Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Artificial intelligence to model bedrock depth uncertainty
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The estimation of bedrock level for soil and rock engineering is a challenge

associated to many uncertainties. Nowadays, this estimation is

performed by geotechnical or geophysics investigations. These methods

are expensive techniques, that normally are not fully used because

of limited budget. Hence, the bedrock levels in between investigations

are roughly estimated and the uncertainty is almost unknown.

Machine learning (ML) is an artificial intelligence technique that

uses algorithms and statistical models to predict determined tasks.

These mathematical models are built dividing the data between training,

testing and validation samples so the algorithm improve automatically

based on passed experiences.

This thesis explores the possibility of applying ML to estimate the

bedrock levels and tries to find a suitable algorithm for the prediction

and estimation of the uncertainties. Many diferent algorithms were

tested during the process and the accuracy level was analysed comparing

with the input data and also with interpolation methods, like

Kriging.

The results show that Kriging method is capable of predicting the

bedrock surface with considerably good accuracy. However, when is

necessary to estimate the prediction interval (PI), Kriging presents a

high standard deviation. The machine learning presents a bedrock

surface almost as smooth as Kriging with better results for PI. The

Bagging regressor with decision tree was the algorithm more capable

of predicting an accurate bedrock surface and narrow PI.

Place, publisher, year, edition, pages
2019. , p. 84
Series
TRITA-ABE-MBT ; 19205
Keywords [en]
Machine learning, Artificial intelligence, Kriging, prediction, algorithm.
National Category
Geotechnical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-252317OAI: oai:DiVA.org:kth-252317DiVA, id: diva2:1318203
External cooperation
Tyréns Konsult AB
Subject / course
Soil and Rock Mechanics
Educational program
Degree of Master - Civil and Architectural Engineering
Presentation
2019-05-24, B25, Brinellvägen 23, SE- 100 44, Stockholm, 10:33 (English)
Supervisors
Examiners
Projects
BIG and BeFo project "Rock and ground water including artificial intelligenceAvailable from: 2019-08-12 Created: 2019-05-27 Last updated: 2019-08-12Bibliographically approved

Open Access in DiVA

fulltext(7051 kB)24 downloads
File information
File name FULLTEXT03.pdfFile size 7051 kBChecksum SHA-512
ecb702dec9191a3ac6a4a4cba4625ffc32b4f3c9b51c9c8332995a80f20ec46386e0aa8bc13b2eb4b00aed2de7799114e8d34a15ba447d3efe82ec107efe89f9
Type fulltextMimetype application/pdf

By organisation
Soil and Rock Mechanics
Geotechnical Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 24 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 64 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf