The study presented in this paper is focused on an analytical method for constructing fragility curves of a given class of existing structures, based on a stochastic approach and which makes use of the Hazus database. The analysed structure is a non-linear one degree of freedom (SDoF) system: its constitutive law is described by means of a hysteretic model, whose parameters are obtained by an identification procedure starting from the Hazus data. A particular class of structures, the essential facilities, are analyzed. The system's response to seismic action, here modelled by means of the modulated Clough and Penzien filtered stochastic process, is obtained by using the stochastic linearization technique and the covariance analysis. In order to develop the fragility curves, a displacement based damage index is adopted and, finally, fragility curves are obtained in terms of the probability of exceeding a given damage level, by using an approximate theory of stochastic processes. The main innovation of the proposed approach is that it can be directly extended to other kinds of structures which are not included in the Hazus database, since the procedure requires only the knowledge of the capacity curve, which can be obtained by standard procedures.

In this work a comparative analysis between different optimization criteria is performed for a linear Tuned Mass Damper (TMD) devices problem. Optimal TMD mechanical parameters are evaluated considering a simple one degree-of-freedom system subject to a base acceleration modeled as a stationary filtered stochastic process. Whereas common TMD optimization methods consider only TMD frequency and damping ratio as design variables, in this study a complete approach will be proposed, which takes into account also the TMD mass as design variable. The effectiveness of the vibration control strategy is evaluated expressing the objective function in terms of two different reduction factors of the system response: the displacement and the inertial acceleration, respectively. The mechanical characteristics of the TMD, frequency ratio, damping ration and mass ratio, represent the design variables of the optimization problem. This is carried out numerically and then various analyses of sensitivity are developed by varying the input and structural parameters in order to assess the efficiency of the TMD strategy under different input and mechanical configurations. Variations between different criteria are also evaluated.

Optimization is a central aspect of structural engineering, but its practical application hasn't been supported by mathematical and numerical tools because of inner strong non-linear aspects involved. Moreover during last few decades Evolutionary Algorithms (EAs) gives new interest and horizons in this specific topic, thanks to their strong capacity in treatment of these problems more efficiently than standard methods. But a common criticism to EAs is lack of efficiency and robustness in handling constraints, mainly because they were originally developed for unconstraint problems only. For this reason during past decade hy-brid algorithms combining evolutionary computation and constraint-handling techniques have shown to be effective in this specific area. Moreover still now this is a crucial point for practical applications in structural optimization. In this paper a Normalized Domination Se-lection-based (NDS) rule is proposed to solve constrained-handling optimization problems using a modified version of proposed Differential Evolution algorithm (NDS - DEa). The strategy developed doesn't requires any additional parameter, increasing the appeal for a simple implementation in many real problems by structural designer without a specific know-ledge in the field. Mainly it is based on a domination criteria in selection phase. Actually a common way for constrained handling is introducing a specific role for selection step, so that all other phase of EA aren't modified; in this way DE flow chart scheme doesn't present any modification from a standard unconstrained one. Anyway the specific constrained selection scheme plays an important role in solution search efficiency, certainly more than in unconstrained cases. Unconstrained selection is based only on comparing individuals OF values, but in constrained one it seemed somewhat different and complicated. The more simple, common and intuitive way for approaching this phase is the penalty function, where OF values are reduced for those individuals don't satisfying constraints disqualifies (unfeasible in-dividuals). It is immediate (and well known in literature) that depending on penalty low adopted, a more drastic or permissive surviving of unfeasible solutions happened. But this is a central point in this problems, because of in many cases indeed real optimal solutions lies just on one constraints, so that its correct evaluation needs of specific research around the boundary, not only in the feasible space. to develop this strategy the domination concept is related to the specific selection that has to be implemented. If in a unconstrained contest it means simply that the domination coincide with the OF ranking, in the constrained contest the question has to be properly treated. In fact there are three possible scenarios: ->both two individuals are feasible -> selection based on rank -> both two individuals are unfeasible -> selection of the feasible one -> one is feasible and the other is unfeasible

The objective of a base isolation system is to decouple the building from the damaging components of the earthquake by placing isolators between the superstructure and the foundation. The correct identification of these devices is, therefore, a critical step towards reliable simulations of base-isolated systems subjected to dynamic ground motion. In this perspective, the parametric identification of seismic isolators from experimental dynamic tests is here addressed. In doing so, the focus is on identifying Bouc-Wen model parameters by means of particle swarm optimization and differential evolution. This paper is especially concerned with the assessment of these non-classical parametric identification techniques using a standardized experimental protocol to set out the dynamic loading conditions. A critical review of the obtained outputs demonstrates that particle swarm optimization and differential evolution can be effectively exploited for the parametric identification of seismic isolators.

The paper is concerned with the analytical description of a resistance mechanism, not considered in previous models, by which the hoops contribute to the shear capacity of RC columns with circular cross sections. The difference from previous approaches consists in observing that, because of deformation, the hoops change their original shape and, as a consequence, their slope does not match anymore the original one in the neighborhood of a crack. The model involves two parameters only, namely the crack inclination and the hoop strain in the neighborhood of a crack. A closed-form analytical formulation to correlate the average value of the crack width and the hoop strain is also provided. Results obtained using the proposed model have been compared with experimental data, and a satisfactory agreement is found

Passive strategies based on the introduction of energy dissipating devices into the structures have received considerable attention in recent years. Within this framework, as re-liable and cheap energy-dissipation devices, viscous fluid dampers have been largely used in seismic protection of industrial machines, technical equipments, buildings and bridges. Since the versatility of this passive protection system satisfactorily meets a wide range of require-ments, a reliable identification of their nonlinear mechanical behavior is of outstanding im-portance. This paper focuses on the parametric identification of fractional derivative based models for nonlinear viscous dampers by means of non-classical methods, which are uncon-ventional algorithms whose inner work is based on socially, physically and/or biologically inspired paradigms. Non-classical strategies are potentially powerful tools for solving com-plex identification problems because of their start-point independence, noise robustness and the capability in looking for the best solution in a global way. In contrast, it is important to highlight that they typically possess weak forms of convergence. For better assessing the cor-rectness of some non-classical methods in parametric identification of viscous dampers, we perform a large comparative analysis which involve the following soft computing based tech-niques: a multi-species genetic algorithm, six standard differential evolution algorithms and four swarm intelligence based algorithms (including a chaotic particle swarm optimization algorithm). A numerical study is initially conducted in order to investigate the general relia-bility of these methods. Moreover, the paper also provides some results about the parametric identification of nonlinear viscous dampers by using experimental data. A critical review of the obtained evidences is given in order to provide useful guidelines for similar engineering applications.