Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks

By Adriana Dantas, M. B. Leite, K. D. J. Nagahama. 2013

301 citations recorded by the source corpus.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/13824c3776de66fc2eb01766680ac4e2d667c0cd

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. Hadi Salehi
2. Rigoberto Burgueño
3. Masoud Ahmadi
4. Ali Kheyroddin
5. Hosein Naderpour

In Civil and Structural Engineering

1. Hadi Salehi
2. Rigoberto Burgueño
3. Masoud Ahmadi
4. Ali Kheyroddin
5. 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): “Nowadays, the artificial neural networks applied to different fields of civil engineering such as analysis and design [24–29].”
• Emerging artificial intelligence methods in structural engineering (2018, background): “[226,227] Prediction of concrete strength Concrete with construction and demolishing waste ANN”