ}$ and $60^{\circ}$. Diameter of pore was 1cm which situated at the upper of the notch i.e., 2 cm above the upper notch tip, 2 cm above the middle of the notch and 2 cm above the lower of the notch tip. The results show that failure pattern was affected by notch orientation and pore position while uniaxial compressive strength is affected by failure pattern.

This paper presents the results of axial pressure testing on reinforced concrete columns (RCCs) filled with confined normal concrete (NC) and high-strength concrete (HSC) using glass-fiber reinforced plastic pipes (GRP) casing as well as fiber reinforced polymer (FRP). This study aims to evaluate the behavior and mechanical properties of columns confined with GRP casing and FRP wrapping under pressure loads. The major parameters in the experiments were the type of concrete, the effect of GRP casing and FRP wrapping, as well as the number of FRP layers. 12 cylindrical RCCs (150*600) mm were prepared and divided into two groups, NC and HSC, and each group was divided into two parts. In each part, one column was without FRP strengthening layer, a column was wrapped with one FRP layer and another column with two FRP layers. All columns were tested under concentrated compression load. The results of the study showed that the utilization of FRP wrapping and GRP casing improved compression capacity and ductility of RCCs. The addition of one and two layers-FRP wrapping increased compression capacity in the NC group to an average of 18.5% and 26.5% and to an average of 10.2% and 24.8% in the HSC group. Meanwhile, the utilization of GRP casing increased the compression capacity of the columns by 4 times in the NC group and 3.38 times in the HSC group. The results indicated that although both FRP wrapping and GRP casing result in confinement, the GRP casing resulted in increased compression capacity and ductility of the RCCs due to higher confinement. Furthermore, the confinement effect was higher on columns made with NC.

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.