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.

In the long-term operation of canals in loess areas, instability and landslides frequently occur due to the effect of wetting–drying cycles, which severely restricts the long-term safe operation of engineering projects. To reveal the evolution law of loess strength under wetting–drying cycles and establish a strength prediction model, this study conducted wetting–drying cycle tests and direct shear tests, analyzing the effects of different cycle times, dry densities, and initial water contents on the shear strength and its parameters. A combined model of improved adaptive genetic algorithm and backpropagation neural network (IAGA-BP) was adopted for shear strength prediction. An adaptive crossover and mutation operator based on the Sigmoid function, which combines the fitness value with the population iteration number, was proposed. By optimizing the parent selection strategy and the uniform crossover genetic method, the population diversity was effectively maintained, and premature convergence was avoided. The test results show that with the increase in the wetting–drying cycle times, both the shear strength and strength parameters of loess exhibit a trend of gradual attenuation and eventually tend to be stable. The increase in the dry density and initial water content can reduce the degradation amplitude of soil cohesion after five wetting–drying cycles. The model verification results indicate that all evaluation indicators of the IAGA-BP neural network model (MAPE = 3.75%, MAE = 0.95 kPa, MSE = 9 × 10⁻⁴, R² = 0.975) are significantly superior to those of the traditional BP and GA-BP models, with the comprehensive prediction performance improved by 62% and 46%, respectively. This model not only effectively overcomes the defect that traditional models are prone to fall into local extremum but also shows significant advantages in prediction accuracy and convergence speed. This study can provide a theoretical reference for the calculation of loess strength degradation and the prediction of long-term stability under the environment of wetting–drying alternation.

To alleviate the shortage of natural river sand and promote the utilization of aeolian sand, concrete was prepared by replacing river sand with Taklamakan Desert aeolian sand at different mass ratios. The effects of replacement ratio and curing age on compressive strength and microstructure were investigated using compressive strength tests, SEM, and EDS. A quadratic regression model was established by response surface methodology for aeolian sand concrete, using the replacement ratio, curing age, and Ca/Si ratio as variables. The results showed that compressive strength first increased and then decreased with increasing aeolian sand content, with the 20% replacement group achieving the highest strength, reaching 43.6 MPa at 28 d and 48.33 MPa at 56 d. Strength increased with curing age, but the growth rate slowed after 28 days. The SEM and EDS results indicated that suitable aeolian sand content promoted hydration product formation and matrix densification, whereas excessive replacement increased pores and interfacial defects. The Ca/Si ratio generally increased with curing age. The model showed good fitting accuracy, with R² = 0.9970, providing a reference for the strength prediction and mix design optimization of aeolian sand concrete.

To alleviate the shortage of natural river sand and promote the utilization of aeolian sand, concrete was prepared by replacing river sand with Taklamakan Desert aeolian sand at different mass ratios. The effects of replacement ratio and curing age on compressive strength and microstructure were investigated using compressive strength tests, SEM, and EDS. A quadratic regression model was established by response surface methodology using replacement ratio, curing age, and Ca/Si ratio as variables. The results showed that compressive strength first increased and then decreased with increasing aeolian sand content, with the 20% replacement group achieving the highest strength. Strength increased with curing age, but the growth rate slowed after 28 days. SEM and EDS results indicated that suitable aeolian sand content promoted hydration product formation and matrix densification, whereas excessive replacement increased pores and interfacial defects. The Ca/Si ratio generally increased with curing age. The model showed good fitting accuracy, with R² = 0.9970, providing a reference for strength prediction and mix design optimization of aeolian sand concrete.

Coarse-grained soils serve as essential fill materials in earth–rock dam engineering, where their mechanical properties critically influence dam deformation and stability, directly impacting project safety. Artificial intelligence (AI) techniques are emerging as powerful tools for predicting the mechanical properties of coarse-grained soils. However, AI-based prediction models for these properties face persistent challenges, particularly in parameter tuning—a process requiring substantial computational resources, extensive time, and specialized expertise. To address these limitations, this study proposes a novel prediction model that integrates Adaptive Simulated Annealing (ASA) with an improved Particle Swarm Optimization (PSO) algorithm to optimize the Elman Neural Network (ENN). The methodology encompasses three key aspects: First, the standard PSO algorithm is enhanced by dynamically adjusting its inertial weight and learning factors. The ASA algorithm is then employed to optimize the Adaptive PSO (APSO), effectively mitigating premature convergence and local optima entrapment during training, thereby ensuring convergence to the global optimum. Second, the refined PSO algorithm optimizes the ENN, overcoming its inherent limitations of slow convergence and susceptibility to local minima. Finally, validation through real-world engineering case studies demonstrates that the ASA-PSO-optimized ENN model achieves high accuracy in predicting the mechanical properties of coarse-grained soils. This model provides reliable constitutive parameters for stress–strain analysis in earth–rock dam engineering applications.