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Developing correlations between the soil fines content and CPT results using neural networks

M. S. Kovacevic, K. G. Gavin, C. Reale, L. Libric, D. Juric Kacunic

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

4   Link opens in a new tab Citations (SciVal)

Abstract

Knowledge of the fines content is necessary for all soil classification systems and an important factor in the evaluation of soil strength in liquefaction and seismic settlement analysis. This paper presents the application of cone penetration test, CPT data for estimating the soil fines content. The correlation can be used either as a first estimate of fines content (for example in the offshore environment) or to provide statistical information on the variation of fines content within a given area of interest (e.g. for a regional liquefaction study). The paper shows how field and laboratory test data were used with a neural network to correlate the CPT results and the fines content. Data from five site investigation locations across Northern Croatia were utilised. Verification of the approach is performed using field and lab test data from the Veliki vrh landslide.

Original languageEnglish
Title of host publication17th European Conference on Soil Mechanics and Geotechnical Engineering, ECSMGE 2019 - Proceedings
EditorsHaraldur Sigursteinsson, Sigurour Erlingsson, Sigurour Erlingsson, Bjarni Bessason
PublisherInternational Society for Soil Mechanics and Geotechnical Engineering
ISBN (Electronic)9789935943613
DOIs
Publication statusPublished - 2019
Event17th European Conference on Soil Mechanics and Geotechnical Engineering, ECSMGE 2019 - Reykjavik, Iceland
Duration: 1 Sept 20196 Sept 2019

Publication series

Name17th European Conference on Soil Mechanics and Geotechnical Engineering, ECSMGE 2019 - Proceedings

Conference

Conference17th European Conference on Soil Mechanics and Geotechnical Engineering, ECSMGE 2019
Country/TerritoryIceland
CityReykjavik
Period1/09/196/09/19

Keywords

  • Correlation
  • CPT
  • Fines content
  • Neural network

ASJC Scopus subject areas

  • Soil Science
  • Geotechnical Engineering and Engineering Geology

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