Ejup Hoxha
Papers
9
Total Citations
106
H-Index
5
About
Ejup Hoxha is a researcher specializing in robotic inspection systems, non-destructive evaluation (NDE), and infrastructure monitoring, with a particular focus on applying advanced sensing technologies and machine learning to civil engineering challenges. His work centers on automating the detection and characterization of subsurface features in both underground utilities and concrete structures, addressing critical safety and construction needs that traditional manual methods struggle to meet efficiently. Among his most significant contributions is the development of robotic systems that integrate Ground Penetrating Radar (GPR) for automated underground utility mapping and 3D subsurface reconstruction. His 2022 paper on robotic GPR inspection for construction surveys has garnered 26 citations, while his learning-based GPR processing framework and the GPRNet model reconstruction system have collectively demonstrated how deep learning can dramatically improve subsurface imaging accuracy. Beyond GPR, Hoxha has pioneered multi-modal inspection approaches combining impact-echo and impact-sounding techniques with robotics to detect internal concrete defects, work that has attracted growing scholarly attention. With a cumulative citation count exceeding 100 across his publications, Hoxha's research is making a meaningful impact on how engineers approach infrastructure inspection, pushing the field toward safer, more reliable, and highly automated solutions for assessing aging and critical built environments.
Research Focus
Key Achievements
Top Papers
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- 3GPR-based Model Reconstruction System for Underground Utilities Using GPRNet18 citations · 2021
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- 8Automatic Impact-sounding Acoustic Inspection of Concrete Structure2 citations · 2021
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