Most of the existing evaluation methods for the groutability of sand stratum are classification evaluation methods

and the classification standards are different

which is not conducive to practical engineering application. To solve this problem

particle swarm optimization (PSO) was used to optimize the least squares support vector machine (LSSVM)

and a quantitative evaluation model of sand groutability was proposed. With water/cement ratio (RWC)

relative density Dr

fine sand content θ (diameter<0.075 mm)

and sand characteristic particle size D10 and D15 as control variables

129 groups of groutability tests were conducted

and grout diffusion distances of each group were measured and used as quantitative evaluation indexes. The relational model between groutability factors and the control variables was obtained by PSO-LSSVM method. Furthermore

the global sensitivity of groutability factors was analyzed by FAST method. Results show that the grout diffusion distance predicted by the PSO-LSSVM model in the test set was close to the test value

the goodness of fit R2 was 0.982

and the prediction model had a high prediction accuracy. The sensitivity sequence of groutability factors was: D10>D15>Dr>θ>RWC

where D10 and D15 were significantly more sensitive than Dr

θ

and RWC.