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Applications of artificial intelligence in constitutive modelling of soils. / Stefanos Drakos

Swansea University Author: Stefanos Drakos

Abstract

An appropriate constitutive model embedded in a finite element engine is the key to the successful prediction of the observed behaviour of geotechnical structures. However, to capture the behaviour of geomaterials accurately, the constitutive models have to be complex involving a large number of mat...

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Published: 2008
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42782
first_indexed 2018-08-02T18:55:32Z
last_indexed 2018-08-03T10:11:05Z
id cronfa42782
recordtype RisThesis
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spelling 2018-08-02T16:24:30.4765951 v2 42782 2018-08-02 Applications of artificial intelligence in constitutive modelling of soils. 2ce1fb8b450e86eed2bccfb8616804ee NULL Stefanos Drakos Stefanos Drakos true true 2018-08-02 An appropriate constitutive model embedded in a finite element engine is the key to the successful prediction of the observed behaviour of geotechnical structures. However, to capture the behaviour of geomaterials accurately, the constitutive models have to be complex involving a large number of material parameters and constants. This thesis resents a methodology for converting or recasting complex constitutive models for eomaterials developed based on any constitutive theory into a fully trained Artificial neural Network (ANN), which is then embedded in an appropriate solution code. The sength of strain trajectory traced by a material point, also called 'intrinsic time' is used as 1 additional input parameter in training. For the purpose of illustration, two constitutive models viz. Hardening Soil Model available in the commercial software, PLAXIS and a wo-surface deviatoric hardening model in the multilaminate framework developed by ee and Pande (2004) have been cast in the form of an ANN. E-Thesis Computer science.;Artificial intelligence.;Soil sciences. 31 12 2008 2008-12-31 COLLEGE NANME Engineering COLLEGE CODE Swansea University Doctoral Ph.D 2018-08-02T16:24:30.4765951 2018-08-02T16:24:30.4765951 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Stefanos Drakos NULL 1 0042782-02082018162521.pdf 10807551.pdf 2018-08-02T16:25:21.5070000 Output 5354874 application/pdf E-Thesis true 2018-08-02T16:25:21.5070000 false
title Applications of artificial intelligence in constitutive modelling of soils.
spellingShingle Applications of artificial intelligence in constitutive modelling of soils.
Stefanos Drakos
title_short Applications of artificial intelligence in constitutive modelling of soils.
title_full Applications of artificial intelligence in constitutive modelling of soils.
title_fullStr Applications of artificial intelligence in constitutive modelling of soils.
title_full_unstemmed Applications of artificial intelligence in constitutive modelling of soils.
title_sort Applications of artificial intelligence in constitutive modelling of soils.
author_id_str_mv 2ce1fb8b450e86eed2bccfb8616804ee
author_id_fullname_str_mv 2ce1fb8b450e86eed2bccfb8616804ee_***_Stefanos Drakos
author Stefanos Drakos
author2 Stefanos Drakos
format E-Thesis
publishDate 2008
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description An appropriate constitutive model embedded in a finite element engine is the key to the successful prediction of the observed behaviour of geotechnical structures. However, to capture the behaviour of geomaterials accurately, the constitutive models have to be complex involving a large number of material parameters and constants. This thesis resents a methodology for converting or recasting complex constitutive models for eomaterials developed based on any constitutive theory into a fully trained Artificial neural Network (ANN), which is then embedded in an appropriate solution code. The sength of strain trajectory traced by a material point, also called 'intrinsic time' is used as 1 additional input parameter in training. For the purpose of illustration, two constitutive models viz. Hardening Soil Model available in the commercial software, PLAXIS and a wo-surface deviatoric hardening model in the multilaminate framework developed by ee and Pande (2004) have been cast in the form of an ANN.
published_date 2008-12-31T07:22:26Z
_version_ 1829992185613778944
score 11.058331

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