Doctoral Student Combines Artificial Intelligence and Geotechnical Engineering in Award Winning Paper
When there is a database full of useful information, but it is too complicated to use practically, how can someone sort through the numbers to gain valuable insights? Even practicing civil engineers have trouble accessing and interpreting the enormous data sets they may need for a project. This is the issue NYU Tandon Ph.D. candidate, Nikolaos Machairas, along with co-authors, Gregory A. Highley and Tandon Professor Magued G. Iskander, sought to resolve in their paper “Evaluation of FHWA Pile Design Method Against the Deep Foundation Load Test Database.” The paper was recently reviewed by the Transportation Research Board’s Committee AFS30 (Foundations of Bridges and Other Structures) and was ranked highest amongst papers received this year, with TRB reviewers noting that it was the only paper designated as both award-caliber, and practice-ready. 
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