Prediction of FRP-confined compressive strength of concrete using artificial neural networks

By Hosein Naderpour, Ali Kheyroddin, G. Amiri. 2010

277 citations recorded by the source corpus.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/b07f44cc5ad79093b5daedad8893c08b4c9ccece

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Masoud Ahmadi
2. Ali Kheyroddin
3. Hosein Naderpour

In Civil and Structural Engineering

1. Adriana Dantas
2. Masoud Ahmadi
3. K. D. J. Nagahama
4. M. B. Leite
5. Ali Kheyroddin
6. Hosein Naderpour

Citations in the corpus

• Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load (2014, background): “[26] H.”
• Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks (2013, background): “Some studies have been published, showing that ANN can model complex and nonlinear relationships between parameters affecting the compressive strength of concrete [10–17], the compressive strength of high strength concrete [18], the compressive strength”