The effective evaluation of compaction quality is a key issue for the safety of earth-rock dams. However, existing prediction models of compaction quality are designed to improve prediction accuracy but generally ignore generalizability and robustness, resulting in deviations from practical evaluation results, making these models inapplicable to complex construction environments. To address these problems, a novel real-time evaluation model for construction unit compaction quality based on random forest optimized by adaptive chaos grey wolf algorithm (RF-ACGWO) is proposed in this article. In RF-ACGWO, RF predicts compaction quality, while ACGWO increases efficiency and accuracy for traditional RF parameter selection and improves the generalizability and robustness of the model. Also, meteorological factors at a project site are also considered to affect the model, thereby improving model accuracy. After embedding the proposed method in a Three-Dimensions (3D) rolling monitoring system, real-time evaluation, guidance and feedback on a project site can be obtained. Compared to the conventional evaluation methods, RF-ACGWO achieves the highest accuracy of 0.838, the best generalizability of 0.793 and the most stable robustness when applied to a large-scale, real-life hydraulic engineering project.
Real-time evaluation of compaction quality based on RF- ACGWO with high robustness and generalization ability
