Abstract
Rock Quality Designation (RQD) of a site is a very important engineering property from geotechnical considerations. RQD is very useful in identifying potential problems related to bearing capacity, settlement, slope stability problems and also serves as warning indicator of low quality rock zones that requires greater scrutiny. However, due to time and cost considerations, geotechnical investigations are carried out at larger spacing which calls for appropriate techniques for estimation of data at intermediate locations for appropriate judgements. An earlier study reported IDW (Inverse distance weighing method) to be appropriate methodology for 20 m level below ground level at the site. It was deemed necessary to examine whether similar technique would be applicable for other layers also. This paper deals with estimation of RQD values at intermediate locations from an available coarse grid data using various spatial interpolation techniques for different depths. Geo-statistical methods selected for the present study are K nearest neighbour (KNN) mean method, Inverse Distance Weighing (IDW) method and Trend Surface Analysis (TSA). Spatial interpolation has performed at depths 20, 25 and 29 m below ground level. IDW and KNN methods estimated the RQD values at all depths with good accuracy followed by TSA method. It is observed that site-specific parameters obtained for best performance are different for different depths. It is necessary to analyse the data at each level and perform spatial interpolation to obtain the site-specific parameters to obtain best results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alexandra, K., Donald, B. G.: A comparative study of interpolation methods for mapping soil properties. Agronomy J. 393–400 (1999)
Annelies, G., Andre, V.: Geostatistical Interpolation of soil properties in boom clay in Flanders. geoENV VII—Geostatistics for environmental applications, 219–230 (2010)
Baxter, V.E.: Distributed Hydrologic Modeling Using GIS. Springer, Netherlands (2001)
Binny, G., Shetty, A., Jayaprakash, and Chaya, D. Y.: Spatial variability of topsoil chemical properties. Indian J. Agric. Res. 49(2), 134–141 (2015)
Burgess, T. M., Webster, R.: Optimal interpolation and isarithmic mapping of soil properties ii, block kriging. Europian J. Soil Sci. 333–341 (1980)
Chen, J., Li, X., Zhu, H.: Geostatistical method for predicting RMR ahead of tunnel face excavation using dynamically exposed geological information. Eng. Geol. 214–223 (2017)
Dasaka, S. M., Zhang, L. M.: Spatial variability of in situ weathered soil. Geotechnique 375–384 (2012)
Kevin, D. M., Laurie, B. G.: Three-dimensional liquefaction potential analysis using geostatistical interpolation. Soil Dyn. Earthquake Eng. 369–381 (2005)
Marvasti, F.: Nonuniform Sampling: Theory and Practice, vol. 1. Springer, US (2001)
Masoud, A. A., Ahmed, K. A.: Three-dimensional geotechnical modeling of the soils in Riyadh city, KSA. Bulletin of Engineering Geology and the Environment 1–17 (2017)
Rafi, A., Saha Dauji & Kapilesh Bhargava.: Estimation of SPT from coarse grid data by spatial interpolation technique. In: Proceedings of Indian Geotechnical Conference, TH-10–66. Indian Institute of Science, Bengaluru, India (2018)
Rafi, A., Dauji, S., Bhargava, K.: Spatial interpolation methods for development of fine resolution RQD map from coarse grid data. In: Proceedings of Indian Conference on Geotechnical and Geo-Environmental Engineering, Paper No. 29. Motilal Nehru National Institute of Technology, Allahabad, India (2019)
Robinson, T. P., Metternicht, G.: Testing the performance of spatial interpolation techniques for mapping soil properties. Comput. Electron. Agric. 97–108 (2006)
Samui, P., Sitharam, T. G.: Application of geostatistical models for estimating spatial variability of rock depth. Sci. Res. Eng. 886–894 (2011)
Selim, A., Alper, S., Gokpete, B.A.: Geostatistical interpolation for modelling SPT data in northern Izmir. Sadhana 38(6), 1451–1468 (2013)
Sitharam, T. G., Samui, P.: Geostatistical modelling of spatial and depth variability of SPT data for Bangalore. Geomech. Geoeng. Int. 307–316 (2007)
Soulie, M., Montes, P., Silvestri, V.: Modelling spatial variability of soil parameters. Can. Geotech. J. 27, 617–630 (1990)
Unwin, D.J.: An introduction to trend surface analysis. Geo Abstracts, University of East Anglia, Norwich (1975)
Wren, E.A.: Trend surface analysis—A review. Canadian Soc. Explor. Geophys. 9, 39–44 (1973)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rafi, A., Dauji, S., Bhargava, K. (2021). Evaluation of Spatial Interpolation for RQD at Different Depths. In: Patel, S., Solanki, C.H., Reddy, K.R., Shukla, S.K. (eds) Proceedings of the Indian Geotechnical Conference 2019. Lecture Notes in Civil Engineering, vol 137. Springer, Singapore. https://doi.org/10.1007/978-981-33-6466-0_56
Download citation
DOI: https://doi.org/10.1007/978-981-33-6466-0_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6465-3
Online ISBN: 978-981-33-6466-0
eBook Packages: EngineeringEngineering (R0)