2016
DOI: 10.1515/geo-2016-0005
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The application of multivariate data analysis in the interpretation of engineering geological parameters

Abstract: 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 wer… Show more

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Cited by 5 publications
(5 citation statements)
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References 28 publications
(37 reference statements)
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“…This is a widely known multivariate classification method in which each case starts in a separate cluster and joins up to the other clusters as the linkage distance grows, until only one cluster remains [41]. This method has been successfully applied in hydrology [42][43][44][45], hydrogeology [46], geology [47][48][49][50][51], chemistry [52] and anthropology [53] to find similar and homogeneous groups of observations. The validity of the groupings was verified using linear discriminant analysis (LDA), which separates the observations with linear planes resulting in a percentage of correctly classified cases [54,55].…”
Section: Discussionmentioning
confidence: 99%
“…This is a widely known multivariate classification method in which each case starts in a separate cluster and joins up to the other clusters as the linkage distance grows, until only one cluster remains [41]. This method has been successfully applied in hydrology [42][43][44][45], hydrogeology [46], geology [47][48][49][50][51], chemistry [52] and anthropology [53] to find similar and homogeneous groups of observations. The validity of the groupings was verified using linear discriminant analysis (LDA), which separates the observations with linear planes resulting in a percentage of correctly classified cases [54,55].…”
Section: Discussionmentioning
confidence: 99%
“…MVA scrutinises the statistical performance of one or more inputs with response at a time [41]. In engineering application, MVA is used to conduct trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest [42,43]. RSM creates a second-order polynomial model for estimating the output responses.…”
Section: Predictive Modellingmentioning
confidence: 99%
“…The layers will be in a state of overlap [12], i.e. the movement of the layers, one in the other (Fig.…”
Section: Anomaly Related To Layer Overlapmentioning
confidence: 99%