Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

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Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches (2019, methodology): “Such algorithms were extended and applied to many civil engineering applications including building materials, concrete, or mortar [24,25].”
A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement (2020, methodology): “This mathematical process can be formulated as follows [21, 35]:”
Machine learning in concrete strength simulations: Multi-nation data analytics (2014, methodology): “Cement materials such as fly ash, blast furnace slag, metakaolin, and silica fume are often used to increase the compressive strength and durability of HPC [3–5].”
Emerging artificial intelligence methods in structural engineering (2018, methodology): “[202] used a neural network to predict the compressive strength of selfcompacting and high performance concrete.”