Using of Multivariate Statistical Analysis in Engineering Geology at the Pest Side of the Metro Line 4 in Budapest, Hungary

Bodnár, Nikolett; Kovács, József; Török, Ákos

doi:10.1007/978-3-319-09060-3_153

Cited by 1 publication

(2 citation statements)

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“…The area of Rákóczi Square metro station is covered by variegated siltsone, which encompasses bentonitic clays and lenticular sand bodies [25,26]. Previous studies [27] suggest the presence of faults that intersects the siltstone layers. The groundwater table is controlled by the River Danube forming a hydrostatic system.…”

Section: Geological Settingmentioning

confidence: 99%

“…The samples with missing data points were not used in the analyses. The extreme values were evaluated using published results [23][24][25]27] and also theoretically, thus mistyped and incorrect analytical results were eliminated. Cluster analysis is a kind of multivariate data analysis that allows the reduction of dimensions and the grouping of samples into fairly "homogeneous" groups.…”

Section: Statistical and Multivariate Data Analysesmentioning

confidence: 99%

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The application of multivariate data analysis in the interpretation of engineering geological parameters

2016

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Abstract:The paper presents the evaluation of engineering geological laboratory test results of core drillings along the new metro line (line 4) in Budapest by using a multivariate data analysis. A data set of 30 core drillings with a total coring length of over 1500 meters was studied. Of the eleven engineering geological parameters considered in this study, only the ve most reliable (void ratio, dry bulk density, angle of internal friction, cohesion and compressive strength) representing 1260 data points were used for multivariate (cluster and discriminant) analyses. To test the results of the cluster analysis discriminant analysis was used. The results suggest that the use of multivariate analyses allows the identi cation of di erent groups of sediments even when the data sets are overlapping and contain several uncertainties. The tests also prove that the use of these methods for seemingly very scattered parameters is crucial in obtaining reliable engineering geological data for design.

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Section: Geological Settingmentioning

confidence: 99%

Section: Statistical and Multivariate Data Analysesmentioning

confidence: 99%