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

International audienceIn 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

Generatives models approach on TFM data based on variational inference for multi-fidelity data generation

International audienceThe present work evaluates the translation from a numerical input into a higher fidelity output by transferring experimental style to simulated data. The hybrid and experimental data similarities are measured to validate the newly generated images. Future work aims to use the hybrid data to enhance the data generation to be used for training classification and regression models in view of decision support systems

Generative model approach on TFM data based on variational inference for muti-fidelity data generation

International audienc

Generatives models approach on TFM data based on variational inference for multi-fidelity data generation

International audienceThe present work evaluates the translation from a numerical input into a higher fidelity output by transferring experimental style to simulated data. The hybrid and experimental data similarities are measured to validate the newly generated images. Future work aims to use the hybrid data to enhance the data generation to be used for training classification and regression models in view of decision support systems

Generative model approach on TFM data based on variational inference for muti-fidelity data generation

International audienc

A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring

Civil structures, infrastructures and lifelines are constantly threatened by natural hazards and climate change. Structural Health Monitoring (SHM) has therefore become an active field of research in view of online structural damage detection and long term maintenance planning. In this work, we propose a new SHM approach leveraging a deep Generative Adversarial Network (GAN), trained on synthetic time histories representing the structural responses of both damaged and undamaged multistory building to earthquake ground motion. In the prediction phase, the GAN generates plausible signals for different damage states, based only on undamaged recorded or simulated structural responses, thus without the need to rely upon real recordings linked to damaged conditions

A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring

International audienceCivil structures, infrastructures and lifelines are constantly threatened by natural 1 hazards and climate change. Structural Health Monitoring (SHM) has therefore become an 2 active field of research in view of online structural damage detection and long term maintenance 3 planning. In this work we propose a new SHM approach leveraging a deep Generative Adversarial 4 Network (GAN), trained on synthetic time histories representing the structural responses of both 5 damaged and undamaged multistory building to earthquake ground motion. In the prediction 6 phase, the GAN generates plausible signals for different damage states, based only on undamaged 7 recorded or simulated structural responses, thus without the need to rely upon real recordings 8 linked to damaged conditions