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dc.contributor.authorShahin, Mohamed
dc.contributor.editorPierre Delage
dc.contributor.editorJacques Desrues
dc.contributor.editorRoger Frank
dc.contributor.editorAlain Puech
dc.contributor.editorFancois Schlosser
dc.date.accessioned2017-01-30T11:50:40Z
dc.date.available2017-01-30T11:50:40Z
dc.date.created2013-09-23T20:01:15Z
dc.date.issued2013
dc.identifier.citationShahin, M.A. 2013. Artificial intelligence for modeling load-settlement response of axially loaded (steel) driven piles, in P. Delage et al (ed), 18th International Conference on Soil Mechanics and Geotechnical Engineering, Sep 2-6 2013, pp. 797-800. Paris, France: Presses des Ponts.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/15587
dc.description.abstract

The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other and the design of pile foundations should thus consider the bearing capacity and settlement in-separately. This requires the full load-settlement behavior of piles to be well predicted. However, it is well known that the actual load-settlement behavior of pile foundations can only be obtained by load tests carried out in-situ, which are expensive and time-consuming. In this paper, artificial intelligence (AI) using the recurrent neural networks (RNN) is used to develop a prediction model that can resemble the full load-settlement response of steel driven piles subjected to axial loading. The developed RNN model is calibrated and validated using several in-situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the RNN model has the ability to predict well the load-settlement response of axially loaded steel driven piles and can thus be used by geotechnical engineers for routine design practice.

dc.publisherPresses des Ponts
dc.relation.urihttp://www.issmge.org/images/joomd/797-800.pdf
dc.subjectartificial intelligence
dc.subjectmodeling
dc.subjectpile foundations
dc.subjectload-settlement
dc.subjectrecurrent neural networks
dc.titleArtificial intelligence for modeling load-settlement response of axially loaded (steel) driven piles
dc.typeConference Paper
dcterms.source.startPage797
dcterms.source.endPage800
dcterms.source.titleProceedings of the 18th International Conference on Soil Mechanics and Geotechnical Engineering
dcterms.source.seriesProceedings of the 18th International Conference on Soil Mechanics and Geotechnical Engineering
dcterms.source.conferenceThe 18th International Conference on Soil Mechanics and Geotechnical Engineering
dcterms.source.conference-start-dateSep 2 2013
dcterms.source.conferencelocationParis, France
dcterms.source.placeFrance
curtin.department
curtin.accessStatusFulltext not available


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