Application of Probabilistic Neural Networks for Prediction of Concrete Strength

The compressive strength of concrete is a commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time consuming. This study presents the probabilistic technique for predicting the compressive strength of concrete on the basis of concrete mix proportions. The estimation of the strength is performed using the probabilistic neural network which is an effective tool for the pattern classification problem and provides a probabilistic viewpoint as well as a deterministic classification result.Application of probabilistic neural networks in the compressive strength estimation of concrete is performed using the mix proportion data and test results of two concrete companies. It has been found that the present methods are very efficient and reasonable in predicting the compressive strength of concrete probabilistically.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/08991561
• Authors:
  • Kim, Doo Kie
  • Lee, Jong Jae
  • Lee, Jong Jae
  • Chang, Seong Kyu
• Publication Date: 2005-6

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 353-362
• Serial:
  • Journal of Materials in Civil Engineering
  • Volume: 17
  • Issue Number: 3
  • Publisher: American Society of Civil Engineers
  • ISSN: 0899-1561
  • EISSN: 1943-5533
  • Serial URL: http://ascelibrary.org/journal/jmcee7

Subject/Index Terms

• TRT Terms: Compressive strength; Concrete; Durability; Forecasting; Neural networks; Probability theory; Strength of materials
• Uncontrolled Terms: Probabilistic neural networks
• Subject Areas: Data and Information Technology; Highways; Materials; I32: Concrete;

Filing Info

• Accession Number: 01000909
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 2 2005 2:24PM