O
Onur Avci
Researcher at Iowa State University
Publications - 101
Citations - 4244
Onur Avci is an academic researcher from Iowa State University. The author has contributed to research in topics: Serviceability (structure) & Structural health monitoring. The author has an hindex of 18, co-authored 80 publications receiving 1955 citations. Previous affiliations of Onur Avci include Virginia Tech & University of Leeds.
Papers
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Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
TL;DR: In this paper, a 1D convolutional neural network (CNN) was proposed to fuse feature extraction and classification blocks into a single and compact learning body for real-time structural damage detection.
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1D convolutional neural networks and applications: A survey
TL;DR: This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field.
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1D Convolutional Neural Networks and Applications: A Survey
TL;DR: A comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field, is presented in this paper, where the benchmark datasets and the principal 1D convolutional neural network software used in those applications are also publically shared in a dedicated website.
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A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications
TL;DR: This paper aims to fulfill the gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
Journal ArticleDOI
1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data
Osama Abdeljaber,Onur Avci,Mustafa Serkan Kiranyaz,Boualem Boashash,Boualem Boashash,Henry A. Sodano,Daniel J. Inman +6 more
TL;DR: This paper presents an enhanced CNN-based approach that requires only two measurement sets regardless of the size of the structure and successfully estimated the actual amount of damage for the nine damage scenarios of the benchmark study.