Peyman Aela

Papers

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About

Peyman Aela is a leading researcher in railway infrastructure engineering, with a primary focus on the application of computer vision and artificial intelligence for structural health monitoring. His work centers on developing non-contact, vision-based methods to assess and maintain railway superstructures, including tracks, fasteners, and ballast. His most-cited paper, "Vision-based monitoring of railway superstructure: A review" (2024, 38 citations), provides a comprehensive synthesis of state-of-the-art techniques, establishing a critical framework for automating inspection processes and improving safety in rail networks. Aela’s contributions are pivotal in shifting traditional manual inspection toward data-driven, real-time monitoring systems, addressing key challenges in defect detection and predictive maintenance. His research has immediate implications for reducing operational costs and enhancing the reliability of transportation infrastructure. With a growing citation record and a focus on bridging computer vision with civil engineering, Aela is recognized as an emerging authority in smart infrastructure, whose work is shaping the future of resilient and intelligent railway systems.