Effect of natural and synthetic fibers reinforcement on California bearing ratio and tensile strength of clay

Use of environmentally friendly approaches with the purpose of strengthening soil layers along with finding correlations between the mechanical characteristics of fiber-reinforced soils such as indirect tensile strength(ITS)and California bearing ratio(CBR)and as well as the evaluation of shear strength parameters obtained from the triaxial test would be very effective at geotechnical construction sites.This research was aimed at investigating the influence of natural fibers as sustainable ones including basalt(BS)and bagasse(BG)as well as synthetic polyester(PET)fibers on the strength behavior of clayey soil.To this end,the effects of various fiber contents(0.5%,1%and 2%)and lengths(2.5 mm,5 mm and 7.5 mm)were experimentally evaluated.By conducting ITS and CBR tests,it was found that increasing fiber content and length had a significant influence on CBR and ITS values.Moreover,2%of 7.5 mm-long fibers led to the largest values of CBR and ITS.The CBR values of soil reinforced with PET,BS,and BG fibers were determined as 19.17%,15.43%and 13.16%,respectively.The ITS values of specimens reinforced with PET,BS,and BG fibers were reported as 48.57 kPa,60.7 kPa and 47.48 kPa,respectively.The results of the triaxial compression test revealed that with the addition of BS fibers,the internal friction angle increased by about 100%,and with the addition of PET fibers,the cohesion increased by about 70%.Moreover,scanning electron microscope(SEM)analysis was employed to confirm the findings.The relationship between CBR and ITS values,obtained via statistical analysis and used for the optimum design of road pavement layers,demonstrated that these parameters had high correlation coefficients.The outcomes of multiple linear regression and sensitivity analysis also confirmed that the fiber content had a greater effect on CBR and ITS values than fiber length.

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines

This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models.The soils used in this study are stabilized using various combinations of cement,lime,and rice husk ash.To predict the results of unconfined compressive strength tests conducted on soils,a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement,lime,and rice husk ash is used.Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement,lime,and rice husk ash under different conditions.The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering.This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks.The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models.Moreover,based on sensitivity analysis results,it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.