Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic

By Mustafa Sarıdemir, Ilker Bekir Topcu, Fatih Özcan, M. H. Severcan. 2009

152 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/22332c9efb47a240a62c66fc449f92a3bb597ebe

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. Adriana Dantas
2. K. D. J. Nagahama
3. M. B. Leite

Citations in the corpus

• 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”
• Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model (2021, unknown): “[38] ANN and fuzzy logic models ANFIS 5 inputs: TA, C, GGBFS, W, and Agg.”