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 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.

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.