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Artificial intelligence to model bedrock depth uncertainty
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Jord- och bergmekanik.
2019 (Engelska)Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
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.

Ort, förlag, år, upplaga, sidor
2019. , s. 84
Serie
TRITA-ABE-MBT ; 19205
Nyckelord [en]
Machine learning, Artificial intelligence, Kriging, prediction, algorithm.
Nationell ämneskategori
Geoteknik
Identifikatorer
URN: urn:nbn:se:kth:diva-252317OAI: oai:DiVA.org:kth-252317DiVA, id: diva2:1318203
Externt samarbete
Tyréns Konsult AB
Ämne / kurs
Jord- och bergmekanik
Utbildningsprogram
Teknologie masterexamen - Husbyggnads- och anläggningsteknik
Presentation
2019-05-24, B25, Brinellvägen 23, SE- 100 44, Stockholm, 10:33 (Engelska)
Handledare
Examinatorer
Projekt
BIG and BeFo project "Rock and ground water including artificial intelligenceTillgänglig från: 2019-08-12 Skapad: 2019-05-27 Senast uppdaterad: 2019-08-12Bibliografiskt granskad

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