Imperial College London
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Dr Christian Malaga-Chuquitaype

Faculty of Engineering, Department of Civil and Environmental Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 5007c.malaga Website CV

 
 
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Assistant

 

Ms Ruth Bello +44 (0)20 7594 6040

 
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Location

 

322Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zahra:2023:10.1002/eqe.3807,
author = {Zahra, F and Macedo, J and Malaga, Chuquitaype C},
doi = {10.1002/eqe.3807},
journal = {Earthquake Engineering and Structural Dynamics},
pages = {1112--1135},
title = {Hybrid data-driven hazard-consistent drift models for SMRF},
url = {http://dx.doi.org/10.1002/eqe.3807},
volume = {52},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 mechanicsbased to the wholly datadriven, 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 groundmotion suite used for their formulation. In this paper, we propose hybrid datadriven models that preserve this critical relationship of hazardconsistency. To this end, we assemble a large database of nonlinear response history analyses (NRHA) on 24 SMRFs of different structural characteristics. These structural models are subjected to 816 groundmotion 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 revisit 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 interstorey drift and roof displacements are then developed. A comparison with currently available formulations highlights the significant levels of overestimation associated with previously proposed nonhazard consistent models.
AU  - Zahra,F
AU  - Macedo,J
AU  - Malaga,Chuquitaype C
DO  - 10.1002/eqe.3807
EP  - 1135
PY  - 2023///
SN  - 0098-8847
SP  - 1112
TI  - Hybrid data-driven hazard-consistent drift models for SMRF
T2  - Earthquake Engineering and Structural Dynamics
UR  - http://dx.doi.org/10.1002/eqe.3807
UR  - https://onlinelibrary.wiley.com/doi/10.1002/eqe.3807
UR  - http://hdl.handle.net/10044/1/102260
VL  - 52
ER  -
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