In this paper, the transient dynamic response of shear type multi-storey buildings subjected to earthquake ground motion is generated via adversarial learning technique under different damage conditions, starting from the relevant undamaged responses. A Representation Generative Adversarial Network (RepGAN) is trained on a database of synthetic accelerograms to obtain the responses of the buildings in their undamaged state and in case of plausible damage patterns. Each structural response, represented by a set of time histories to catch the lateral storey displacements/accelerations, is encoded to learn its hidden features and infer the associated damage class. By re-sampling the encoded latent space, it is shown how to switch from the undamaged to the damaged class and to decode the damaged response. The proposed methodology enables damage classification in shear-type multi-storey buildings proving that it can successfully detect damage and assess two different damage severity levels whenever the time-history of a sufficient number of floors is monitored. To outline the generalization capability of the proposed approach, the signal reconstruction is quantitatively assessed for all damage conditions and even in case of a damage condition different from the one corresponding to the encoded signal.

Multi-storey shear type buildings under earthquake loading: Adversarial learning-based prediction of the transient dynamics and damage classification

Rosafalco L.;Colombera G.;Mariani S.;Corigliano A.
2023-01-01

Abstract

In this paper, the transient dynamic response of shear type multi-storey buildings subjected to earthquake ground motion is generated via adversarial learning technique under different damage conditions, starting from the relevant undamaged responses. A Representation Generative Adversarial Network (RepGAN) is trained on a database of synthetic accelerograms to obtain the responses of the buildings in their undamaged state and in case of plausible damage patterns. Each structural response, represented by a set of time histories to catch the lateral storey displacements/accelerations, is encoded to learn its hidden features and infer the associated damage class. By re-sampling the encoded latent space, it is shown how to switch from the undamaged to the damaged class and to decode the damaged response. The proposed methodology enables damage classification in shear-type multi-storey buildings proving that it can successfully detect damage and assess two different damage severity levels whenever the time-history of a sufficient number of floors is monitored. To outline the generalization capability of the proposed approach, the signal reconstruction is quantitatively assessed for all damage conditions and even in case of a damage condition different from the one corresponding to the encoded signal.
2023
Damage identification
Deep learning
Earthquake engineering
Generative adversarial network
Structural dynamics
Structural health monitoring
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1248357
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