Benny Mathews Abraham

Utilization of Quarry Dust to Improve the Geotechnical Properties of Soils in Highway Construction

Quarries and aggregate crushers are basic requisites for construction industry and quarry dust is a byproduct of rubble crusher units. Geotechnical and mineralogical characterization of quarry dust and its interaction behavior with soils can lead to viable solutions for its large-scale utilization and disposal. The effect of addition of quarry dust on properties of red earth and two different cohesive soils; viz. kaolinite, Cochin marine clay was studied in detail. The results indicate that compaction characteristics and CBR of soils are improved by addition of quarry dust. Problems associated with the construction of highways over clayey subgrade can be reduced significantly by mixing with quarry dust.

A study of geotechnical properties of Cochin Marine Clays

Most of the Greater Cochin area, which is undergoing rapid industrialisation, consists of extremely soft marine clay calling for expensive deep foundations. This paper presents a study on the physical properties and engeering characteristics of Cochin marine clays. These marine clays are characterised by high Atterberg limits and natural water contents. They are moderately sensitive with liquidity indices ranging over 0.46 to 0.87.The grain size distribution shows almost equal fractions of clay and silt size with sand content varying around 20%. Use of a dispersing agent in carrying out grain size distribution test plays an important role. The fabric of these clays had been identified as flocculant. The pore water has low salinity which results in marginal changes in properties on washing.Consolidation test results showed a preconsolidation pressure of up to about 0.5 kg/cm2 with high compression indices. Compression index vs liquid limit yielded a correlation comparable to that of published data. The undisturbed samples have a much larger coefficient of secondary consolidation as a result of flocculant fabric. These clays have very low undrained shear strength.

Artificial Neural Networks: A Solution to the Ambiguity in Prediction of Engineering Properties of Fine-Grained Soils

Determination of soaked california bearing ratio (CBR) and compaction characteristics of soils in the laboratory require considerable time and effort. To make a preliminary assessment of the suitability of soils required for a project, prediction models for these engineering properties on the basis of laboratory tests—which are quick to perform, less time consuming and cheap—such as the tests for index properties of soils, are preferable. Nevertheless researchers hold divergent views regarding the most influential parameters to be taken into account for prediction of soaked CBR and compaction characteristics of fine-grained soils. This could be due to the complex behaviour of soils—which, by their very nature, exhibit extreme variability. However this disagreement is a matter of concern as it affects the dependability of prediction models. This study therefore analyses the ability of artificial neural networks and multiple regression to handle different influential parameters simultaneously so as to make accurate predictions on soaked CBR and compaction characteristics of fine-grained soils. The results of simple regression analyses included in this study indicate that optimum moisture content (OMC) and maximum dry density (MDD) of fine-grained soils bear better correlation with soaked CBR of fine-grained soils than plastic limit and liquid limit. Simple regression analyses also indicate that plastic limit has stronger correlation with compaction characteristics of fine-grained soils than liquid limit. On the basis of these correlations obtained using simple regression analyses, neural network prediction models and multiple regression prediction models—with varying number of input parameters are developed. The results reveal that neural network models have more ability to utilize relatively less influential parameters than multiple regression models. The study establishes that in the case of neural network models, the relatively less powerful parameters—liquid limit and plastic limit can also be used effectively along with MDD and OMC for better prediction of soaked CBR of fine-grained soils. Also with the inclusion of less significant parameter—liquid limit along with plastic limit the predictions on compaction characteristics of fine-grained soils using neural network analysis improves considerably. Thus in the case of neural network analysis, the use of relatively less influential input parameters along with stronger parameters is definitely beneficial, unlike conventional statistical methods—for which, the consequence of this approach is unpredictable—giving sometimes not so favourable results. Very weak input parameters alone need to be avoided for neural network analysis. Consequently, when there is ambiguity regarding the most influential input parameters, neural network analysis is quite useful as all such influential parameters can be taken to consideration simultaneously, which will only improve the performance of neural network models. As soils by their very nature, exhibit extreme complexity, it is necessary to include maximum number of influential parameters—as can be determined easily using simple laboratory tests—in the prediction models for soil properties, so as to improve the reliability of these models—for which, use of neural networks is more desirable.

Water Content Ratio: An Effective Substitute for Liquidity Index for Prediction of Shear Strength of Clays

Undrained shear strength of saturated clays is a very important property in geo-technical engineering practice. Since the collection of undisturbed samples and testing the same is difficult task and time consuming process, any attempt to obtain correlations between shear strength and consistency limits would be highly desirable. Several attempts have been made in the past to correlate shear strength with Liquidity index. The computation of Liquidity index involves the value of plastic limit determined by Casagrande thread rolling method; but the determination of the same is relatively a difficult task in geotechnical engineering practice especially so in less plastic soils. It has been shown that a good linear correlation exists between log of shear strength and water content ratio (ratio of water content to liquid limit). With the help of numerous experimental results, it could be established that water content ratio could replace the well-known parameter liquidity index to predict shear strength. This enables to eliminate the determination of the plastic limit. The relation between water content ratio and liquidity index depends on the liquid limit to plastic limit ratio, irrespective of the geological origin of the soil.