Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network

By Cahit Bilim, Cengiz Duran Atis, Harun Tanyıldızı, Okan Karahan. Advances in Engineering Software, 2009

331 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/3008532141b1773cc5a46597ff95661f1cd785b0

How researchers see this paper

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

In Civil and Structural Engineering

1. Denvid Lau
2. Binh Thai Pham
3. L. Feo
4. C. Chow
5. Thuy-Anh Nguyen
6. Hai-Van Thi Mai
7. A. Al-Mansour
8. Adriana Dantas

Citations in the corpus

• Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks (2013, background): “Several studies [10–24] have used artificial intelligence techniques for predicting concrete properties successfully.”
• Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model (2021, methodology): “[33] ANN model 6 inputs: C, GGBFS, W, SP, Agg.”
• Green Concrete: By-Products Utilization and Advanced Approaches (2019, background): “Notes: The data in the first three rows and the last row are taken from the references [58,59] respectively.”