Neural networks for predicting compressive strength of structural light weight concrete

By Marai M. Alshihri, A. Azmy, M. S. El-Bisy. 2009

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Computational design optimization of concrete mixtures: A review (2018, methodology): “Since then, researchers have applied ANN methods to wide-ranging problems in cement and concrete research; they have been used to model several types of concrete properties, including compressive strength, slump, filling capacity, and segregation and for many types of concrete including high perform”
Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes (2019, methodology): “Additionally, the network will obtain new input patterns to generate the matching output and mapping.(31) Among all the parameters affecting the eventual structure of BPNN, the connection weights and bias values, the number of hidden layers, and the number of neurons in each layer have the direct ef”
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): “[20] used an ANN model in the compressive strength prediction of lightweight concrete and concluded that using ANN can reduce cost and save time.”