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Roads in places with seasonal frost undergo several freeze-thaw (F-T) cycles annually, resulting in variable degrees of deterioration in the mechanical properties of the subgrade. To methodically investigate the mechanical properties of subgrade clay during freeze-thaw cycles and to develop a precise constitutive model, triaxial tests were conducted under the most unfavorable soil conditions. The studies indicate that the degrading impact of the freeze-thaw cycle on the mechanical characteristics of the soil predominantly transpires during the initial freeze-thaw cycle. Soil strength reaches its minimum after the third freeze-thaw cycle, followed by a slight increase, and ultimately stabilizes between the fifth and seventh cycles. The maximum strength reduction at confining pressures of 100 kPa, 200 kPa, and 300 kPa was 39%, 37%, and 33%, respectively. As confining pressure escalates, the reduction in soil strength lessens. The soil demonstrates differing degrees of degradation following F-T cycles at both high and low compaction levels, with the degradation becoming increasingly evident as compaction intensifies. Utilizing the experimental database, a genetic algorithm (GA) enhanced backpropagation neural network (BPNN) model (GA-BPNN) and a BP-aided Duncan-Chang (D-C) model were developed to forecast the mechanical properties of freeze-thaw clay. The R2 values for the two models on the test set were 0.995 and 0.967, respectively. The efficacy of these two models demonstrates that machine learning can attain commendable outcomes in extensive data structures (total stress-strain curve) as well as exhibit superior performance in limited data (model parameters) while developing the constitutive model of soil.

来源平台：ENVIRONMENTAL EARTH SCIENCES