A simplified model for seismic risk assessment of three-dimensional irregular steel moment frame buildings

Seismic risk assessment of irregular buildings requires nonlinear dynamic analysis and threedimensional (3D) models. However, creating and analyzing such models can be intricate and time-consuming, making the assessment process challenging. Furthermore, this complexity escalates when evaluating building stocks or generating a dataset of buildings with diverse attributes for use in learning algorithms. Therefore, this paper proposes an energy-based model for irregular steel moment-resisting frame buildings to simplify 3D models and improve the time efficiency of structural assessment compared to conventional 3D models. The proposed model is derived from the fish-bone model but has enhanced configuration, elastic, and inelastic properties. These improvements aim to broaden its applicability to encompass 3D irregular mid-rise buildings. Four-, six-, and nine-story irregular buildings in plan and height are utilized to validate the proposed model. The responses investigated in these buildings include eigenvalues, pushover curves, time history responses, incremental dynamic analysis results, fragility curves, and risk analysis outcomes. The findings demonstrate that the simplified model responses are in close agreement with those of 3D models. The comparison of results between the proposed simplified model and existing models indicates that the accuracy of the simplified model is at least 2.2 times that of the existing models. Moreover, it provides an average error reduction of 60 % in contrast to the existing models. These advancements have been achieved while the proposed simplified model maintains a level of time efficiency comparable to the current models. The results from the analysis of the created buildings using the proposed simplified model indicate that the model is suitable for application in the seismic risk assessment of building inventories and generating datasets for future research involving machine learning-based methodologies.