Papers
1
Total Citations
12
H-Index
1
About
Junbo Xi is a researcher whose work sits at the intersection of civil engineering and artificial intelligence, with a primary focus on automating construction quality inspection. His most notable contribution is the development of a vision-based deep learning framework combined with computational geometry for the automatic spacing inspection of rebar spacers on reinforcement skeletons. This work, published in 2023, has already garnered 12 citations, signaling its immediate relevance to the construction industry’s push toward digitalization and quality control. By replacing manual, error-prone inspection methods with a precise, automated system, Xi addresses a critical need in reinforced concrete construction—ensuring structural integrity while reducing labor costs and human error. His research demonstrates a practical application of computer vision and geometric algorithms to solve real-world engineering challenges, bridging the gap between theoretical AI advances and on-site construction demands. Xi’s work is particularly valuable for researchers and practitioners exploring smart construction technologies, offering a scalable solution that could be extended to other inspection tasks in civil infrastructure.
Research Focus
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Top Papers
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