Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic

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A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement (2020, background): “This is mainly due to the fact that many predictable or unpredictable factors may significantly affect the properties of concrete [1, 2].”
Machine learning in concrete strength simulations: Multi-nation data analytics (2014, methodology): “Other material science problems that have been solved by ML include mixture design, predicting mechanical properties, or fault diagnosis [10,14,16–19].”
Optimum shape design of arch dams for earthquake loading using a fuzzy inference system and wavelet neural networks (2009, methodology): “The process of FIS involves some concepts: fuzzy sets; membership functions; logical operations; and if–then rules (Topcu and Sarıdemir 2008).”
Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches (2019, unknown): “Such algorithms were extended and applied to many civil engineering applications including building materials, concrete, or mortar [24,25].”