Layout optimization of long-span structures subject to self-weight and multiple load-cases View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-06-30

AUTHORS

Helen E. Fairclough, Matthew Gilbert

ABSTRACT

Layout optimization provides a powerful means of identifying materially efficient structures. It has the potential to be particularly valuable when long-span structures are involved, since self-weight represents a significant proportion of the overall loading. However, previously proposed numerical layout optimization methods neglect or make non-conservative approximations in their modelling of self-weight and/or multiple load-cases. Combining these effects presents challenges that are not encountered when they are considered separately. In this paper, three formulations are presented to address this. One formulation makes use of equal stress catenary elements, whilst the other two make use of elements with bending resistance. Strengths and weaknesses of each formulation are discussed. Finally, an approach that combines formulations is proposed to more closely model real-world behaviour and to reduce computational expense. The efficacy of this approach is demonstrated through application to a number of 2D- and 3D-structural design problems. More... »

PAGES

197

References to SciGraph publications

  • 2015-07-10. Rationalization of trusses generated via layout optimization in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2015-07-10. Optimum structure for a uniform load over multiple spans in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2011-09-09. Singular optimum topology of skeletal structures with frequency constraints by AGGA in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2018-03-09. Theoretically optimal bracing for pre-existing building frames in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2012-02-11. On the optimality of Hemp’s arch with vertical hangers in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2020-03-11. Layout optimization of simplified trusses using mixed integer linear programming with runtime generation of constraints in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2010-01-07. Optimum structure to carry a uniform load between pinned supports in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2015-04-24. On the layout of a least weight multiple span structure with uniform load in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2018-03-28. Design of optimum grillages using layout optimization in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2013-08-21. Topology optimization approaches in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2022-01-25. Layout optimization of structures with distributed self-weight, lumped masses and frictional supports in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2020-06-01. Closing the gap towards super-long suspension bridges using computational morphogenesis in NATURE COMMUNICATIONS
  • 2013-07-02. A mixed integer programming approach to designing periodic frame structures with negative Poisson’s ratio in OPTIMIZATION AND ENGINEERING
  • 1994-04. Optimization methods for truss geometry and topology design in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2017-08-02. Mixed-integer linear programming approach for global discrete sizing optimization of frame structures in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 1975. Symmetric Plane Frameworks of Least Weight in OPTIMIZATION IN STRUCTURAL DESIGN
  • 2017-01-25. A note on truss topology optimization under self-weight load: mixed-integer second-order cone programming approach in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00158-022-03242-9

    DOI

    http://dx.doi.org/10.1007/s00158-022-03242-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1149109948


    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/2008/09", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Engineering", 
            "type": "DefinedTerm"
          }, 
          {
            "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"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Civil and Structural Engineering, The University of Sheffield, Mappin St, S1 3JD, Sheffield, UK", 
              "id": "http://www.grid.ac/institutes/grid.11835.3e", 
              "name": [
                "Department of Civil and Structural Engineering, The University of Sheffield, Mappin St, S1 3JD, Sheffield, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fairclough", 
            "givenName": "Helen E.", 
            "id": "sg:person.013303436110.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013303436110.46"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Civil and Structural Engineering, The University of Sheffield, Mappin St, S1 3JD, Sheffield, UK", 
              "id": "http://www.grid.ac/institutes/grid.11835.3e", 
              "name": [
                "Department of Civil and Structural Engineering, The University of Sheffield, Mappin St, S1 3JD, Sheffield, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gilbert", 
            "givenName": "Matthew", 
            "id": "sg:person.012624304756.31", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012624304756.31"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00158-012-0769-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005810101", 
              "https://doi.org/10.1007/s00158-012-0769-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-017-1657-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1074194514", 
              "https://doi.org/10.1007/s00158-017-1657-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf01742459", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003622105", 
              "https://doi.org/10.1007/bf01742459"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-019-02449-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1125558752", 
              "https://doi.org/10.1007/s00158-019-02449-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-018-1921-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101398725", 
              "https://doi.org/10.1007/s00158-018-1921-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-015-1248-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041702906", 
              "https://doi.org/10.1007/s00158-015-1248-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-009-0467-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040561196", 
              "https://doi.org/10.1007/s00158-009-0467-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11081-013-9225-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033464626", 
              "https://doi.org/10.1007/s11081-013-9225-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-013-0978-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044638290", 
              "https://doi.org/10.1007/s00158-013-0978-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-018-1930-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101828224", 
              "https://doi.org/10.1007/s00158-018-1930-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-015-1260-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012916313", 
              "https://doi.org/10.1007/s00158-015-1260-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-020-16599-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1128096817", 
              "https://doi.org/10.1038/s41467-020-16599-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-011-0708-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010820285", 
              "https://doi.org/10.1007/s00158-011-0708-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-80895-1_23", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013561283", 
              "https://doi.org/10.1007/978-3-642-80895-1_23"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-015-1278-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009497359", 
              "https://doi.org/10.1007/s00158-015-1278-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-021-03139-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1144977935", 
              "https://doi.org/10.1007/s00158-021-03139-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-017-1770-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090952859", 
              "https://doi.org/10.1007/s00158-017-1770-9"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-06-30", 
        "datePublishedReg": "2022-06-30", 
        "description": "Layout optimization provides a powerful means of identifying materially efficient structures. It has the potential to be particularly valuable when long-span structures are involved, since self-weight represents a significant proportion of the overall loading. However, previously proposed numerical layout optimization methods neglect or make non-conservative approximations in their modelling of self-weight and/or multiple load-cases. Combining these effects presents challenges that are not encountered when they are considered separately. In this paper, three formulations are presented to address this. One formulation makes use of equal stress catenary elements, whilst the other two make use of elements with bending resistance. Strengths and weaknesses of each formulation are discussed. Finally, an approach that combines formulations is proposed to more closely model real-world behaviour and to reduce computational expense. The efficacy of this approach is demonstrated through application to a number of 2D- and 3D-structural design problems.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s00158-022-03242-9", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1050630", 
            "issn": [
              "1615-147X", 
              "1615-1488"
            ], 
            "name": "Structural and Multidisciplinary Optimization", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "7", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "65"
          }
        ], 
        "keywords": [
          "long-span structures", 
          "layout optimization method", 
          "catenary element", 
          "overall loading", 
          "layout optimization", 
          "optimization method", 
          "efficient structure", 
          "design problem", 
          "computational expense", 
          "optimization", 
          "formulation", 
          "loading", 
          "structure", 
          "make use", 
          "strength", 
          "modelling", 
          "elements", 
          "applications", 
          "resistance", 
          "behavior", 
          "approach", 
          "real-world behavior", 
          "method", 
          "use", 
          "problem", 
          "powerful means", 
          "approximation", 
          "potential", 
          "means", 
          "effect", 
          "expense", 
          "challenges", 
          "number", 
          "proportion", 
          "weakness", 
          "significant proportion", 
          "efficacy", 
          "non-conservative approximations", 
          "paper"
        ], 
        "name": "Layout optimization of long-span structures subject to self-weight and multiple load-cases", 
        "pagination": "197", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1149109948"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00158-022-03242-9"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00158-022-03242-9", 
          "https://app.dimensions.ai/details/publication/pub.1149109948"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:49", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_935.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s00158-022-03242-9"
      }
    ]
     

    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/s00158-022-03242-9'

    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/s00158-022-03242-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00158-022-03242-9'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00158-022-03242-9'


     

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

    171 TRIPLES      21 PREDICATES      80 URIs      55 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00158-022-03242-9 schema:about anzsrc-for:09
    2 anzsrc-for:0905
    3 schema:author Nca5ca863a76b4dfc9b122f39bd771dc2
    4 schema:citation sg:pub.10.1007/978-3-642-80895-1_23
    5 sg:pub.10.1007/bf01742459
    6 sg:pub.10.1007/s00158-009-0467-0
    7 sg:pub.10.1007/s00158-011-0708-x
    8 sg:pub.10.1007/s00158-012-0769-5
    9 sg:pub.10.1007/s00158-013-0978-6
    10 sg:pub.10.1007/s00158-015-1248-6
    11 sg:pub.10.1007/s00158-015-1260-x
    12 sg:pub.10.1007/s00158-015-1278-0
    13 sg:pub.10.1007/s00158-017-1657-9
    14 sg:pub.10.1007/s00158-017-1770-9
    15 sg:pub.10.1007/s00158-018-1921-7
    16 sg:pub.10.1007/s00158-018-1930-6
    17 sg:pub.10.1007/s00158-019-02449-7
    18 sg:pub.10.1007/s00158-021-03139-z
    19 sg:pub.10.1007/s11081-013-9225-7
    20 sg:pub.10.1038/s41467-020-16599-6
    21 schema:datePublished 2022-06-30
    22 schema:datePublishedReg 2022-06-30
    23 schema:description Layout optimization provides a powerful means of identifying materially efficient structures. It has the potential to be particularly valuable when long-span structures are involved, since self-weight represents a significant proportion of the overall loading. However, previously proposed numerical layout optimization methods neglect or make non-conservative approximations in their modelling of self-weight and/or multiple load-cases. Combining these effects presents challenges that are not encountered when they are considered separately. In this paper, three formulations are presented to address this. One formulation makes use of equal stress catenary elements, whilst the other two make use of elements with bending resistance. Strengths and weaknesses of each formulation are discussed. Finally, an approach that combines formulations is proposed to more closely model real-world behaviour and to reduce computational expense. The efficacy of this approach is demonstrated through application to a number of 2D- and 3D-structural design problems.
    24 schema:genre article
    25 schema:isAccessibleForFree true
    26 schema:isPartOf Naad2ba96cc9345bfab5ebd66a0edc636
    27 Nc81a7e2466164afcbc8e07a282a23cf1
    28 sg:journal.1050630
    29 schema:keywords applications
    30 approach
    31 approximation
    32 behavior
    33 catenary element
    34 challenges
    35 computational expense
    36 design problem
    37 effect
    38 efficacy
    39 efficient structure
    40 elements
    41 expense
    42 formulation
    43 layout optimization
    44 layout optimization method
    45 loading
    46 long-span structures
    47 make use
    48 means
    49 method
    50 modelling
    51 non-conservative approximations
    52 number
    53 optimization
    54 optimization method
    55 overall loading
    56 paper
    57 potential
    58 powerful means
    59 problem
    60 proportion
    61 real-world behavior
    62 resistance
    63 significant proportion
    64 strength
    65 structure
    66 use
    67 weakness
    68 schema:name Layout optimization of long-span structures subject to self-weight and multiple load-cases
    69 schema:pagination 197
    70 schema:productId N3da5062b940240f1bd1fbb485b17c1b3
    71 N80dc23cfeec4450dabebc4341e695d2f
    72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1149109948
    73 https://doi.org/10.1007/s00158-022-03242-9
    74 schema:sdDatePublished 2022-10-01T06:49
    75 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    76 schema:sdPublisher N6f4aa8ef1a7f4c588dd8998a8ddb2e4e
    77 schema:url https://doi.org/10.1007/s00158-022-03242-9
    78 sgo:license sg:explorer/license/
    79 sgo:sdDataset articles
    80 rdf:type schema:ScholarlyArticle
    81 N3da5062b940240f1bd1fbb485b17c1b3 schema:name doi
    82 schema:value 10.1007/s00158-022-03242-9
    83 rdf:type schema:PropertyValue
    84 N6f4aa8ef1a7f4c588dd8998a8ddb2e4e schema:name Springer Nature - SN SciGraph project
    85 rdf:type schema:Organization
    86 N80dc23cfeec4450dabebc4341e695d2f schema:name dimensions_id
    87 schema:value pub.1149109948
    88 rdf:type schema:PropertyValue
    89 Naad2ba96cc9345bfab5ebd66a0edc636 schema:issueNumber 7
    90 rdf:type schema:PublicationIssue
    91 Nc81a7e2466164afcbc8e07a282a23cf1 schema:volumeNumber 65
    92 rdf:type schema:PublicationVolume
    93 Nca5ca863a76b4dfc9b122f39bd771dc2 rdf:first sg:person.013303436110.46
    94 rdf:rest Ndebb6a7b528344aaaeab6aff8832f194
    95 Ndebb6a7b528344aaaeab6aff8832f194 rdf:first sg:person.012624304756.31
    96 rdf:rest rdf:nil
    97 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    98 schema:name Engineering
    99 rdf:type schema:DefinedTerm
    100 anzsrc-for:0905 schema:inDefinedTermSet anzsrc-for:
    101 schema:name Civil Engineering
    102 rdf:type schema:DefinedTerm
    103 sg:journal.1050630 schema:issn 1615-147X
    104 1615-1488
    105 schema:name Structural and Multidisciplinary Optimization
    106 schema:publisher Springer Nature
    107 rdf:type schema:Periodical
    108 sg:person.012624304756.31 schema:affiliation grid-institutes:grid.11835.3e
    109 schema:familyName Gilbert
    110 schema:givenName Matthew
    111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012624304756.31
    112 rdf:type schema:Person
    113 sg:person.013303436110.46 schema:affiliation grid-institutes:grid.11835.3e
    114 schema:familyName Fairclough
    115 schema:givenName Helen E.
    116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013303436110.46
    117 rdf:type schema:Person
    118 sg:pub.10.1007/978-3-642-80895-1_23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013561283
    119 https://doi.org/10.1007/978-3-642-80895-1_23
    120 rdf:type schema:CreativeWork
    121 sg:pub.10.1007/bf01742459 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003622105
    122 https://doi.org/10.1007/bf01742459
    123 rdf:type schema:CreativeWork
    124 sg:pub.10.1007/s00158-009-0467-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040561196
    125 https://doi.org/10.1007/s00158-009-0467-0
    126 rdf:type schema:CreativeWork
    127 sg:pub.10.1007/s00158-011-0708-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1010820285
    128 https://doi.org/10.1007/s00158-011-0708-x
    129 rdf:type schema:CreativeWork
    130 sg:pub.10.1007/s00158-012-0769-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005810101
    131 https://doi.org/10.1007/s00158-012-0769-5
    132 rdf:type schema:CreativeWork
    133 sg:pub.10.1007/s00158-013-0978-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044638290
    134 https://doi.org/10.1007/s00158-013-0978-6
    135 rdf:type schema:CreativeWork
    136 sg:pub.10.1007/s00158-015-1248-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041702906
    137 https://doi.org/10.1007/s00158-015-1248-6
    138 rdf:type schema:CreativeWork
    139 sg:pub.10.1007/s00158-015-1260-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1012916313
    140 https://doi.org/10.1007/s00158-015-1260-x
    141 rdf:type schema:CreativeWork
    142 sg:pub.10.1007/s00158-015-1278-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009497359
    143 https://doi.org/10.1007/s00158-015-1278-0
    144 rdf:type schema:CreativeWork
    145 sg:pub.10.1007/s00158-017-1657-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074194514
    146 https://doi.org/10.1007/s00158-017-1657-9
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/s00158-017-1770-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090952859
    149 https://doi.org/10.1007/s00158-017-1770-9
    150 rdf:type schema:CreativeWork
    151 sg:pub.10.1007/s00158-018-1921-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101398725
    152 https://doi.org/10.1007/s00158-018-1921-7
    153 rdf:type schema:CreativeWork
    154 sg:pub.10.1007/s00158-018-1930-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101828224
    155 https://doi.org/10.1007/s00158-018-1930-6
    156 rdf:type schema:CreativeWork
    157 sg:pub.10.1007/s00158-019-02449-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1125558752
    158 https://doi.org/10.1007/s00158-019-02449-7
    159 rdf:type schema:CreativeWork
    160 sg:pub.10.1007/s00158-021-03139-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1144977935
    161 https://doi.org/10.1007/s00158-021-03139-z
    162 rdf:type schema:CreativeWork
    163 sg:pub.10.1007/s11081-013-9225-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033464626
    164 https://doi.org/10.1007/s11081-013-9225-7
    165 rdf:type schema:CreativeWork
    166 sg:pub.10.1038/s41467-020-16599-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1128096817
    167 https://doi.org/10.1038/s41467-020-16599-6
    168 rdf:type schema:CreativeWork
    169 grid-institutes:grid.11835.3e schema:alternateName Department of Civil and Structural Engineering, The University of Sheffield, Mappin St, S1 3JD, Sheffield, UK
    170 schema:name Department of Civil and Structural Engineering, The University of Sheffield, Mappin St, S1 3JD, Sheffield, UK
    171 rdf:type schema:Organization
     




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


    ...