Neural network nonlinear modeling for hydrogen production using anaerobic fermentation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-03

AUTHORS

Ahmed El-Shafie

ABSTRACT

The potential of utilizing artificial neural network (ANN) model approach for simulate and predict the hydrogen yield in batch model using Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) was investigated. A unique architecture has been introduced in this research to mimic the inter-relationship between three input parameters initial substrate, initial medium pH and reaction temperature (37 °C, 6.0 ± 0.2, 10), respectively, to predict hydrogen yield. Sixty data records from the experiment have been utilized to develop the ANN model. The results showed that the proposed ANN model provided significant level of accuracy for prediction with maximum error (10 %). Furthermore, a comparative analysis with a traditional approach Box–Wilson design (BWD) has proved that the ANN model output significantly outperformed the BWD. ANN model overcomes the limitation of the BWD approach with respect to the number of records, which is merely considering limited length of stochastic pattern for hydrogen yield (15 records). More... »

PAGES

539-547

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00521-012-1268-8

DOI

http://dx.doi.org/10.1007/s00521-012-1268-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025339590


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": "National University of Malaysia", 
          "id": "https://www.grid.ac/institutes/grid.412113.4", 
          "name": [
            "Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Malaysia, Malaysia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "El-Shafie", 
        "givenName": "Ahmed", 
        "id": "sg:person.016526360001.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016526360001.43"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s1364-8152(99)00007-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002751584"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.fuel.2006.01.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008271377"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biortech.2007.05.055", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010283482"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-010-0340-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010896068", 
          "https://doi.org/10.1007/s00521-010-0340-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-010-0340-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010896068", 
          "https://doi.org/10.1007/s00521-010-0340-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0360-3199(02)00074-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011160545"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0360-3199(02)00074-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011160545"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/089976699300016223", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013517689"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijhydene.2008.09.066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014369724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11814-008-0212-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015094684", 
          "https://doi.org/10.1007/s11814-008-0212-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.inffus.2010.01.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015789554"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1623/hysj.52.3.414", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018537084"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3923/pjbs.2003.1273.1275", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019169353"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/bit.260281106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022059977"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-1694(92)90046-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023807980"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-1694(92)90046-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023807980"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0360-3199(97)00080-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023956639"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/bp980042f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026310893"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-009-0318-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027913692", 
          "https://doi.org/10.1007/s00521-009-0318-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-009-0318-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027913692", 
          "https://doi.org/10.1007/s00521-009-0318-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-009-0318-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027913692", 
          "https://doi.org/10.1007/s00521-009-0318-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00253-006-0647-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029039689", 
          "https://doi.org/10.1007/s00253-006-0647-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00253-006-0647-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029039689", 
          "https://doi.org/10.1007/s00253-006-0647-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biombioe.2008.02.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031787458"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijhydene.2008.07.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032122474"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3923/jas.2008.4487.4499", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035233404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijhydene.2008.03.048", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035488332"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0360-3199(81)90041-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035590484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-010-0486-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038512837", 
          "https://doi.org/10.1007/s00521-010-0486-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-010-0486-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038512837", 
          "https://doi.org/10.1007/s00521-010-0486-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3923/pjbs.2009.1462.1467", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044771127"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/es048569d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046034848"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/es048569d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046034848"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijhydene.2008.10.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047253026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijhydene.2003.11.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049783422"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/5.58343", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061179732"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3844/ajessp.2009.80.86", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071458366"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511812651", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098665985"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-03", 
    "datePublishedReg": "2014-03-01", 
    "description": "The potential of utilizing artificial neural network (ANN) model approach for simulate and predict the hydrogen yield in batch model using Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) was investigated. A unique architecture has been introduced in this research to mimic the inter-relationship between three input parameters initial substrate, initial medium pH and reaction temperature (37 \u00b0C, 6.0 \u00b1 0.2, 10), respectively, to predict hydrogen yield. Sixty data records from the experiment have been utilized to develop the ANN model. The results showed that the proposed ANN model provided significant level of accuracy for prediction with maximum error (10 %). Furthermore, a comparative analysis with a traditional approach Box\u2013Wilson design (BWD) has proved that the ANN model output significantly outperformed the BWD. ANN model overcomes the limitation of the BWD approach with respect to the number of records, which is merely considering limited length of stochastic pattern for hydrogen yield (15 records).", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00521-012-1268-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1104357", 
        "issn": [
          "0941-0643", 
          "1433-3058"
        ], 
        "name": "Neural Computing and Applications", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3-4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "24"
      }
    ], 
    "name": "Neural network nonlinear modeling for hydrogen production using anaerobic fermentation", 
    "pagination": "539-547", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "78b7ffa586b6d5ff9c7f190e5cd4689d1a8275e25ba840b61a87fd366e3bada3"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00521-012-1268-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1025339590"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00521-012-1268-8", 
      "https://app.dimensions.ai/details/publication/pub.1025339590"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T00:16", 
    "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_8695_00000512.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs00521-012-1268-8"
  }
]
 

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/s00521-012-1268-8'

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/s00521-012-1268-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00521-012-1268-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00521-012-1268-8'


 

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

156 TRIPLES      21 PREDICATES      57 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00521-012-1268-8 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nfc982b9e3f6b4bdb8aa4c99d62410bac
4 schema:citation sg:pub.10.1007/s00253-006-0647-4
5 sg:pub.10.1007/s00521-009-0318-3
6 sg:pub.10.1007/s00521-010-0340-5
7 sg:pub.10.1007/s00521-010-0486-1
8 sg:pub.10.1007/s11814-008-0212-1
9 https://doi.org/10.1002/bit.260281106
10 https://doi.org/10.1016/0022-1694(92)90046-x
11 https://doi.org/10.1016/0360-3199(81)90041-0
12 https://doi.org/10.1016/j.biombioe.2008.02.005
13 https://doi.org/10.1016/j.biortech.2007.05.055
14 https://doi.org/10.1016/j.fuel.2006.01.021
15 https://doi.org/10.1016/j.ijhydene.2003.11.002
16 https://doi.org/10.1016/j.ijhydene.2008.03.048
17 https://doi.org/10.1016/j.ijhydene.2008.07.010
18 https://doi.org/10.1016/j.ijhydene.2008.09.066
19 https://doi.org/10.1016/j.ijhydene.2008.10.010
20 https://doi.org/10.1016/j.inffus.2010.01.003
21 https://doi.org/10.1016/s0360-3199(02)00074-5
22 https://doi.org/10.1016/s0360-3199(97)00080-3
23 https://doi.org/10.1016/s1364-8152(99)00007-9
24 https://doi.org/10.1017/cbo9780511812651
25 https://doi.org/10.1021/bp980042f
26 https://doi.org/10.1021/es048569d
27 https://doi.org/10.1109/5.58343
28 https://doi.org/10.1162/089976699300016223
29 https://doi.org/10.1623/hysj.52.3.414
30 https://doi.org/10.3844/ajessp.2009.80.86
31 https://doi.org/10.3923/jas.2008.4487.4499
32 https://doi.org/10.3923/pjbs.2003.1273.1275
33 https://doi.org/10.3923/pjbs.2009.1462.1467
34 schema:datePublished 2014-03
35 schema:datePublishedReg 2014-03-01
36 schema:description The potential of utilizing artificial neural network (ANN) model approach for simulate and predict the hydrogen yield in batch model using Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) was investigated. A unique architecture has been introduced in this research to mimic the inter-relationship between three input parameters initial substrate, initial medium pH and reaction temperature (37 °C, 6.0 ± 0.2, 10), respectively, to predict hydrogen yield. Sixty data records from the experiment have been utilized to develop the ANN model. The results showed that the proposed ANN model provided significant level of accuracy for prediction with maximum error (10 %). Furthermore, a comparative analysis with a traditional approach Box–Wilson design (BWD) has proved that the ANN model output significantly outperformed the BWD. ANN model overcomes the limitation of the BWD approach with respect to the number of records, which is merely considering limited length of stochastic pattern for hydrogen yield (15 records).
37 schema:genre research_article
38 schema:inLanguage en
39 schema:isAccessibleForFree false
40 schema:isPartOf Naa5230eb7bb54356aef24a080b87c96b
41 Nb74eb8e3aaac4919a2861a640da5ba02
42 sg:journal.1104357
43 schema:name Neural network nonlinear modeling for hydrogen production using anaerobic fermentation
44 schema:pagination 539-547
45 schema:productId N1490ad6638b6415ea9135b3a907059ab
46 N823f47d67bef49eb81db9ccfc85983e4
47 N87d598d100144ee3aa9d56bb0b2dd2dd
48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025339590
49 https://doi.org/10.1007/s00521-012-1268-8
50 schema:sdDatePublished 2019-04-11T00:16
51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
52 schema:sdPublisher N463278b8629c4a11800ab8264d3228e0
53 schema:url http://link.springer.com/10.1007%2Fs00521-012-1268-8
54 sgo:license sg:explorer/license/
55 sgo:sdDataset articles
56 rdf:type schema:ScholarlyArticle
57 N1490ad6638b6415ea9135b3a907059ab schema:name dimensions_id
58 schema:value pub.1025339590
59 rdf:type schema:PropertyValue
60 N463278b8629c4a11800ab8264d3228e0 schema:name Springer Nature - SN SciGraph project
61 rdf:type schema:Organization
62 N823f47d67bef49eb81db9ccfc85983e4 schema:name doi
63 schema:value 10.1007/s00521-012-1268-8
64 rdf:type schema:PropertyValue
65 N87d598d100144ee3aa9d56bb0b2dd2dd schema:name readcube_id
66 schema:value 78b7ffa586b6d5ff9c7f190e5cd4689d1a8275e25ba840b61a87fd366e3bada3
67 rdf:type schema:PropertyValue
68 Naa5230eb7bb54356aef24a080b87c96b schema:volumeNumber 24
69 rdf:type schema:PublicationVolume
70 Nb74eb8e3aaac4919a2861a640da5ba02 schema:issueNumber 3-4
71 rdf:type schema:PublicationIssue
72 Nfc982b9e3f6b4bdb8aa4c99d62410bac rdf:first sg:person.016526360001.43
73 rdf:rest rdf:nil
74 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
75 schema:name Information and Computing Sciences
76 rdf:type schema:DefinedTerm
77 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
78 schema:name Artificial Intelligence and Image Processing
79 rdf:type schema:DefinedTerm
80 sg:journal.1104357 schema:issn 0941-0643
81 1433-3058
82 schema:name Neural Computing and Applications
83 rdf:type schema:Periodical
84 sg:person.016526360001.43 schema:affiliation https://www.grid.ac/institutes/grid.412113.4
85 schema:familyName El-Shafie
86 schema:givenName Ahmed
87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016526360001.43
88 rdf:type schema:Person
89 sg:pub.10.1007/s00253-006-0647-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029039689
90 https://doi.org/10.1007/s00253-006-0647-4
91 rdf:type schema:CreativeWork
92 sg:pub.10.1007/s00521-009-0318-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027913692
93 https://doi.org/10.1007/s00521-009-0318-3
94 rdf:type schema:CreativeWork
95 sg:pub.10.1007/s00521-010-0340-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010896068
96 https://doi.org/10.1007/s00521-010-0340-5
97 rdf:type schema:CreativeWork
98 sg:pub.10.1007/s00521-010-0486-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038512837
99 https://doi.org/10.1007/s00521-010-0486-1
100 rdf:type schema:CreativeWork
101 sg:pub.10.1007/s11814-008-0212-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015094684
102 https://doi.org/10.1007/s11814-008-0212-1
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1002/bit.260281106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022059977
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1016/0022-1694(92)90046-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1023807980
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1016/0360-3199(81)90041-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035590484
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1016/j.biombioe.2008.02.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031787458
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1016/j.biortech.2007.05.055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010283482
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/j.fuel.2006.01.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008271377
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/j.ijhydene.2003.11.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049783422
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.ijhydene.2008.03.048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035488332
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/j.ijhydene.2008.07.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032122474
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/j.ijhydene.2008.09.066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014369724
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/j.ijhydene.2008.10.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047253026
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/j.inffus.2010.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015789554
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/s0360-3199(02)00074-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011160545
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/s0360-3199(97)00080-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023956639
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/s1364-8152(99)00007-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002751584
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1017/cbo9780511812651 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098665985
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1021/bp980042f schema:sameAs https://app.dimensions.ai/details/publication/pub.1026310893
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1021/es048569d schema:sameAs https://app.dimensions.ai/details/publication/pub.1046034848
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1109/5.58343 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061179732
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1162/089976699300016223 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013517689
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1623/hysj.52.3.414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018537084
145 rdf:type schema:CreativeWork
146 https://doi.org/10.3844/ajessp.2009.80.86 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071458366
147 rdf:type schema:CreativeWork
148 https://doi.org/10.3923/jas.2008.4487.4499 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035233404
149 rdf:type schema:CreativeWork
150 https://doi.org/10.3923/pjbs.2003.1273.1275 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019169353
151 rdf:type schema:CreativeWork
152 https://doi.org/10.3923/pjbs.2009.1462.1467 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044771127
153 rdf:type schema:CreativeWork
154 https://www.grid.ac/institutes/grid.412113.4 schema:alternateName National University of Malaysia
155 schema:name Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Malaysia, Malaysia
156 rdf:type schema:Organization
 




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


...