Shweta Dabetwar
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant professor
Also affiliated: Texas Tech University (2016–2024); University of Massachusetts Lowell (2022–2023); University of Massachusetts System (2022); University of Massachusetts Boston (2022)
Faculty Researcher
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Shweta Dabetwar's research focuses on developing and applying advanced sensing and data analysis techniques for structural health monitoring and prognostics. Her work includes systematic reviews of noncontact sensing methods for AI-aided structural health assessment and comparative analyses of infrared thermography processing for detecting sub-pavement voids. Dabetwar also investigates the sensitivity of unmanned aerial vehicle (UAV)-borne 3D point cloud reconstruction from infrared images and evaluates deep learning algorithms for classifying heat loss damage in buildings using UAV-borne infrared data. Her publications also address fatigue damage diagnostics in composites through data fusion and data augmentation with deep neural networks, and the prognostics and health management of wind energy infrastructure systems. Additionally, she has developed a framework for estimating the remaining useful life of Li-ion batteries under limited data conditions and a sensor-based calibration system for three-dimensional digital image correlation.
Metrics
- h-index: 11
- Publications: 28
- Citations: 332
Selected Publications
-
A Study of the Effect of Unbalanced Dataset and Pretraining on the Multiclass Classification of Brain Tumors Using MRI Scans (2025)
-
Optimization of Experimental Infrared Image Dataset for the Identification of Barely Visible Damage in Wind Turbine Blade Samples Using Transfer Learning (2025)
-
Identification and Localization of Areas of Damage in Composite Materials Using Infrared Thermography and Artificial Intelligence (2024)
-
Classification of Damage State in Carbon Fiber Reinforced Composites Using Transfer Learning (2024)
-
A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier (2024)
Collaboration Network
Top Collaborators
- Classification of Damage State in Carbon Fiber Reinforced Composites Using Transfer Learning
- Optimization of Experimental Infrared Image Dataset for the Identification of Barely Visible Damage in Wind Turbine Blade Samples Using Transfer Learning
- Identification and Localization of Areas of Damage in Composite Materials Using Infrared Thermography and Artificial Intelligence
- Optimization of Experimental Infrared Image Dataset for the Identification of Barely Visible Damage in Wind Turbine Blade Samples Using Transfer Learning
- A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier
- A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier
- A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier
- A Study of the Effect of Unbalanced Dataset and Pretraining on the Multiclass Classification of Brain Tumors Using MRI Scans
Similar Researchers
Based on overlapping research topics