Ekin Ozer

Assistant Professor

Dublin City University

Ireland

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About

Dr. Ekin Ozer is an Assistant Professor at University College Dublin, Ireland. His research areas include generative artificial intelligence tools in education, structural health monitoring of bridges, and seismic risk assessment methodologies. Recent publications highlight his work on decision support systems for earthquake early warning and the use of mobile sensing for bridge network reliability. Dr. Ozer is committed to advancing understanding in these critical fields through innovative research and multidisciplinary collaboration.

Articles (14)

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2025

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2024

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2024

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2024

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2023

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (6)

JUAN JOSÉ GALIANA-MERINO

Professor

Universidad de Alicante

SPAIN

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Jawad Fayaz

Teesside University

UNITED KINGDOM

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Carmine Galasso

UNITED KINGDOM

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Enrico Tubaldi

Senior Lecturer

University Of Strathclyde

UNITED KINGDOM

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Rolands Kromanis

Assistant Professor

University of Twente

NETHERLANDS

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Sergio Molina-Palacios

Universidad de Alicante

SPAIN

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Ekin Ozer

Research Interests

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About

Articles (14)

AI-ENGAGE: A Multicentre Intervention to Support Teaching and Learning Engagement with Generative Artificial Intelligence Tools

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

Extracting Bridge Modal Frequencies Using Stationary Versus Drive-By Modes of Smartphone Measurements

Developing and evaluating a risk-informed decision support system for earthquake early warning at a railway bridge

Systemic reliability of bridge networks with mobile sensing-based model updating for postevent transportation decisions

Bridge modal analysis under Kernel clock distribution due to sampling jitter: Single-output identification

A Bayesian network‐based probabilistic framework for updating aftershock risk of bridges

Systemic reliability of bridge networks with mobile sensing-based model updating for postevent transportation decisions

Bridge Network Seismic Risk Assessment Using ShakeMap/HAZUS with Dynamic Traffic Modeling

A Bayesian network-based probabilistic framework for updating aftershock risk of bridges

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Rapid earthquake loss updating of spatially distributed systems via sampling-based Bayesian inference

Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference

Raspberry Shake-Based Rapid Structural Identification of Existing Buildings Subject to Earthquake Ground Motion: The Case Study of Bucharest

Collaborators (6)

JUAN JOSÉ GALIANA-MERINO

Jawad Fayaz

Carmine Galasso

Enrico Tubaldi

Rolands Kromanis

Sergio Molina-Palacios

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