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Accuracy and precision of bedrock sur-face prediction using geophysics and geostatistics.
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Land and Water Resources Engineering.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In underground construction and foundation engineering uncertainties associated with subsurface properties are inevitable to deal with. Site investigations are expensive to perform, but a limited understanding of the subsurface may result in major problems; which often lead to an unexpected increase in the overall cost of the construction project. This study aims to optimize the pre-investigation program to get as much correct information out from a limited input of resources, thus making it as cost effective as possible. To optimize site investigation using soil-rock sounding three different sampling techniques, a varying number of sample points and two different interpolation methods (Inverse distance weighting and point Kriging) were tested on four modeled reference surfaces. The accuracy of rock surface predictions was evaluated using a 3D gridding and modeling computer software (Surfer 8.02®). Samples with continuously distributed data, resembling profile lines from geophysical surveys were used to evaluate how this could improve the accuracy of the prediction compared to adding additional sampling points. The study explains the correlation between the number of sampling points and the accuracy of the prediction obtained using different interpolators. Most importantly it shows how continuous data significantly improves the accuracy of the rock surface predictions and therefore concludes that geophysical measurement should be used combined with traditional soil rock sounding to optimize the pre-investigation program.

Place, publisher, year, edition, pages
, TRITA-LWR Degree Project, ISSN 1651-064X ; 2015:12
Keyword [en]
Bedrock surface prediction; Geotechnical site investigations; Sampling techniques; Geophysics; Kriging; Inverse distance weighting
National Category
Civil Engineering
URN: urn:nbn:se:kth:diva-171859OAI: diva2:844732
Educational program
Master of Science in Engineering - Urban Management
Available from: 2015-09-21 Created: 2015-08-07 Last updated: 2015-09-21Bibliographically approved

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