Intelligent compaction quality assessment of earth-rock dams considering small samples uncertainty

In light of the poor real-time performance，low accuracy，weak generalization，insufficient training data and susceptible to the external changes in compaction quality assessment of earth-rock dams. This paper proposes a compaction quality assessment model based on the binary muti-population genetic algorithm fused back propagation neural network. This model addresses the problem of small sample learning in the intelligent compaction assessment of rockfill materials on-site by adjusting the transfer activation function，improving the migration updating mechanism，and constructing a training loss function considering the epistemic uncertainty and aleatoric uncertainty. The results show that the assessment performance of the proposed model is better than 9 comparison models，and the combinatorial uncertainty can improve the generalization and data error tolerance of the model，which features generalizability and application value in other engineering scenarios. © 2024 Editorial Board of Jilin University. All rights reserved.