Laboratory-scale model of carbon dioxide deposition for soilstabilisation

Olivine sand is a natural mineral,which,when added to soil,can improve the soil’s mechanical properties while also sequester carbon dioxide(CO2)from the surrounding environment.The originality of this paper stems from the novel two-stage approach.In the first stage,natural carbonation of olivine and carbonation of olivine treated soil under different CO2pressures and times were investigated.In this stage,the unconfined compression test was used as a tool to evaluate the strength performance.In the second stage,details of the installation and performance of carbonated olivine columns using a laboratory-scale model were investigated.In this respect,olivine was mixed with the natural soil using the auger and the columns were then carbonated with gaseous CO2.The unconfined compressive strengths of soil in the first stage increased by up to 120% compared to those of the natural untreated soil.The strength development was found to be proportional to the CO2pressure and carbonation period.Microstructural analyses indicated the presence of magnesite on the surface of carbonated olivinetreated soil,demonstrating that modified physical properties provided a stronger and stiffer matrix.The performance of the carbonated olivine-soil columns,in terms of ultimate bearing capacity,showed that the carbonation procedure occurred rapidly and yielded a bearing capacity value of 120 k Pa.Results of this study are of significance to the construction industry as the feasibility of carbonated olivine for strengthening and stabilizing soil is validated.Its applicability lies in a range of different geotechnical applications whilst also mitigates the global warming through the sequestration of CO2.

Predicting lateral displacement caused by seismic liquefaction and performing parametric sensitivity analysis:Considering cumulative absolute velocity and fine content

Lateral displacement due to liquefaction(D_(H))is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil.Among all earthquake parameters,the standardized cumulative absolute velocity(CAV_(5))exhibits the largest correlation with increasing pore water pressure and liquefaction.Furthermore,the complex effect of fine content(FC)at different values has been studied and demonstrated.Nevertheless,these two contexts have not been entered into empirical and semi-empirical models to predict D_(H)This study bridges this gap by adding CAV_(5)to the data set and developing two artificial neural network(ANN)models.The first model is based on the entire range of the parameters,whereas the second model is based on the samples with FC values that are less than the 28%critical value.The results demonstrate the higher accuracy of the second model that is developed even with less data.Additionally,according to the uncertainties in the geotechnical and earthquake parameters,sensitivity analysis was performed via Monte Carlo simulation(MCS)using the second developed ANN model that exhibited higher accuracy.The results demonstrated the significant influence of the uncertainties of earthquake parameters on predicting D_(H).