Laboratory simulation of irrigation-induced settlement of collapsible desert soils under constant surcharge

The heterogeneous nature of soil as a load bearing material, coupled with varying environmental conditions, pose challenges to geotechnical engineers in their quest to characterize and understand ground behavior for safe design of structures. Standard procedures for checking bearing capacity and settlement alone may sometimes be insufficient to achieve an acceptable degree of durability and in-service performance of a structure, particularly under varying environmental conditions, whether natural or man-made. There exists a wide variety of problematic soils that exhibit swelling, shrinkage, dispersion and collapse characteristics occasioned by changes in moisture content. Specific examples are collapsible soils, which occur mainly in arid and semi-arid regions, are generally capable of resisting fairly large loads in the dry condition but suffer instability and significant strength loss when in contact with water. A number of case studies in the United Arab Emirates were examined, where lightly loaded structures such as boundary walls, pavements and footpaths had been built on ground overlying collapsible soil strata. Sustained irrigation of the dry landscapes was found to have caused uneven settlement of the collapsible soils leading to continuous distress to the structures as evident from cracking and deformation. To help address the problem, an opportunity has been taken to develop a laboratory method of simulating the loaded behavior of collapsible soils in varying situations and to measure its deformation at constant surcharge and ground water infiltration rates. Finally, relationships were developed to estimate the time and magnitude of settlement, if thickness of collapsible soil is known

Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

As a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better