Closure to "Shear Compression Failure in Reinforced Concrete Deep Beams" by Prodromos D. Zararis

In this brief article, Zararis replies to a discussion article on his research in which the shear failure of reinforced concrete deep beams under two-point or a single-point loading, with a shear span to effective depth ratio (a/d) between 1.0 and 2.5, is due to a crushing of concrete in a compression zone with a restricted depth above the tip of the critical diagonal crack. In the original study, simple expressions are derived for the restricted depth of the compression zone, as well as for ultimate shear force of deep beams with and without web reinforcement. In the discussion article, Rajasekaran and Nalinaa (June 2005) make the case for the use of a multilayer feed-forward artificial neural network to predict the ultimate shear strength of reinforced concrete deep beams. The discussion authors covered three types of problems: deep beams without shear reinforcement; deep beams with web reinforcement; and deep beams with both horizontal and vertical web reinforcement. They focus on the use of three types of sequential learning neural network (SLNN) architectures. Zararis agrees with the commentary authors that SLNN is an appropriate and useful method for finding the shear capacity of reinforced concrete deep beams, but contends that SLNN methods cannot go as deeply into the matter of the subject as analytical methods.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/07339445
• Authors:
  • Zararis, Prodromos D
• Publication Date: 2005-6

Language

• English

Media Info

• Media Type: Print
• Features: References;
• Pagination: p 991
• Serial:
  • Journal of Structural Engineering
  • Volume: 131
  • Issue Number: 6
  • Publisher: American Society of Civil Engineers
  • ISSN: 0733-9445
  • Serial URL: http://ascelibrary.org/loi/jsendh

Subject/Index Terms

• TRT Terms: Beams; Compression; Concrete structures; Failure analysis; Neural networks; Reinforced concrete; Shear properties; Shear strain; Shear strength; Structural engineering; Structural members; Web stiffened structures
• Uncontrolled Terms: Sequential learning neural networks
• Subject Areas: Bridges and other structures; Design; Highways; Materials; I24: Design of Bridges and Retaining Walls; I32: Concrete;

Filing Info

• Accession Number: 01001852
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 5 2005 1:33PM