Prediction of zeolite-cement-sand unconfined compressive strength using polynomial neural network View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-04

AUTHORS

H. MolaAbasi, I. Shooshpasha

ABSTRACT

The improvement of local soils with cement and zeolite can provide great benefits, including strengthening slopes in slope stability problems, stabilizing problematic soils and preventing soil liquefaction. Recently, dosage methodologies are being developed for improved soils based on a rational criterion as it exists in concrete technology. There are numerous earlier studies showing the possibility of relating Unconfined Compressive Strength (UCS) and Cemented sand (CS) parameters (voids/cement ratio) as a power function fits. Taking into account the fact that the existing equations are incapable of estimating UCS for zeolite cemented sand mixture (ZCS) well, artificial intelligence methods are used for forecasting them. Polynomial-type neural network is applied to estimate the UCS from more simply determined index properties such as zeolite and cement content, porosity as well as curing time. In order to assess the merits of the proposed approach, a total number of 216 unconfined compressive tests have been done. A comparison is carried out between the experimentally measured UCS with the predictions in order to evaluate the performance of the current method. The results demonstrate that generalized polynomial-type neural network has a great ability for prediction of the UCS. At the end sensitivity analysis of the polynomial model is applied to study the influence of input parameters on model output. The sensitivity analysis reveals that cement and zeolite content have significant influence on predicting UCS. More... »

PAGES

108

Identifiers

URI

http://scigraph.springernature.com/pub.10.1140/epjp/i2016-16108-5

DOI

http://dx.doi.org/10.1140/epjp/i2016-16108-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033270626


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/2008/0905", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Civil Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Babol Noshirvani University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.411496.f", 
          "name": [
            "Geotechnical Department, Babol University of Technology (BUT), Babol, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "MolaAbasi", 
        "givenName": "H.", 
        "id": "sg:person.014277150135.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014277150135.83"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Babol Noshirvani University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.411496.f", 
          "name": [
            "Geotechnical Department, Babol University of Technology (BUT), Babol, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shooshpasha", 
        "givenName": "I.", 
        "id": "sg:person.012136755264.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012136755264.33"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.conbuildmat.2013.08.062", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002318801"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0950-0618(99)00048-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002500896"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enggeo.2014.11.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003323626"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compgeo.2006.03.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003669170"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.engappai.2014.03.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005383256"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "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/10.1016/s0008-8846(03)00063-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013352961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0008-8846(03)00063-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013352961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1139/t05-069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016050680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.powtec.2015.07.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017560038"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.geotexmem.2008.11.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021487652"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.geotexmem.2013.07.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022415489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.conbuildmat.2010.09.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022816729"
        ], 
        "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.buildenv.2006.11.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035861904"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sandf.2015.10.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036554254"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1515/eng-2015-0011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037794650"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.engappai.2008.11.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038502280"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0008-8846(98)00165-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039700072"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1387-1811(03)00369-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042400918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1387-1811(03)00369-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042400918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.geotexmem.2015.02.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050047392"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)0733-9410(1995)121:5(429)", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057588051"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)0887-3801(2000)14:1(1)", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057609284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)0899-1561(2009)21:5(210)", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057612792"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)1084-0699(1999)4:3(232)", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057615878"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)1090-0241(1998)124:12(1211)", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057617867"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)1090-0241(2007)133:2(197)", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057619407"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)gt.1943-5606.0001296", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057633265"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)mt.1943-5533.0001110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057640125"
        ], 
        "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/cca10273j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067613308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1680/geng.2009.162.2.111", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068207834"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1680/geolett.13.00081", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068208105"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1680/geot.1993.43.1.53", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068210528"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1680/geot.2000.50.1.99", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068210996"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1680/geot.2006.56.1.69", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068211574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1680/geot.2008.58.8.675", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068211872"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-04", 
    "datePublishedReg": "2016-04-01", 
    "description": "The improvement of local soils with cement and zeolite can provide great benefits, including strengthening slopes in slope stability problems, stabilizing problematic soils and preventing soil liquefaction. Recently, dosage methodologies are being developed for improved soils based on a rational criterion as it exists in concrete technology. There are numerous earlier studies showing the possibility of relating Unconfined Compressive Strength (UCS) and Cemented sand (CS) parameters (voids/cement ratio) as a power function fits. Taking into account the fact that the existing equations are incapable of estimating UCS for zeolite cemented sand mixture (ZCS) well, artificial intelligence methods are used for forecasting them. Polynomial-type neural network is applied to estimate the UCS from more simply determined index properties such as zeolite and cement content, porosity as well as curing time. In order to assess the merits of the proposed approach, a total number of 216 unconfined compressive tests have been done. A comparison is carried out between the experimentally measured UCS with the predictions in order to evaluate the performance of the current method. The results demonstrate that generalized polynomial-type neural network has a great ability for prediction of the UCS. At the end sensitivity analysis of the polynomial model is applied to study the influence of input parameters on model output. The sensitivity analysis reveals that cement and zeolite content have significant influence on predicting UCS.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1140/epjp/i2016-16108-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1052877", 
        "issn": [
          "2190-5444"
        ], 
        "name": "The European Physical Journal Plus", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "131"
      }
    ], 
    "name": "Prediction of zeolite-cement-sand unconfined compressive strength using polynomial neural network", 
    "pagination": "108", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "9e7f24b9d1b96d129835db4f5749074eeb812f5dd7f645e1b20f7410c3e265d7"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1140/epjp/i2016-16108-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1033270626"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1140/epjp/i2016-16108-5", 
      "https://app.dimensions.ai/details/publication/pub.1033270626"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T16:41", 
    "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_8669_00000506.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1140%2Fepjp%2Fi2016-16108-5"
  }
]
 

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.10.1140/epjp/i2016-16108-5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1140/epjp/i2016-16108-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1140/epjp/i2016-16108-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1140/epjp/i2016-16108-5'


 

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

175 TRIPLES      21 PREDICATES      63 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1140/epjp/i2016-16108-5 schema:about anzsrc-for:09
2 anzsrc-for:0905
3 schema:author Ne46f6edd8ff6480987e46f7d18c9241a
4 schema:citation https://doi.org/10.1016/j.buildenv.2006.11.006
5 https://doi.org/10.1016/j.compgeo.2006.03.006
6 https://doi.org/10.1016/j.compgeo.2008.09.003
7 https://doi.org/10.1016/j.conbuildmat.2010.09.027
8 https://doi.org/10.1016/j.conbuildmat.2013.08.062
9 https://doi.org/10.1016/j.engappai.2008.11.005
10 https://doi.org/10.1016/j.engappai.2014.03.012
11 https://doi.org/10.1016/j.enggeo.2008.09.006
12 https://doi.org/10.1016/j.enggeo.2014.11.015
13 https://doi.org/10.1016/j.geotexmem.2008.11.005
14 https://doi.org/10.1016/j.geotexmem.2013.07.010
15 https://doi.org/10.1016/j.geotexmem.2015.02.004
16 https://doi.org/10.1016/j.powtec.2015.07.026
17 https://doi.org/10.1016/j.sandf.2015.10.001
18 https://doi.org/10.1016/s0008-8846(03)00063-2
19 https://doi.org/10.1016/s0008-8846(98)00165-3
20 https://doi.org/10.1016/s0950-0618(99)00048-3
21 https://doi.org/10.1016/s1387-1811(03)00369-x
22 https://doi.org/10.1061/(asce)0733-9410(1995)121:5(429)
23 https://doi.org/10.1061/(asce)0887-3801(2000)14:1(1)
24 https://doi.org/10.1061/(asce)0899-1561(2009)21:5(210)
25 https://doi.org/10.1061/(asce)1084-0699(1999)4:3(232)
26 https://doi.org/10.1061/(asce)1090-0241(1998)124:12(1211)
27 https://doi.org/10.1061/(asce)1090-0241(2007)133:2(197)
28 https://doi.org/10.1061/(asce)gt.1943-5606.0001296
29 https://doi.org/10.1061/(asce)mt.1943-5533.0001110
30 https://doi.org/10.1139/t05-069
31 https://doi.org/10.1243/09544050360673161
32 https://doi.org/10.1515/eng-2015-0011
33 https://doi.org/10.1520/cca10273j
34 https://doi.org/10.1680/geng.2009.162.2.111
35 https://doi.org/10.1680/geolett.13.00081
36 https://doi.org/10.1680/geot.1993.43.1.53
37 https://doi.org/10.1680/geot.2000.50.1.99
38 https://doi.org/10.1680/geot.2006.56.1.69
39 https://doi.org/10.1680/geot.2008.58.8.675
40 schema:datePublished 2016-04
41 schema:datePublishedReg 2016-04-01
42 schema:description The improvement of local soils with cement and zeolite can provide great benefits, including strengthening slopes in slope stability problems, stabilizing problematic soils and preventing soil liquefaction. Recently, dosage methodologies are being developed for improved soils based on a rational criterion as it exists in concrete technology. There are numerous earlier studies showing the possibility of relating Unconfined Compressive Strength (UCS) and Cemented sand (CS) parameters (voids/cement ratio) as a power function fits. Taking into account the fact that the existing equations are incapable of estimating UCS for zeolite cemented sand mixture (ZCS) well, artificial intelligence methods are used for forecasting them. Polynomial-type neural network is applied to estimate the UCS from more simply determined index properties such as zeolite and cement content, porosity as well as curing time. In order to assess the merits of the proposed approach, a total number of 216 unconfined compressive tests have been done. A comparison is carried out between the experimentally measured UCS with the predictions in order to evaluate the performance of the current method. The results demonstrate that generalized polynomial-type neural network has a great ability for prediction of the UCS. At the end sensitivity analysis of the polynomial model is applied to study the influence of input parameters on model output. The sensitivity analysis reveals that cement and zeolite content have significant influence on predicting UCS.
43 schema:genre research_article
44 schema:inLanguage en
45 schema:isAccessibleForFree false
46 schema:isPartOf N0b6e4df05e6f4304ad9a35b7c57e3eac
47 Nd9a2ecfe1dba4f689371e2d559f0c1e7
48 sg:journal.1052877
49 schema:name Prediction of zeolite-cement-sand unconfined compressive strength using polynomial neural network
50 schema:pagination 108
51 schema:productId N5d55e877e16b4d8d869af75661d77e50
52 N82687823ef554ea4af237c0bc7422ab1
53 Nafbaf5e931574c1e84ba3c2dcbe94848
54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033270626
55 https://doi.org/10.1140/epjp/i2016-16108-5
56 schema:sdDatePublished 2019-04-10T16:41
57 schema:sdLicense https://scigraph.springernature.com/explorer/license/
58 schema:sdPublisher Nbcd0c830bc86444abf200a829d160709
59 schema:url http://link.springer.com/10.1140%2Fepjp%2Fi2016-16108-5
60 sgo:license sg:explorer/license/
61 sgo:sdDataset articles
62 rdf:type schema:ScholarlyArticle
63 N0b6e4df05e6f4304ad9a35b7c57e3eac schema:issueNumber 4
64 rdf:type schema:PublicationIssue
65 N5d55e877e16b4d8d869af75661d77e50 schema:name readcube_id
66 schema:value 9e7f24b9d1b96d129835db4f5749074eeb812f5dd7f645e1b20f7410c3e265d7
67 rdf:type schema:PropertyValue
68 N82687823ef554ea4af237c0bc7422ab1 schema:name dimensions_id
69 schema:value pub.1033270626
70 rdf:type schema:PropertyValue
71 Nafbaf5e931574c1e84ba3c2dcbe94848 schema:name doi
72 schema:value 10.1140/epjp/i2016-16108-5
73 rdf:type schema:PropertyValue
74 Nbcd0c830bc86444abf200a829d160709 schema:name Springer Nature - SN SciGraph project
75 rdf:type schema:Organization
76 Nd9a2ecfe1dba4f689371e2d559f0c1e7 schema:volumeNumber 131
77 rdf:type schema:PublicationVolume
78 Ne46f6edd8ff6480987e46f7d18c9241a rdf:first sg:person.014277150135.83
79 rdf:rest Ne9811c7ed0e4472abb2b75a0db2f7314
80 Ne9811c7ed0e4472abb2b75a0db2f7314 rdf:first sg:person.012136755264.33
81 rdf:rest rdf:nil
82 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
83 schema:name Engineering
84 rdf:type schema:DefinedTerm
85 anzsrc-for:0905 schema:inDefinedTermSet anzsrc-for:
86 schema:name Civil Engineering
87 rdf:type schema:DefinedTerm
88 sg:journal.1052877 schema:issn 2190-5444
89 schema:name The European Physical Journal Plus
90 rdf:type schema:Periodical
91 sg:person.012136755264.33 schema:affiliation https://www.grid.ac/institutes/grid.411496.f
92 schema:familyName Shooshpasha
93 schema:givenName I.
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012136755264.33
95 rdf:type schema:Person
96 sg:person.014277150135.83 schema:affiliation https://www.grid.ac/institutes/grid.411496.f
97 schema:familyName MolaAbasi
98 schema:givenName H.
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014277150135.83
100 rdf:type schema:Person
101 https://doi.org/10.1016/j.buildenv.2006.11.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035861904
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1016/j.compgeo.2006.03.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003669170
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1016/j.compgeo.2008.09.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005835178
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1016/j.conbuildmat.2010.09.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022816729
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/j.conbuildmat.2013.08.062 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002318801
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/j.engappai.2008.11.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038502280
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1016/j.engappai.2014.03.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005383256
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1016/j.enggeo.2008.09.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024121428
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1016/j.enggeo.2014.11.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003323626
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1016/j.geotexmem.2008.11.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021487652
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/j.geotexmem.2013.07.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022415489
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/j.geotexmem.2015.02.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050047392
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.powtec.2015.07.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017560038
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/j.sandf.2015.10.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036554254
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/s0008-8846(03)00063-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013352961
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/s0008-8846(98)00165-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039700072
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/s0950-0618(99)00048-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002500896
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/s1387-1811(03)00369-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1042400918
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1061/(asce)0733-9410(1995)121:5(429) schema:sameAs https://app.dimensions.ai/details/publication/pub.1057588051
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1061/(asce)0887-3801(2000)14:1(1) schema:sameAs https://app.dimensions.ai/details/publication/pub.1057609284
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1061/(asce)0899-1561(2009)21:5(210) schema:sameAs https://app.dimensions.ai/details/publication/pub.1057612792
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1061/(asce)1084-0699(1999)4:3(232) schema:sameAs https://app.dimensions.ai/details/publication/pub.1057615878
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1061/(asce)1090-0241(1998)124:12(1211) schema:sameAs https://app.dimensions.ai/details/publication/pub.1057617867
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1061/(asce)1090-0241(2007)133:2(197) schema:sameAs https://app.dimensions.ai/details/publication/pub.1057619407
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1061/(asce)gt.1943-5606.0001296 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057633265
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1061/(asce)mt.1943-5533.0001110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057640125
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1139/t05-069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016050680
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1243/09544050360673161 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064448039
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1515/eng-2015-0011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037794650
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1520/cca10273j schema:sameAs https://app.dimensions.ai/details/publication/pub.1067613308
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1680/geng.2009.162.2.111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068207834
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1680/geolett.13.00081 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068208105
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1680/geot.1993.43.1.53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068210528
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1680/geot.2000.50.1.99 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068210996
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1680/geot.2006.56.1.69 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068211574
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1680/geot.2008.58.8.675 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068211872
172 rdf:type schema:CreativeWork
173 https://www.grid.ac/institutes/grid.411496.f schema:alternateName Babol Noshirvani University of Technology
174 schema:name Geotechnical Department, Babol University of Technology (BUT), Babol, Iran
175 rdf:type schema:Organization
 




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


...