University of British Columbia
2019–2025
Mohsen Azimi
Smart Structures @ UBC
My main research interest is in devising machine learning methods and computer vision for Structural Health Monitoring.
Research Interests
Education & Experience
Georgia Institute of Technology
2022–2025
Quantic School of Business and Technology
2020–2020
University of Nevada, Reno
2017–2017
North Dakota State University
2014–2014
Iran University of Science and Technology
2009–2011
University of Mohaghegh Ardabili
2005–2009
Research Interests
Machine Learning
Application of neural networks and deep learning architectures for structural analysis and monitoring.
Computer Vision
Implementing computer vision techniques for visual inspection and structural damage detection.
Structural Health Monitoring
Developing intelligent systems for real-time monitoring of civil infrastructure.
Recent Publications
Deep Learning
Transformer-based framework for accurate segmentation of high-resolution images in structural health monitoring
High-resolution image segmentation is essential in structural health monitoring (SHM), enabling accurate detection and quantification of structural components and damages.
Deep Learning
Volumetric Defect Classification in Nanoresolution X-ray Computed Tomography Images of Laser Powder Bed Fusion via Deep Learning
Additively manufactured components often contain volumetric defects that significantly impact mechanical and fatigue properties across various material systems.
Feature Selection
Structural health monitoring via a group-theoretic WSA for optimal feature selection and data fusion
In this study, two binary versions of the Water Strider Algorithm (WSA) are proposed and applied to optimal feature selection in classification problems.
Framework Development
An open-source framework for the FE modeling and optimal design of fiber-steered variable-stiffness composite cylinders
In this article, an open-source ABAQUS/MATLAB-based framework is developed for the bending-induced buckling design of variable-stiffness (VS) composite cylinders.
Deep Learning
Structural health monitoring using extremely compressed data through deep learning
This study introduces a novel convolutional neural network (CNN)‐based approach for structural health monitoring (SHM) that exploits a form of measured compressed response data.
Swarm Intelligence
Swarm-Based Parallel Control of Adjacent Irregular Buildings Considering Soil–Structure Interaction
Inspired by swarm intelligence in nature, a new control method, known as swarm-based parallel control (SPC), is proposed in this study.