Frost resistance prediction of recycled aggregate concrete based on supervised algorithms

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The frost resistance of recycled aggregate concrete (RAC) is a crucial performance indicator in its engineering application. Considering the complexity of factors that affect the frost resistance of RAC, in this study five neural network algorithms were employed, back-propagation neural network, radial basis function network, convolutional neural network, support vector machine and random forest regressor, along with two optimisations methods, particle swarm optimisation-back-propagation (PSO-BPNN) and genetic algorithm-back-propagation to predict the frost resistance of RAC. A database was compiled from 616 mixes in the peer-reviewed literature, and eight variables affecting the freeze–thaw resistance of RAC were taken as inputs. The compressive strength, mass loss rate and relative dynamic elastic modulus after freeze–thaw cycles were taken as outputs. The results showed that the main factors affecting the freezing resistance of RAC were the number of freeze–thaw cycles, the replacement rate of recycled aggregates, crushing index and water–cement ratio. Among the seven algorithms, the PSO-BPNN model had the best comprehensive prediction performance, with R2 of predicted compressive strength reaching 0.9714. It provides a reference value for further research on RAC frost resistance.