S
Shuai Li
Researcher at University of Tennessee
Publications - 79
Citations - 1582
Shuai Li is an academic researcher from University of Tennessee. The author has contributed to research in topics: Computer science & Ground-penetrating radar. The author has an hindex of 18, co-authored 68 publications receiving 831 citations. Previous affiliations of Shuai Li include University of Southern California & Purdue University.
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Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawings
TL;DR: A semi-automatic approach is developed to establish a systematic, accurate and convenient digital twinning system based on images and CAD drawings based on neuro-fuzzy system and elaborates on the methodological framework.
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Civil Engineering Grand Challenges: Opportunities for Data Sensing, Information Analysis, and Knowledge Discovery
Burcin Becerik-Gerber,Mohsin K. Siddiqui,Ioannis Brilakis,Omar El-Anwar,Nora El-Gohary,Tarek Mahfouz,Gauri M. Jog,Shuai Li,Amr Kandil +8 more
TL;DR: In this paper, the authors present an exploratory analysis to identify civil engineering challenges that can be addressed with further data sensing and analysis (DSA) research and evidence of economic, environmental, and societal impacts of these challenges is presented through a review of the literature.
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Uncertainty-aware geospatial system for mapping and visualizing underground utilities
TL;DR: The geospatial 3G system, together with the proposed 3D probabilistic uncertainty bands, enables the mapping and visualization of some underground utilities in a non-destructive and uncertainty-aware manner to avoid disastrous utility strikes and promote safe excavations.
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Integrated Processing of Image and GPR Data for Automated Pothole Detection
TL;DR: This paper proposes a method to integrate the processing of two-dimensional images and GPR data to automate accurate and efficient pothole detection that was designed by investigating the patterns of GPR signals reflected by poth...
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Automatic Pavement-Crack Detection and Segmentation Based on Steerable Matched Filtering and an Active Contour Model
TL;DR: This data indicates that pavement cracks are an important symptom of pavement deterioration and deficiency and accurate and complete information regarding pavement cracks is critical to determining pavement maintenance schedul...