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Structural engineers often use deep columns in high seismic areas to reduce drifts, yet this somehow contradicts what is stated in some tests in the sense that even though deep columns may satisfy current seismic provisions, they can suffer premature twisting; this is an indication that a lot of research is needed in this area. Numerical and experimental studies have been conducted to estimate the response of steel buildings with medium and deep columns under the action of static and cyclic loading; however, studies accounting for the dynamic characteristics of buildings and strong motions are not common. In addition, responses in terms of local parameters have not been considered either. In this study, the nonlinear seismic responses of steel buildings with perimeter moment-resisting frames and medium (W14) columns are numerically calculated and compared to those of similar steel buildings with equivalent deep columns in terms of cost (W27 and larger). Low-, mid-, and high-rise steel building models with different dynamic characteristics, as well as several strong motions with different frequency contents, are considered. Results indicate that the drifts of the models with medium columns may be up to 140% greater than those of the models with deep columns. Significant reductions are also observed for top displacements, normalized interstory shears, and combined normalized axial loads and bending moments. Hence, the seismic demands of the buildings with deep columns may be much smaller than those of the buildings with medium columns and, therefore, the buildings with deep columns exhibit a superior behavior, which results in more economical designs. The reduction is greater for the case of low- and mid-rise buildings than for high-rise buildings. One of the reasons for this is that as medium columns are replaced by deep columns, the stiffness and the strength increase, which are lower in the tallest model.

A numerical investigation regarding ductility evaluation of steel buildings with moment resisting steel frames is conducted. Bending (µLϕ) and tension (µLδ) local ductilities as well as story (µS) and global ductilities are studied. Global ductility is calculated as the mean values of story ductilities (µGS) and as the ratio of the maximum inelastic to yielding top displacements (µGt). 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. µLϕ is much larger for beams than for columns. Even though the demands of µLδ are considered an important issue they are less relevant than µLϕ. µS is much smaller than µLϕ for beams. µGS for dynamic analysis give reasonable values, but µGt does not. µLϕ, µS and µGS obtained from pushover are larger than those obtained from dynamic analysis and unlike the case of dynamic analysis, µLϕ tend to increase with the story number showing an opposite trend. Considering that: µGt 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 µLδ is not relevant in framed steel buildings, the ratio (RLG) of global to local ductility capacity is calculated as the ratio of µGS to µLϕ 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.

The response of steel moment frames is estimated by first considering that the mass matrix is the concentrated type (ML) and then consistent type (MC). The effect of considering more than one element per beam is also evaluated. Low-, mid- and high-rise frames, modeled as complex-2D-MDOF systems, are used in the numerical study. Results indicate that if ML is used, depending upon the response parameter under consideration, the structural model, the seismic intensity and the structural location, the response can be significantly overestimated, precisely calculated, or significantly underestimated. Axial loads at columns, on an average basis, are significantly overestimated (up to 60%), while lateral drifts and flexural moments at beams are precisely calculated. Inter-story shears and flexural moments at columns, on average, are underestimated by up to 15% and 35%, respectively; however, underestimations of up to 60% can be seen for some individual strong motions. Similarly, if just one element per beam is used in the structural modeling, inter-story shears and axial loads on columns are overestimated, on average, by up to 21% and 95%, respectively, while the lateral drifts are precisely calculated. Flexural moments at columns and beams can be considerably underestimated (on average up to 14% and 35%, respectively), but underestimations larger than 50% can be seen for some individual cases. Hence, there is no error in terms of lateral drifts if ML or one element per beam is used, but significant errors can be introduced in the design due to the overestimation and underestimation of the design forces. It is strongly suggested to use MC and at least two elements per beam in the structural modeling.

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%.