Hybrid data‐driven hazard‐consistent drift models for SMRF

• Published in: Earthquake engineering & structural dynamics 2023-04, Vol.52 (4), p.1112-1135
• Main Authors: Zahra, Faridah, Macedo, Jorge, Málaga‐Chuquitaype, Christian
• Format: Article
• Language: English
• Subjects: Building codes; Consistency; Design; Design analysis; Drift; drift demands; earthquake engineering; Geological hazards; hazard‐consistent; Machine learning; Mechanics; Modelling; moment‐resisting frames; Preliminary designs; Seismic activity; Seismic design; Seismic hazard; Seismic properties; steel; Steel frames; Structural engineers; Structural models
• ISSN: 0098-8847; 1096-9845
• DOI: 10.1002/eqe.3807

The seismic design and assessment of steel moment resisting frames (SMRFs) rely heavily on drifts. It is unsurprising, therefore, that several simplified methods have been proposed to predict lateral deformations in SMRFs, ranging from the purely mechanics‐based to the wholly data‐driven, which aim to alleviate the structural engineer's burden of conducting detailed nonlinear analyses either as part of preliminary design iterations or during regional seismic assessments. While many of these methods have been incorporated in design codes or are commonly used in research, they all suffer from a lack of consideration of the causal link between the seismic hazard level and the ground‐motion suite used for their formulation. In this paper, we propose hybrid data‐driven models that preserve this critical relationship of hazard‐consistency. To this end, we assemble a large database of non‐linear response history analyses (NRHA) on 24 SMRFs of different structural characteristics. These structural models are subjected to 816 ground‐motion records whose occurrence rates and spectral shapes are selected to ensure the hazard consistency of our outputs. Two sites with different seismic hazards are examined to enable comparisons under different seismic demands. An initial examination of the resulting drift hazard curves allows us to re‐visit the influence of salient structural modelling assumptions such as plastic resistance, geometric configurations and joint deterioration modelling. This is followed by a machine learning (ML)‐guided feature selection process that considers structural and seismic parameters as well as key static response features, hence the hybrid nature of our models. New models for inter‐storey drift and roof displacements are then developed. A comparison with currently available formulations highlights the significant levels of overestimation associated with previously proposed non‐hazard consistent models.