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Engineering and Technology

D-Index
42
Citations
6766
World Ranking
6589
National Ranking
1806

Overview

F. Necati Catbas is affiliated with the University of Central Florida in the United States and specializes in engineering, with a focus on civil and structural engineering. Their research extensively covers topics related to infrastructure maintenance and monitoring, structural health monitoring techniques, and concrete corrosion and durability. Additional areas of study include 3D surveying and cultural heritage, geophysical methods and applications, structural engineering and vibration analysis, as well as non-destructive testing techniques.

Catbas has contributed to a number of recent publications in various reputable venues. These include:

  • "A review of computer vision-based structural health monitoring at local and global levels" (2020), published in Structural Health Monitoring
  • "Swaying displacement measurement for structural monitoring using computer vision and an unmanned aerial vehicle" (2020), published in Measurement
  • "Attention-guided analysis of infrastructure damage with semi-supervised deep learning" (2021), published in Automation in Construction
  • "Generative adversarial networks for labeled acceleration data augmentation for structural damage detection" (2022), published in Journal of Civil Structural Health Monitoring
  • "CycleGAN for undamaged-to-damaged domain translation for structural health monitoring and damage detection" (2023), published in Mechanical Systems and Signal Processing

The frequent coauthors collaborating with Catbas include Furkan Luleci, Onur Avcı, Chuan-Zhi Dong, Selçuk Baş, and Enes Karaaslan.

Catbas has published repeatedly in several scientific venues, such as:

  • arXiv (Cornell University)
  • Frontiers in Built Environment
  • Sensors
  • Automation in Construction
  • Journal of Civil Structural Health Monitoring

The research profile of Catbas reflects a comprehensive engagement in engineering disciplines, particularly focusing on the integration of computer vision, deep learning, and data augmentation techniques for structural health monitoring and damage detection.

Best Publications

  • A review of computer vision–based structural health monitoring at local and global levels:

    Chuan-Zhi Dong;F Necati Catbas

  • Structural Health Monitoring and Reliability Estimation: Long Span Truss Bridge Application With Environmental Monitoring Data

    F. Necati Catbas;Melih Susoy;Dan M. Frangopol

  • Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications

    Mustafa Gul;F. Necati Catbas

  • CONDITION AND DAMAGE ASSESSMENT: ISSUES AND SOME PROMISING INDICES

    F. Necati Catbas;A. Emin Aktan

  • Completely contactless structural health monitoring of real‐life structures using cameras and computer vision

    Tung Khuc;F. Necati Catbas

  • Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering

    Mustafa Gul;F. Necati Catbas

  • Computer vision-based displacement and vibration monitoring without using physical target on structures

    Tung Khuc;F. Necati Catbas

  • Parameter Estimation for Multiple-Input Multiple-Output Modal Analysis of Large Structures

    F. Necati Catbas;David L. Brown;A. Emin Aktan

  • Use of Modal Flexibility for Damage Detection and Condition Assessment: Case Studies and Demonstrations on Large Structures

    F. Necati Catbas;F. Necati Catbas;F. Necati Catbas;David L. Brown;David L. Brown;David L. Brown;A. Emin Aktan;A. Emin Aktan;A. Emin Aktan

  • Structural displacement monitoring using deep learning-based full field optical flow methods

    Chuan Zhi Dong;Ozan Celik;F. Necati Catbas;Eugene J. O’Brien

  • Structural identification of constructed systems : approaches, methods, and technologies for effective practice of St-Id

    F. Necati Çatbaş;Tracy Kijewski-Correa;A. Emin Aktan

  • Marker-free monitoring of the grandstand structures and modal identification using computer vision methods:

    Chuan-Zhi Dong;Ozan Celik;F Necati Catbas

  • Ambient Vibration Data Analysis for Structural Identification and Global Condition Assessment

    Mustafa Gul;F. Necati Catbas

  • Automated finite element model updating of a scale bridge model using measured static and modal test data

    Masoud Sanayei;Ali Khaloo;Mustafa Gul;F. Necati Catbas

  • Practical identification of favorable time windows for infrared thermography for concrete bridge evaluation

    Azusa Watase;Recep Birgul;Recep Birgul;Shuhei Hiasa;Masato Matsumoto

  • Conceptual damage-sensitive features for structural health monitoring: Laboratory and field demonstrations

    F. Necati Catbas;Mustafa Gul;Jason L. Burkett

  • Damage Assessment with Ambient Vibration Data Using a Novel Time Series Analysis Methodology

    Mustafa Gul;F. Necati Catbas

  • Structural health monitoring using video stream, influence lines, and statistical analysis:

    Ricardo Zaurin;F. Necati Catbas

  • Structural Identification Using Computer Vision–Based Bridge Health Monitoring

    Tung Khuc;F. Necati Catbas

  • Nonparametric analysis of structural health monitoring data for identification and localization of changes: Concept, lab, and real-life studies:

    F Necati Catbas;Hasan B Gokce;Mustafa Gul

  • Sensor Networks, Computer Imaging, and Unit Influence Lines for Structural Health Monitoring: Case Study for Bridge Load Rating

    F. Necati Catbas;Ricardo Zaurin;Mustafa Gul;Hasan Burak Gokce

  • Attention-guided analysis of infrastructure damage with semi-supervised deep learning

    Enes Karaaslan;Ulas Bagci;F. Necati Catbas

Frequent Co-Authors

Dan M. Frangopol
Dan M. Frangopol Lehigh University
Eugene J. O'Brien
Eugene J. O'Brien University College Dublin
Ulas Bagci
Ulas Bagci Northwestern University
Naim Kapucu
Naim Kapucu University of Central Florida

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