Effect of soaking time of dithionite-citrate-bicarbonate solution on strength and deformation characteristics of lateritic soil

This study aimed to reveal the influence of different free-iron-oxides contents on the strength and deformation characteristics of in situ lateritic soil.A test method that combined the selective chemical dissolution method and in situ Ménard pressuremeter test(PMT)was proposed.The soaking time in dithioniteecitrateebicarbonate(DCB)solution was used as a variable to control the free-iron-oxides content in lateritic soil.Then,the in situ lateritic soil boreholes with different soaking time were tested by PMT.The results showed that the in situ horizontal pressure p0,critical edge pressure pf,ultimate pressure prediction pl,pressuremeter modulus Em,shear modulus Gm,and foundation-bearing capacity f0k of lateritic soil decreased rapidly after immersing in DCB solution within 1e4 d.With increasing soaking time,the decrease rate reduced gradually.Moreover,the relationship curve between free-iron-oxides content and soaking time declined rapidly and then stabilized,and the free-iron-oxides content at the inflection point was 30.11 g/kg.When the free-iron-oxides content changed to the inflection point,the free-iron-oxides that played a cementing role was largely removed,indicating that the effective cementing iron-content of Miaoling lateritic soil was about 52.9%.This study demonstrated that the proposed test method can determine the influence of free-iron-oxides content on the strength and deformation characteristics of lateritic soil.

Calibration of soil parameters based on intelligent algorithm using efficient sampling method

This study combined a neural network and Latin hypercube sampling(LHS)to calibrate soil parameters.The Monte Carlo parameters were calibrated by generating different numbers of training samples for pressuremeter tests and excavations.The results showed that when the number of samples was 25 or 50,the parameter calibration accuracy was very high.However,the improvement in accuracy did not increase significantly with a further increase in the number of samples,but tended to be stable.The number of training samples was set at 50 to strike a balance between the calibration accuracy and efficiency for four parameters.For 25 groups of samples,the calibration results using LHS were better than those using orthogonal sampling.Compared to stochastic optimization algorithms,a neural network combined with LHS could significantly reduce the calibration time.This method was applied to actual foundation pit engineering in China.The results showed that using the proposed calibration method clearly improved the accuracy when predicting the deformation induced by the excavation.