Work

Optimization of Gaussian Process Regression with Intelligent Algorithms for Predicting Compacted Density of Gravel-Soil Materials

Year: 2025

Type: article

Abstract: To effectively control the deformation of high concrete face rockfill dams, this study proposes an intelligent prediction model (CO–SA–GPR) that integrates the Cheetah Optimizer (CO) and Simulated Annealing (SA) algorithm to optimize Gaussian Process Regression (GPR) for accurately estimating the compaction density of sandy gravel materials. Firstly, a theoretical derivation of the specification-similar gradation scaling method was conducted, clarifying the relationship between gradation parameters before and after scaling. On this basis, the CO and SA algorithms were employed to adaptively optimize the hyperparameters of the GPR model, obtaining the global optimal solution through intelligent search, thereby enhancing the model’s prediction accuracy and robustness. Application of the established model to actual engineering predictions shows that in estimating the maximum and minimum dry densities, the CO–SA–GPR model achieves R2 values as high as 0.9752 and 0.9741, with RMSE as low as 0.0022 and 0.0028, respectively, significantly outperforming comparative models. The proposed model enables accurate prediction of compaction density from laboratory scaled-down tests to prototype gradations, providing a reliable new method for quality control in high rockfill dam construction and offering important theoretical and technical reference values for similar coarse-grained soil engineering.

Source: Buildings

DOI: 10.3390/buildings15213910

Authors: Haijuan Wang, Jiang Li, Yufei Zhao, Biao Liu

Institutions: Shihezi University, China Institute of Water Resources and Hydropower Research

Language: English

Topic: Dam Engineering and Safety

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137