A numerical investigation regarding ductility evaluation of steel buildings with moment resisting steel frames is conducted. Bending (µ_Lϕ) and tension (µ_Lδ) local ductilities as well as story (µ_S) and global ductilities are studied. Global ductility is calculated as the mean values of story ductilities (µ_GS) and as the ratio of the maximum inelastic to yielding top displacements (µ_Gt). The ductility capacity is associated to drifts of about 5%. Ductility values significantly may vary with the strong motion, ductility definition, structural element, story number, type of analysis, and model. µ_Lϕ is much larger for beams than for columns. Even though the demands of µ_Lδ are considered an important issue they are less relevant than µ_Lϕ. µ_S is much smaller than µ_Lϕ for beams. µ_GS for dynamic analysis give reasonable values, but µ_Gt does not. µ_Lϕ, µ_S and µ_GS obtained from pushover are larger than those obtained from dynamic analysis and unlike the case of dynamic analysis, µ_Lϕ tend to increase with the story number showing an opposite trend. Considering that: µ_Gt for dynamic analysis results in unreasonable values, pushover analysis does not consider energy dissipation, the strong column–weak beam (SCWB) concept was followed in the model designs, and µ_Lδ is not relevant in framed steel buildings, the ratio (R_LG) of global to local ductility capacity is calculated as the ratio of µ_GS to µ_Lϕ of beams, for dynamic analysis. A value of 1/3 is proposed. Thus, if bending local ductility capacity is stated as the basis for the design, the global ductility capacity can be easily estimated.

With the aim to improve code-based real records selection criteria, an approach inspired in a parameter proxy of spectral shape, namedNp, is analyzed. The procedure is based on several objectives aimed to minimize the record-to-record variability of the ground motions selected for seismic structural assessment. In order to select the best ground motion set of records to be used as an input for nonlinear dynamic analysis, an optimization approach is applied using genetic algorithms focuse on finding the set of records more compatible with a target spectrum and targetNpvalues. The results of the newNp-based approach suggest that the real accelerograms obtained with this procedure, reduce the scatter of the response spectra as compared with the traditional approach; furthermore, the mean spectrum of the set of records is very similar to the target seismic design spectrum in the range of interest periods, and at the same time, similarNpvalues are obtained for the selected records and the target spectrum.

In this paper, various moment‐resisting steel frames (MRSFs) are subjected to 30 narrow‐band motions scaled at different ground motion intensity levels in terms of spectral acceleration at first mode of vibration S_a(T₁) in order to perform incremental dynamic analysis for peak and residual interstory drift demands. The results are used to compute the structural reliability of the steel frames by means of hazard curves for peak and residual drifts. It is observed that the structures exceed the threshold residual drift of 0.5%, which is perceptible to human occupants, and it could lead to human discomfort according to recent investigations. For this reason, posttensioned connections (PTCs) are incorporated into the steel frames in order to improve the structural reliability. The results suggest that the annual rate of exceedance of peak and residual interstory drift demands are reduced with the use of PTC. Thus, the structural reliability of the steel frames with PTC is superior to that of the MRSFs. In particular, the residual drift demands tend to be smaller when PTCs are incorporated in the steel structures.

The aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF) structures subjected to dynamics wind load using Artificial Neural Networks (ANNs) through the combination of several structural and turbulent wind parameters. The maximum story drift of 1600 MDOF structures under 16 simulated wind conditions are computed with the purpose of generating the data set for the networks training with the Levenberg–Marquardt method. The Shinozuka and Newmark methods are used to simulate the turbulent wind and dynamic response, respectively. In order to optimize the computational time required for the dynamic analyses, an array format based on the Shinozuka method is presented to perform the parallel computing. Finally, it is observed that the already trained ANNs allow for predicting adequately the maximum story drift with a correlation close to 99%.