Machine learning design of R/C sections revisited

This paper revisits our recent work on rapid and accurate design of reinforced concrete (R/C) columns and bridge piers using Artificial Neural Networks (ANNs). Both rectangular and circular, solid and hollow sections are treated. The new functions for rectangular sections now accommodate a much greater aspect ratio, making them suitable for all sections typically used for bridge piers, without sacrificing performance. For the first time, to the best of our knowledge, new design functions for Tbeams and singly- reinforced rectangular beams are also derived. The error estimation is presented in detail using extremely extensive test sets, while auxiliary ANNs are employed to screen out improper data input. All design functions are sufficiently accurate, unconditionally stable, and orders of magnitude faster than any iterative section analysis procedure. The forward feed of the final ANNs has been translated into optimized code in all popular programming languages, which can be easily used without the need of specialized software, even on a spreadsheet.