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About

Diar Sanakov is an emerging researcher specializing in non-destructive evaluation (NDE), robotic inspection systems, and subsurface defect detection for civil infrastructure. His work sits at the intersection of advanced sensing technologies and intelligent data processing, with a particular focus on Ground Penetrating Radar (GPR) and acoustic-based inspection methods such as impact-echo and impact-sounding. Sanakov's most notable contribution is the development of GPRNet, a deep learning-based reconstruction system capable of processing sparse GPR measurements to model underground utilities — a significant advancement over prior approaches limited to image-based feature detection alone. This work, accumulating 20 citations across two publication iterations, addresses a critical gap in utility localization and infrastructure mapping. Equally impactful is his research on robotic inspection platforms that integrate multiple NDE modalities, enabling automated, large-scale characterization of subsurface defects in concrete structures — work that has garnered 18 citations and pushes the field beyond the limitations of single-sensor approaches. With a total citation count approaching 45, Sanakov's research is gaining meaningful traction within the infrastructure inspection and robotics communities. His efforts to automate labor-intensive inspection workflows hold strong implications for improving the safety, efficiency, and sustainability of aging civil infrastructure worldwide.