Collection
Special Issue: Artificial Intelligence, Sensing and Big Data Analytics in Earthquake Engineering
The digital revolution has brought all scientific disciplines into a data-driven world where digitization is becoming a must for keeping up the pace. Due to the need of processing large amounts of data and the complex methods required to address inherent uncertainties, earthquake engineering field can more easily adapt to this fast transformation than more classical disciplines of engineering. In order to take the lead in this transformation, Guest Editors are proposing a Special Issue to Bulletin of Earthquake Engineering on topics related to the emerging technologies such as artificial intelligence, sensing and big data analytics in the field of earthquake engineering.
Editors
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Eleni Smyrou
Hanze University of Applied Sciences Groningen NoorderRuimte, Centre of Research & Innovation for Built Environment Zernikeplein 7 9704 AA Groningen, The Netherlands e-mail: e.smyrou@pl.hanze.nl
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Ihsan Engin Bal
Hanze University of Applied Sciences Groningen Van OlstToren Zernikeplein 7 9747 AS Groningen, the Netherlands
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Vasilis Sarhosis
School of Civil Engineering University of Leeds Room 4.10, Civil Engineering, Weetwood Lane, LS2 9JT Leeds, UK
Articles (11 in this collection)
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Seismic response of bridges employing knowledge-enhanced neural networks for the lumped plasticity modelling of RC piers
Authors (first, second and last of 4)
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Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions
Authors (first, second and last of 10)
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Comparison between Bayesian updating and approximate Bayesian computation for model identification of masonry towers through dynamic data
Authors (first, second and last of 4)
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Generative adversarial networks review in earthquake-related engineering fields
Authors (first, second and last of 4)
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A dual Kriging-XGBoost model for reconstructing building seismic responses using strong motion data
Authors
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Evolutionary numerical model for cultural heritage structures via genetic algorithms: a case study in central Italy
Authors (first, second and last of 5)
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