B
Barbaros Cetiner
Researcher at University of California, Los Angeles
Publications - 12
Citations - 277
Barbaros Cetiner is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Resilience (network) & Deep learning. The author has an hindex of 4, co-authored 12 publications receiving 56 citations.
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Real‐time regional seismic damage assessment framework based on long short‐term memory neural network
TL;DR: A framework for real‐time regional seismic damage assessment that is based on a Long Short‐Term Memory (LSTM) neural network architecture is proposed and can perform damage assessment in real time at regional scale with high prediction accuracy and acceptable variance.
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A deep learning approach to rapid regional post‐event seismic damage assessment using time‐frequency distributions of ground motions
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Data-Driven Approach to Predict the Plastic Hinge Length of Reinforced Concrete Columns and Its Application
TL;DR: Inelastic response of reinforced concrete columns to combined axial and flexural loading is characterized by plastic deformations localized in small regions, which are idealized as plastic... as mentioned in this paper.
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Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management
Chaofeng Wang,Qian Yu,Qian Yu,Kincho H. Law,Frank McKenna,Stella X. Yu,Stella X. Yu,Ertugrul Taciroglu,Ádám Zsarnóczay,Wael Elhaddad,Barbaros Cetiner +10 more
TL;DR: A framework for regional scale building information generation/gathering to support regional hazard analysis is presented and a novel data mining tool is developed to overcome the data scarcity issue, quantify the uncertainty and enrich the data repository.
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Rapid visual screening of soft-story buildings from street view images using deep learning classification
Qian Yu,Chaofeng Wang,Frank McKenna,Stella X. Yu,Ertugrul Taciroglu,Barbaros Cetiner,Kincho H. Law +6 more
TL;DR: An automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale based on a database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model.