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.
Luo et al. (Wed,) studied this question.