Sakib Ashraf Zargar

College of Engineering

Works (3)

Updated: January 29th, 2024 07:39

2020 journal article

Augmented reality for enhanced visual inspection through knowledge-based deep learning

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 20(1), 426–442.

By: S. Wang n, S. Zargar n & F. Yuan n

author keywords: Enhanced visual inspection; augmented reality; knowledge-based learning; structural health monitoring; automated damage detection; deep learning; object detection; image segmentation; convolutional neural networks
TL;DR: A two-stage knowledge-based deep learning algorithm is presented for enabling automated damage detection in real-time using the augmented reality smart glasses and the more challenging task of defect detection in a multi-joint bolted region is addressed. (via Semantic Scholar)
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Added: February 15, 2021

2020 review

Impact diagnosis in stiffened structural panels using a deep learning approach

[Review of ]. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 20(2), 681–691.

By: S. Zargar n & F. Yuan n

author keywords: Impact diagnosis; physics-inspired deep learning; full wavefield analysis; spatio-temporal information extraction; convolutional neural networks; recurrent neural networks; real-time impact monitoring system; highspeed camera; computer-vision
TL;DR: An approach for the autonomous analysis of wavefields for impact diagnosis, that is, identifying the impact location and reconstructing the impact force time-history by incorporating the physics-based concept of time-reversal in the recurrent part of the network. (via Semantic Scholar)
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Added: July 6, 2020

2020 article

Machine Learning for Structural Health Monitoring: Challenges and Opportunities

SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2020, Vol. 11379.

By: F. Yuan n, S. Zargar n, Q. Chen  n & S. Wang n

author keywords: Machine learning; artificial neural networks; physics-informed learning; visual inspection; augmented reality; impact diagnosis; damage diagnosis; structural health monitoring
TL;DR: As a step towards the goal of automated damage detection, preliminary results are presented from dynamic modelling of beam structures using physics-informed artificial neural networks and a sensing paradigm for non-contact full-field measurements for damage diagnosis is presented. (via Semantic Scholar)
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Added: December 11, 2020

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