Seismic Drift Demand Estimation for Steel Moment Frame Buildings: From Mechanics-Based to Data-Driven Models

Guan, Xingquan; Burton, Henry; Shokrabadi, Mehrdad; Yi, Zhengxiang

doi:10.1061/(asce)st.1943-541x.0003004

Cited by 53 publications

(24 citation statements)

References 15 publications

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“…Predictive models of structural drift can generally be divided into: (i) classical mechanics‐based, (ii) hybrid and (iii) purely data‐driven models 7 . Mechanics‐based models, e.g., Refs 8–10 .…”

Section: Introductionmentioning

confidence: 99%

“…Predictive models of structural drift can generally be divided into: (i) classical mechanics-based, (ii) hybrid and (iii) purely data-driven models. 7 Mechanics-based models, e.g., Refs. [8][9][10] are derived solely from classical mechanics theory, which makes them easy to generalise and interpret, although they are also often oversimplified and imprecise.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid data‐driven hazard‐consistent drift models for SMRF

Zahra

Macedo

Mariotti

2023

Earthq Engng Struct Dyn

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid data‐driven hazard‐consistent drift models for SMRF

Zahra

Macedo

Mariotti

2023

Earthq Engng Struct Dyn

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“…Many publications described ML models considered in this study for predicting properties of concrete and reinforced concrete structures . Fewer papers have been published on ML applications to steel structures, including buckling analysis of beam-columns [66], cold-formed steel (CFS) space structure optimization [67], web crippling strength prediction [68], elastic distortional buckling stress determination [69,70], rotation capacity prediction [71], strength prediction of concrete-filled steel tubular columns [72], failure mode identification of column base plate connection [73], capacity prediction of cold-formed stainless steel tubular columns [74], seismic drift demand estimation for steel moment frame buildings [75], and shear strength of CFS channels with staggered perforated webs [76][77][78][79]. ML techniques were previously applied to steel cellular beams.…”

Section: Introductionmentioning

confidence: 99%

Buckling and ultimate load prediction models for perforated steel beams using machine learning algorithms

Degtyarev

Tsavdaridis

2022

Journal of Building Engineering

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“…Often, this mapping function is developed based on fundamental structural dynamic theory in conjunction with Newtonian mechanics. Such physics based approaches have found wide use in earthquake engineering practice due to underlying physical robustness and intuitiveness [4]. However, these methods often rely on many inherent simplistic assumptions, which can reduce the accuracy of response estimates.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading

Parida¹,

Bose²,

Butcher³

et al. 2022

Preprint

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The computationally expensive estimation of engineering demand parameters (EDPs) via finite element (FE) models, while considering earthquake and parameter uncertainty limits the use of the Performance Based Earthquake Engineering framework. Attempts have been made to substitute FE models with surrogate models, however, most of these models are a function of building parameters only. This necessitates re-training for earthquakes not previously seen by the surrogate. In this paper, the authors propose a machine learning based surrogate model framework, which considers both these uncertainties in order to predict for unseen earthquakes. Accordingly,

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Seismic Drift Demand Estimation for Steel Moment Frame Buildings: From Mechanics-Based to Data-Driven Models

Cited by 53 publications

References 15 publications

Hybrid data‐driven hazard‐consistent drift models for SMRF

Hybrid data‐driven hazard‐consistent drift models for SMRF

Buckling and ultimate load prediction models for perforated steel beams using machine learning algorithms

Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading

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