3D geotechnical modeling of subsurface soils in Kerman city, southeast Iran View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

I. Aghamolaie, G. R. Lashkaripour, M. Ghafoori, N. Hafezi Moghaddas

ABSTRACT

Subsurface stratigraphy has recently been a subject of intense research within the last two decades for dealing with the complicated nature of subsurface ground layers through building three-dimensional geological models. Considering the importance of subsurface soil characterization in urban areas in geotechnical and geological engineering projects, the present study was conducted to propose a three-dimensional (3D) geological engineering model using the sequential Gaussian simulation (SGS) approach for the subsurface soil of the city of Kerman, southeast Iran. Due to the intense variability of soil in the study area, civil engineering projects are sometimes encountered with severe problems in the city. Hence, a better understanding of the soil-related problems has to be gained for dealing with issues in the current and future civil engineering works. The main goal of the present research is to incorporate the SGS method for studying the spatial variability of soil through variograms and then forecasting the values of soil properties at unsampled locations. For this purpose, index geotechnical parameters, including standard penetration test number (SPTN), fine-grained material percentage, plasticity index (PI), and liquid limit (LL), were used as the input data. A database consisting of 700 borehole data was prepared. Then, the raw data were normalized prior to importing to the SGS model. Next, maps related to the average of all realizations along with the coefficient of variation (CV) were prepared for each variable. Finally, the prepared maps were interpreted based on the sedimentary environment of Kerman. More... »

PAGES

1385-1400

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10064-018-1240-7

DOI

http://dx.doi.org/10.1007/s10064-018-1240-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101293762


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