Shear Wave Velocity by Polynomial Neural Networks and Genetic Algorithms Based on Geotechnical Soil Properties View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-04

AUTHORS

H. Mola-Abasi, A. Eslami, P. Tabatabaie Shourijeh

ABSTRACT

Shear wave velocity (VS) is a basic engineering soil property implemented in evaluating the soil shear modulus. In many instances it may be preferable to determine VS indirectly by common in-situ tests, for instance the standard penetration test. In this paper, the relation between VS and geotechnical soil parameters such as standard penetration test blow counts (N160), effective stress and fines content, as well as overburden stress ratio is investigated. A new polynomial model is proposed to correlate geotechnical parameters and VS, predicated on a total of 620 data sets, including field investigation records for the Kocaeli (Turkey, 1999) and Chi-Chi (Taiwan, 1999) earthquakes. This study addresses the question of whether group method of data handling (GMDH) type neural networks (NN) optimized using genetic algorithms could be used to (1) estimate VS based on specified geotechnical variables, (2) assess the influence of each variable on VS. Results suggest that GMDH-type NN, in comparison to previous statistical relations, provides an effective means of efficiently recognizing the patterns in data and accurately predicting the shear wave velocity. The sensitivity analysis reveals that and fines content have significant influence on predicting VS. More... »

PAGES

829-838

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13369-012-0525-6

DOI

http://dx.doi.org/10.1007/s13369-012-0525-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050129450


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Babol Noshirvani University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.411496.f", 
          "name": [
            "Department of Civil Engineering, Babol University of Technology, Babol, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mola-Abasi", 
        "givenName": "H.", 
        "id": "sg:person.011470773747.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011470773747.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Amirkabir University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.411368.9", 
          "name": [
            "Department of Civil and Environmental Engineering, Amirkabir University Technology (Tehran Polytechnic), Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Eslami", 
        "givenName": "A.", 
        "id": "sg:person.015254444221.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015254444221.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Amirkabir University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.411368.9", 
          "name": [
            "Department of Civil and Environmental Engineering, Amirkabir University Technology (Tehran Polytechnic), Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shourijeh", 
        "givenName": "P. Tabatabaie", 
        "id": "sg:person.015110066513.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015110066513.67"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.compgeo.2008.09.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005835178"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/eqe.4290060205", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022119439"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/13632469909350352", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022360108"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enggeo.2008.09.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024121428"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.soildyn.2004.06.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025358320"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10064-006-0063-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038204910", 
          "https://doi.org/10.1007/s10064-006-0063-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10064-006-0063-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038204910", 
          "https://doi.org/10.1007/s10064-006-0063-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0304-3800(99)00099-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038767707"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2005.02.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040901370"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.soildyn.2006.11.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046972638"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.soildyn.2004.06.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051893192"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1742-2132/6/1/007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059162666"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmc.1971.4308320", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061792566"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1243/09544050360673161", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064448039"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1243/09544050360673161", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064448039"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1520/gtj10896j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067615616"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1520/jte100159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067619443"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2013-04", 
    "datePublishedReg": "2013-04-01", 
    "description": "Shear wave velocity (VS) is a basic engineering soil property implemented in evaluating the soil shear modulus. In many instances it may be preferable to determine VS indirectly by common in-situ tests, for instance the standard penetration test. In this paper, the relation between VS and geotechnical soil parameters such as standard penetration test blow counts (N160), effective stress and fines content, as well as overburden stress ratio is investigated. A new polynomial model is proposed to correlate geotechnical parameters and VS, predicated on a total of 620 data sets, including field investigation records for the Kocaeli (Turkey, 1999) and Chi-Chi (Taiwan, 1999) earthquakes. This study addresses the question of whether group method of data handling (GMDH) type neural networks (NN) optimized using genetic algorithms could be used to (1) estimate VS based on specified geotechnical variables, (2) assess the influence of each variable on VS. Results suggest that GMDH-type NN, in comparison to previous statistical relations, provides an effective means of efficiently recognizing the patterns in data and accurately predicting the shear wave velocity. The sensitivity analysis reveals that and fines content have significant influence on predicting VS.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s13369-012-0525-6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136851", 
        "issn": [
          "2193-567X", 
          "2191-4281"
        ], 
        "name": "Arabian Journal for Science and Engineering", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "38"
      }
    ], 
    "name": "Shear Wave Velocity by Polynomial Neural Networks and Genetic Algorithms Based on Geotechnical Soil Properties", 
    "pagination": "829-838", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "eecd4ca2f3b080593805fd0c9d5f1ceaad15b8c3bf3db5d6f207f0790deefbae"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s13369-012-0525-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1050129450"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s13369-012-0525-6", 
      "https://app.dimensions.ai/details/publication/pub.1050129450"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T17:34", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8672_00000524.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs13369-012-0525-6"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s13369-012-0525-6'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s13369-012-0525-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13369-012-0525-6'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13369-012-0525-6'


 

This table displays all metadata directly associated to this object as RDF triples.

124 TRIPLES      21 PREDICATES      42 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s13369-012-0525-6 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N3d355fdfbb194cdab7571476fc3a6ed4
4 schema:citation sg:pub.10.1007/s10064-006-0063-0
5 https://doi.org/10.1002/eqe.4290060205
6 https://doi.org/10.1016/j.compgeo.2008.09.003
7 https://doi.org/10.1016/j.enggeo.2008.09.006
8 https://doi.org/10.1016/j.jmatprotec.2005.02.020
9 https://doi.org/10.1016/j.soildyn.2004.06.001
10 https://doi.org/10.1016/j.soildyn.2004.06.005
11 https://doi.org/10.1016/j.soildyn.2006.11.001
12 https://doi.org/10.1016/s0304-3800(99)00099-x
13 https://doi.org/10.1080/13632469909350352
14 https://doi.org/10.1088/1742-2132/6/1/007
15 https://doi.org/10.1109/tsmc.1971.4308320
16 https://doi.org/10.1243/09544050360673161
17 https://doi.org/10.1520/gtj10896j
18 https://doi.org/10.1520/jte100159
19 schema:datePublished 2013-04
20 schema:datePublishedReg 2013-04-01
21 schema:description Shear wave velocity (VS) is a basic engineering soil property implemented in evaluating the soil shear modulus. In many instances it may be preferable to determine VS indirectly by common in-situ tests, for instance the standard penetration test. In this paper, the relation between VS and geotechnical soil parameters such as standard penetration test blow counts (N160), effective stress and fines content, as well as overburden stress ratio is investigated. A new polynomial model is proposed to correlate geotechnical parameters and VS, predicated on a total of 620 data sets, including field investigation records for the Kocaeli (Turkey, 1999) and Chi-Chi (Taiwan, 1999) earthquakes. This study addresses the question of whether group method of data handling (GMDH) type neural networks (NN) optimized using genetic algorithms could be used to (1) estimate VS based on specified geotechnical variables, (2) assess the influence of each variable on VS. Results suggest that GMDH-type NN, in comparison to previous statistical relations, provides an effective means of efficiently recognizing the patterns in data and accurately predicting the shear wave velocity. The sensitivity analysis reveals that and fines content have significant influence on predicting VS.
22 schema:genre research_article
23 schema:inLanguage en
24 schema:isAccessibleForFree false
25 schema:isPartOf Na6ac8c5108824948aab177afb8393d9e
26 Nc186cee260bc45fa952518ffa7dfb7c1
27 sg:journal.1136851
28 schema:name Shear Wave Velocity by Polynomial Neural Networks and Genetic Algorithms Based on Geotechnical Soil Properties
29 schema:pagination 829-838
30 schema:productId N024988fa668b486da80310795f016d18
31 N104fd896e9d947fe9348eff8618c05cc
32 N19a464e1bca04dedbf9286942a174ae7
33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050129450
34 https://doi.org/10.1007/s13369-012-0525-6
35 schema:sdDatePublished 2019-04-10T17:34
36 schema:sdLicense https://scigraph.springernature.com/explorer/license/
37 schema:sdPublisher N4dcc849e04504c90b08a1bfe499f4204
38 schema:url http://link.springer.com/10.1007%2Fs13369-012-0525-6
39 sgo:license sg:explorer/license/
40 sgo:sdDataset articles
41 rdf:type schema:ScholarlyArticle
42 N024988fa668b486da80310795f016d18 schema:name dimensions_id
43 schema:value pub.1050129450
44 rdf:type schema:PropertyValue
45 N08d80ea21f3d4259afd79a631561dd31 rdf:first sg:person.015110066513.67
46 rdf:rest rdf:nil
47 N102cc1d9d9e34f0b9981aac33f2379dd rdf:first sg:person.015254444221.32
48 rdf:rest N08d80ea21f3d4259afd79a631561dd31
49 N104fd896e9d947fe9348eff8618c05cc schema:name doi
50 schema:value 10.1007/s13369-012-0525-6
51 rdf:type schema:PropertyValue
52 N19a464e1bca04dedbf9286942a174ae7 schema:name readcube_id
53 schema:value eecd4ca2f3b080593805fd0c9d5f1ceaad15b8c3bf3db5d6f207f0790deefbae
54 rdf:type schema:PropertyValue
55 N3d355fdfbb194cdab7571476fc3a6ed4 rdf:first sg:person.011470773747.00
56 rdf:rest N102cc1d9d9e34f0b9981aac33f2379dd
57 N4dcc849e04504c90b08a1bfe499f4204 schema:name Springer Nature - SN SciGraph project
58 rdf:type schema:Organization
59 Na6ac8c5108824948aab177afb8393d9e schema:volumeNumber 38
60 rdf:type schema:PublicationVolume
61 Nc186cee260bc45fa952518ffa7dfb7c1 schema:issueNumber 4
62 rdf:type schema:PublicationIssue
63 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
64 schema:name Information and Computing Sciences
65 rdf:type schema:DefinedTerm
66 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
67 schema:name Artificial Intelligence and Image Processing
68 rdf:type schema:DefinedTerm
69 sg:journal.1136851 schema:issn 2191-4281
70 2193-567X
71 schema:name Arabian Journal for Science and Engineering
72 rdf:type schema:Periodical
73 sg:person.011470773747.00 schema:affiliation https://www.grid.ac/institutes/grid.411496.f
74 schema:familyName Mola-Abasi
75 schema:givenName H.
76 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011470773747.00
77 rdf:type schema:Person
78 sg:person.015110066513.67 schema:affiliation https://www.grid.ac/institutes/grid.411368.9
79 schema:familyName Shourijeh
80 schema:givenName P. Tabatabaie
81 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015110066513.67
82 rdf:type schema:Person
83 sg:person.015254444221.32 schema:affiliation https://www.grid.ac/institutes/grid.411368.9
84 schema:familyName Eslami
85 schema:givenName A.
86 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015254444221.32
87 rdf:type schema:Person
88 sg:pub.10.1007/s10064-006-0063-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038204910
89 https://doi.org/10.1007/s10064-006-0063-0
90 rdf:type schema:CreativeWork
91 https://doi.org/10.1002/eqe.4290060205 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022119439
92 rdf:type schema:CreativeWork
93 https://doi.org/10.1016/j.compgeo.2008.09.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005835178
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1016/j.enggeo.2008.09.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024121428
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1016/j.jmatprotec.2005.02.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040901370
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1016/j.soildyn.2004.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025358320
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1016/j.soildyn.2004.06.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051893192
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1016/j.soildyn.2006.11.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046972638
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1016/s0304-3800(99)00099-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1038767707
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1080/13632469909350352 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022360108
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1088/1742-2132/6/1/007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059162666
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1109/tsmc.1971.4308320 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061792566
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1243/09544050360673161 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064448039
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1520/gtj10896j schema:sameAs https://app.dimensions.ai/details/publication/pub.1067615616
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1520/jte100159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067619443
118 rdf:type schema:CreativeWork
119 https://www.grid.ac/institutes/grid.411368.9 schema:alternateName Amirkabir University of Technology
120 schema:name Department of Civil and Environmental Engineering, Amirkabir University Technology (Tehran Polytechnic), Tehran, Iran
121 rdf:type schema:Organization
122 https://www.grid.ac/institutes/grid.411496.f schema:alternateName Babol Noshirvani University of Technology
123 schema:name Department of Civil Engineering, Babol University of Technology, Babol, Iran
124 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...