Lazzat Kurmangaliyeva
Papers
1
Total Citations
3
H-Index
1
About
Dr. Lazzat Kurmangaliyeva is a researcher advancing the field of automated infrastructure inspection, with a primary focus on intelligent defect detection and classification systems for pipelines. Her most-cited work, "Development of in-pipe defects detection and classification system" (2025), directly addresses the critical inefficiencies of traditional manual inspection methods, which are notoriously time-consuming, costly, and susceptible to human error. By developing a novel automated system, Dr. Kurmangaliyeva’s research aims to significantly enhance the accuracy, speed, and reliability of identifying structural flaws within pipelines. This contribution is vital for preventing catastrophic failures and reducing maintenance costs in industries ranging from oil and gas to municipal water systems. Though early in its citation trajectory, this work has already garnered 3 citations, signaling growing interest from peers in non-destructive testing and computer vision. Her research bridges the gap between practical engineering challenges and cutting-edge machine learning applications, positioning her as an emerging authority in smart infrastructure monitoring. Dr. Kurmangaliyeva’s dedication to solving real-world industrial problems through technological innovation marks her as a promising talent in the field of automated inspection systems.
Research Focus
Key Achievements
Top Papers
- 1Development of in-pipe defects detection and classification system3 citations · 2025