Correlation Of Soil Parameters Research Articles

Reliability analysis of an embankment dam slope based on an ellipsoid model and PSO-ELM

Accurately comprehending the intricate characteristics of geotechnical parameters can be challenging due to the complexity of geotechnical engineering and the lack of available information. The accuracy and efficiency of conventional reliability analysis methodologies are impeded by the copiousness of raw data. This study proposed a non-probabilistic method for reliability analysis, which employed an ellipsoidal model integrated with a machine learning algorithm. This novel method characterized parameter uncertainties by their boundaries, utilized the synergy between particle swarm optimization and extreme learning machine for performance function surrogate, and employed an ellipsoidal model to calculate the reliability index. The proposed method was applied to evaluate the stability of an embankment dam slope in France. The results revealed the significant influence of the alterations in internal friction and cohesion on the stability of the embankment dam slope. Therefore, it can be imperative to account for the variability and correlation of soil parameters when conducting a rigorous analysis of embankment dam slope stability. Notably, the proposed method can circumvent the requirement for employing probability distribution forms to represent parameters, while concurrently ensuring a commendable level of computational efficiency and accuracy. Furthermore, the proposed method can be compatible with probabilistic analysis methods in evaluating the stability of the embankment dam, thereby providing an effective method for conducting reliability analyses of such structures.

Prediction of undrained shear strength and correlation in between soil parameters

Correlation of soil parameters has undeniable benefit in the determination of engineering properties of soil to solve problems in geotechnical Engineering area. The tests were conducted within geotechnical laboratory. These tested soil parameters, used in the correlation analysis are unconfined compressive strength, bulk unit weight and dry unit weight. The aim of this study is proposing a relationship in between the strength parameter with some of the index properties of soils using statistical regression analysis. The linear regression analyses have been done for prediction of unconfined compressive strength (qu ) from bulk and dry unit weight as model-1 and model-2 respectively. And dry unit weight was predicted from bulk unit weight as model-3. Model-4 represents the multiple linear regression analysis to predict qu . The health of developed models is measured by coefficient of determination (R 2) values. Though, model-1, model-2, model-3 and model-4 have R – squared values of 0.9112, 0.9333, 0.9109 and 0.9452 respectively. Therefore, they are correlated strongly and positively. The prediction of unconfined compressive strength of these soils correlated in linear regression, are fairly determined with Model-2 compared with model-1 and model-4 (MLR).

Slope Reliability Analysis Considering Variability of Shear Strength Parameters

The traditional safety factor method cannot consider the uncertainty in the slope system, and the reliability theory based on probability and statistics can more objectively evaluate the degree of influence on the slope, which has become a trend of slope stability evaluation. In this paper, the spatial variability of soil strength parameters cohesion c, internal friction angle φ is considered to build a stochastic variable model, the Monte Carlo method is adopted to study the effect of reliability index β for two parameters. The results shows that, the soil parameters c, have a significant impact on the reliability index β, however, φ is more sensitive in the calculation process which have a greater impact on the calculation of the safety factor and reliability index. The steeper slope has less reliability index. For high variability of the steep slope, a reliable indicator of φ variability is no longer sensitive. In actual engineering, the correlation of soil parameters c and is often not considered, so the reliable index calculated in this case is smaller than the actual one.

Evaluation of correlation between parameters from CPTU and DMT tests and soil type behavior chart

Abstract This paper presents the correlation of soil parameters obtained from the dilatometer of the Marchetti (DMT) and the cone penetration tests (CPT). The data for the development of these correlations come from four experimental sites of the Department of Geotechnical Engineering of the Warsaw University of Life Sciences – SGGW: test embankment in Antoniny, and the Koszyce and Nielisz water dams, Stegna test site, WULS-SGGW Campus. Additionally, the relationship between the soil behavior type classification index (Ic) from the Cone Penetration Test (CPT) and the material index (I D) from the dilatometer test (DMT) based on the Robertson method, modified in this paper, is presented. Finally, a new soil behavior type classification chart was presented, allowing to determine not only the type and condition of the soil, but also the stress history (OCR).

An Investigation on Spatial Variability of Selected Soil Parameters of Landscape under Mahogany in Wet Zone of Sri Lanka

Soils show a wide variability in their macro and micronutrient contents and distribution. Thespatial distribution of soil nutrients and physical parameters can be largely attributed tophysical, chemical, and biological processes that undergo in different rates and differentintensities in different localities. The availability of nutrients and the degree of interactionbetween these nutrients and the soil ultimately influence the vegetation that grows on aparticular soil. Although past literature has investigated the spatial variability of soilparameters and correlation of soil parameters with various agricultural crops, few studieshave examined such relationships in forest plantation landscapes. GIS has emerged as avaluable tool in visualizing and understanding spatial variability of soil parameters. Such soilparameter maps of large landscapes improve our understanding of the nature and extent ofnutrient problems, as well as aid in exploring their possible relationships with various sitefactors such as slope, physical features, moisture and temperature etc.The present study examined the spatial dependency of selected soil organic matters andnutrients on a 34-acre 8-year old mixed Mahogany stand in Wet Zone of Sri Lanka. Soilparameters of pH, Total Organic Carbon, Total Nitrogen, Ammonium Chloride extractable K,Ammonium Chloride extractable Mg, Ammonium Chloride extractable Ca was analysedusing soil samples collected following a systematic grid point sampling method (50m x 50mgrid).Accordingly, data from 44 soil sampling points were analysed using geostatistics andGIS techniques. Spatial distribution maps of above soil parameters were prepared in ArcMap9.3 environment. The relationships of above soil parameters with various site specificattributes such as elevation, soil texture, proximity to drainage systems/streams werestatistically examined.Results revealed that soil parameters tested show spatial heterogeneityalong the landscape while showing correlations with selected site attributes. Implications ofthe findings in management of Mahogany forest crop are also discussed.