Analytical modeling and seismic performance of a novel energy dissipative kinematic isolation for building structures

Bantilas, Kosmas E.; Kavvadias, Ioannis E.; Elenas, Anaxagoras

doi:10.1016/j.engstruct.2023.116777

Cited by 4 publications

(1 citation statement)

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“…In contrast to fixed-base structural systems, whose seismic response is primarily governed by the dissipative energy process occurring at predefined locations through damage, free-standing structures' rocking response results in diminished structural damage, owing to the presence of negative post-uplift stiffness [2]. Apart from large-scale structures [3][4][5][6][7][8][9][10][11][12][13], non-structural elements within buildings may also uplift and rock [14][15][16] in response to floor accelerations [17,18].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for the Prediction of the Seismic Response of Rigid Rocking Blocks

Karampinis,

Bantilas,

Kavvadias

et al. 2023

Applied Sciences

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A variety of structural members and non-structural components, including bridge piers, museum artifacts, furniture, or electrical and mechanical equipment, can uplift and rock under ground motion excitations. Given the inherently non-linear nature of rocking behavior, employing machine learning algorithms to predict rocking response presents a notable challenge. In the present study, the performance of supervised ML algorithms in predicting the maximum seismic response of free-standing rigid blocks subjected to ground motion excitations is evaluated. As such, both regression and classification algorithms were developed and tested, aiming to model the finite rocking response and rocking overturn. From this point of view, it is essential to estimate the maximum rocking rotation and to efficiently classify its magnitude by successfully assigning respective labels. To this end, a dataset containing the response data of 1100 rigid blocks subjected to 15,000 ground motion excitations, was employed. The results showed high accuracy in both the classification (95% accuracy) and regression (coefficient of determination R2=0.89) tasks.

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