Tushar Joshi
Papers
1
Total Citations
29
H-Index
1
About
Tushar Joshi’s research lies at the intersection of structural health monitoring, signal processing, and machine learning, with a particular focus on ensuring the safety and integrity of critical energy infrastructure. His most-cited work, “Application of wavelet analysis and machine learning on vibration data from gas pipelines for structural health monitoring” (2019, 29 citations), addresses a pressing industrial challenge: detecting corrosion and fatigue defects in natural gas pipelines. Rather than relying solely on traditional invasive methods like pipeline inspection gauges (PIGs), Joshi pioneered a non-intrusive approach that combines wavelet-based signal decomposition with machine learning classifiers to identify structural anomalies from vibration data. This work has been influential in advancing data-driven, cost-effective pipeline monitoring strategies. His contributions are especially relevant for researchers and engineers working on predictive maintenance and infrastructure resilience. With a growing citation footprint, Joshi is establishing himself as a key voice in applying computational intelligence to real-world civil and mechanical engineering problems. His research not only pushes the boundaries of non-destructive evaluation but also offers practical pathways toward safer, smarter energy transport systems.
Research Focus
Key Achievements
Top Papers
- 1