Fatigue is a principal failure mode for steel structures, and it is still less understood than any other modes of failure. Fatigue life estimation of metal bridges is a major issue for making cost effective decisions on the rehabilitation or replacement of existing infrastructure. The fatigue design procedures given by the standard codes are either empirical or based on nominal stress approach. Since the fatigue life estimation through field measurements is difficult and costly, more researches are needed to develop promising techniques in the fatigue analysis of bridges through Finite Element Analysis (FEA). This paper aims to develop a methodology for the Fatigue life estimation of railway steel bridge using FEA. The guidelines of IIW-1823-07 were used in the development of the methodology. The Finite Element (FE) package ANSYS and the programming software MATLAB were used to implement this methodology on an Indian Railway Standard (IRS) welded plate girder bridge. The results obtained were compared with results from published literature and found satisfactory.

The aim of this research is to evaluate the effects of the number of spans, height of spans, number of floors, height of floors, column to beam moment of inertia ratio, and plastic joints distance of beams from columns on the ductility of moment frames. For the facility in controlling the ductility of the frames, this paper offers a simple relation instead of complex equations of different codes. For this purpose, 500 analyzed and designed frames were randomly selected, and their ductility was calculated by the use of nonlinear static analysis. The results cleared that the column-to-beam moment of inertia ratio had the highest effect on ductility, and if this relation was more than 2.8, there would be no need for using the complex relations of codes for controlling the ductility of frames. Finally, the ductility of the most frames of this research could be estimated by using the combination of genetic algorithm and artificial neural networks properly.

In the last decades, research efforts have been spent to investigate the effect of prestressing on the dynamic behaviour of prestressed concrete (PSC) beams. Whereas no agreement has been reached among the achievements obtained by different Researchers and among the theoretical and the experimental results for simply supported beams, very few researches have addressed this problem in continuous PSC beams. This topic is, indeed, worthy of consideration bearing in mind that many relevant bridges and viaducts in the road and railway networks have been designed and constructed with this structural scheme. In this paper the attention is, thus, focused on the dynamic features of continuous PSC bridges taking into account the effect of prestressing. This latter, in fact, contributes to the modification of the distribution of the bending stress along the beam, also by means of the secondary moments, and influences the flexural stiffness of the beam itself. The dynamic properties of a continuous, two spans bridge connected by a nonlinear spring have been extracted by solving an eigenvalue problem in different linearized configurations corresponding to different values of the prestress force. The stiffness of the nonlinear spring has been calculated considering the mechanical behaviour of the PSC beam in the uncracked and in the cracked stage. The application of the proposed methodology to several case studies indicates that the shift from the uncracked to the cracked stage due to an excessive prestress loss is clearly detectable looking at the variation of the dynamic properties of the beam. In service conditions, this shift happens for low values of the prestress losses (up to 20%) for structure with a high value of the ratio between the permanent load and the total load, as happens for instance in long span, continuous box bridges. In such conditions, the detection of the dynamic properties can provide meaningful information regarding the structural state of the PSC beam.

This paper reported test of full-scale cables attached with four types of dampers: viscous damper, passive Magneto-Rheological (MR) damper, friction damper and High Damping Rubber (HDR) damper. The logarithmic decrements of the cable with attached dampers were calculated from free vibration time history. The efficiency ratios of the mean damping ratios of the tested four dampers to theoretical maximum damping ratio were derived, which was very important for practical damper design and parameter optimization. Non-ideal factors affecting damper performance were discussed based on the test results. The effects of concentrated mass and negative stiffness were discussed in detail and compared theoretically. Approximate formulations were derived and verified using numerical solutions. The critical values for non-dimensional concentrated mass coefficient and negative stiffness were identified. Efficiency ratios were approximately 0.6, 0.6, and 0.3 for the viscous damper, passive MR damper and HDR damper, respectively. The efficiency ratio for the friction damper was between 0-1.0. The effects of concentrated mass and negative stiffness on cable damping were positive as both could increase damping ratio; the concentrated mass was more effective than negative stiffness for higher vibration modes.

In this paper, damage assessment in wind-turbine towers using vibration-based artificial neural networks (ANNs) is numerically investigated. At first, a vibration-based ANNs algorithm is designed for damage detection in a wind turbine tower. The ANNs architecture consists of an input, an output, and hidden layers. Modal parameters of the wind turbine tower such as mode shapes and frequencies are utilized as the input and the output layer composes of element stiffness indices. Next, the finite element model of a real wind-turbine tower is established as the test structure. The natural frequencies and mode shapes of the test structure are computed under various damage cases of single and multiple damages to generate training patterns. Finally, the ANNs are trained using the generated training patterns and employed to detect damaged elements and severities in the test structure.