Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2020-02-27

AUTHORS

Hooman Harandizadeh, Danial Jahed Armaghani, Edy Tonnizam Mohamad

ABSTRACT

The tensile strength (TS) of the rock is one the most key parameters in designing process of foundations and tunnels structures. However, direct techniques for TS determination (laboratory investigations) are not efficient with respect to cost and time. This investigation attempts to develop an innovative hybrid intelligent model, i.e. fuzzy-group method of data handling (GMDH) optimized by the gravitational search algorithm (GSA), fuzzy-GMDH-GSA, for prediction of the rock TS. To establish a database, the rock samples collected from a tunnel site were evaluated in the laboratory and a database (with the Schmidt hammer test, dry density test, and point load test as inputs and Brazilian tensile strength, BTS, as output) was prepared for modelling. Then, a fuzzy-GMDH-GSA model was developed to predict BTS of the rock considering the most influential of this predictive model. In addition, a fuzzy model as well as a GMDH model were constructed to predict BTS for comparison purposes. The performances of the proposed predictive models were evaluated by comparing the values of several statistical metrics such as correlation coefficient (R). R values of 0.90, 0.86, and 0.86 were obtained for testing datasets of fuzzy-GMDH-GSA, GMDH, and fuzzy models, respectively, which show that the fuzzy-GMDH-GSA predictive model is able to deliver greater prediction performance compared to other constructed models. The results confirmed the effective role of the GSA, as a powerful optimization algorithm in efficiency of hybrid fuzzy-GMDH-GSA model. Moreover, results of sensitivity analysis showed that the point load index is the most effective input on output of this study. More... »

PAGES

14047-14067

References to SciGraph publications

  • 2018-10-18. Rock tensile strength prediction using empirical and soft computing approaches in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2014-07-05. Assessment of River Water Quality Based on Theory of Variable Fuzzy Sets and Fuzzy Binary Comparison Method in WATER RESOURCES MANAGEMENT
  • 2014-02-06. Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates in EARTH SCIENCE INFORMATICS
  • 2019-05-10. The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production in ENGINEERING WITH COMPUTERS
  • 2009-12-23. BGSA: binary gravitational search algorithm in NATURAL COMPUTING
  • 2018-07-28. Predicting tunnel boring machine performance through a new model based on the group method of data handling in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2013-11-27. Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization in ARABIAN JOURNAL OF GEOSCIENCES
  • 2016-05-02. Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models in ENGINEERING WITH COMPUTERS
  • 2019-01-04. Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures in NEURAL COMPUTING AND APPLICATIONS
  • 2016-05-26. An optimized ANN model based on genetic algorithm for predicting ripping production in NEURAL COMPUTING AND APPLICATIONS
  • 2017-04-28. Self-compacting concrete strength prediction using surrogate models in NEURAL COMPUTING AND APPLICATIONS
  • 2016-12-21. Rock strength estimation: a PSO-based BP approach in NEURAL COMPUTING AND APPLICATIONS
  • 2011-11-02. Predicting the Uniaxial Compressive and Tensile Strengths of Gypsum Rock by Point Load Testing in ROCK MECHANICS AND ROCK ENGINEERING
  • 2016-08-30. Feasibility of ICA in approximating ground vibration resulting from mine blasting in NEURAL COMPUTING AND APPLICATIONS
  • 2019-03-08. Evaluation of conjugate depths of hydraulic jump in circular pipes using evolutionary computing in SOFT COMPUTING
  • 2016-09-14. Airblast prediction through a hybrid genetic algorithm-ANN model in NEURAL COMPUTING AND APPLICATIONS
  • 2019-12-10. Concrete compressive strength using artificial neural networks in NEURAL COMPUTING AND APPLICATIONS
  • 2016-10-24. Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming in NEURAL COMPUTING AND APPLICATIONS
  • 2019-06-25. Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns in ENGINEERING WITH COMPUTERS
  • 2014-01-30. A Review of the Tensile Strength of Rock: Concepts and Testing in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2015-06-10. Neuro-fuzzy technique to predict air-overpressure induced by blasting in ARABIAN JOURNAL OF GEOSCIENCES
  • 2015-03-20. Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods in ENGINEERING WITH COMPUTERS
  • 2016-01-06. Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers in ENVIRONMENTAL EARTH SCIENCES
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00521-020-04803-z

    DOI

    http://dx.doi.org/10.1007/s00521-020-04803-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1125135148


    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 Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Pajoohesh Sq., Imam Khomeni Highway, P.O. Box 76169133, Kerman, Iran", 
              "id": "http://www.grid.ac/institutes/grid.412503.1", 
              "name": [
                "Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Pajoohesh Sq., Imam Khomeni Highway, P.O. Box 76169133, Kerman, Iran"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Harandizadeh", 
            "givenName": "Hooman", 
            "id": "sg:person.015162255210.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015162255210.05"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute of Research and Development, Duy Tan University, 550000, Da Nang, Vietnam", 
              "id": "http://www.grid.ac/institutes/grid.444918.4", 
              "name": [
                "Institute of Research and Development, Duy Tan University, 550000, Da Nang, Vietnam"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Armaghani", 
            "givenName": "Danial Jahed", 
            "id": "sg:person.01156522364.57", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01156522364.57"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia", 
              "id": "http://www.grid.ac/institutes/grid.410877.d", 
              "name": [
                "Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Mohamad", 
            "givenName": "Edy Tonnizam", 
            "id": "sg:person.010264523752.19", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010264523752.19"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00521-016-2618-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044893429", 
              "https://doi.org/10.1007/s00521-016-2618-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2598-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042598741", 
              "https://doi.org/10.1007/s00521-016-2598-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00366-015-0402-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022416262", 
              "https://doi.org/10.1007/s00366-015-0402-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12517-013-1174-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038954491", 
              "https://doi.org/10.1007/s12517-013-1174-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00500-019-03877-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1112634067", 
              "https://doi.org/10.1007/s00500-019-03877-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10064-018-1349-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105883247", 
              "https://doi.org/10.1007/s10064-018-1349-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-018-03965-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1111154918", 
              "https://doi.org/10.1007/s00521-018-03965-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10706-014-9732-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047239750", 
              "https://doi.org/10.1007/s10706-014-9732-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00366-019-00808-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1117495481", 
              "https://doi.org/10.1007/s00366-019-00808-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-3007-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085086275", 
              "https://doi.org/10.1007/s00521-017-3007-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11047-009-9175-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003181811", 
              "https://doi.org/10.1007/s11047-009-9175-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11269-014-0738-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003597238", 
              "https://doi.org/10.1007/s11269-014-0738-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12517-015-1984-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005193044", 
              "https://doi.org/10.1007/s12517-015-1984-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-019-04663-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1123266113", 
              "https://doi.org/10.1007/s00521-019-04663-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10064-018-1405-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107705934", 
              "https://doi.org/10.1007/s10064-018-1405-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00366-019-00770-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1114039927", 
              "https://doi.org/10.1007/s00366-019-00770-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12145-014-0144-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014032354", 
              "https://doi.org/10.1007/s12145-014-0144-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2728-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015743528", 
              "https://doi.org/10.1007/s00521-016-2728-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00366-016-0452-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050462131", 
              "https://doi.org/10.1007/s00366-016-0452-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2359-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007611346", 
              "https://doi.org/10.1007/s00521-016-2359-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12665-015-4877-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022107356", 
              "https://doi.org/10.1007/s12665-015-4877-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00603-011-0196-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014798387", 
              "https://doi.org/10.1007/s00603-011-0196-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2577-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019069579", 
              "https://doi.org/10.1007/s00521-016-2577-0"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2020-02-27", 
        "datePublishedReg": "2020-02-27", 
        "description": "The tensile strength (TS) of the rock is one the most key parameters in designing process of foundations and tunnels structures. However, direct techniques for TS determination (laboratory investigations) are not efficient with respect to cost and time. This investigation attempts to develop an innovative hybrid intelligent model, i.e. fuzzy-group method of data handling (GMDH) optimized by the gravitational search algorithm (GSA), fuzzy-GMDH-GSA, for prediction of the rock TS. To establish a database, the rock samples collected from a tunnel site were evaluated in the laboratory and a database (with the Schmidt hammer test, dry density test, and point load test as inputs and Brazilian tensile strength, BTS, as output) was prepared for modelling. Then, a fuzzy-GMDH-GSA model was developed to predict BTS of the rock considering the most influential of this predictive model. In addition, a fuzzy model as well as a GMDH model were constructed to predict BTS for comparison purposes. The performances of the proposed predictive models were evaluated by comparing the values of several statistical metrics such as correlation coefficient (R). R values of 0.90, 0.86, and 0.86 were obtained for testing datasets of fuzzy-GMDH-GSA, GMDH, and fuzzy models, respectively, which show that the fuzzy-GMDH-GSA predictive model is able to deliver greater prediction performance compared to other constructed models. The results confirmed the effective role of the GSA, as a powerful optimization algorithm in efficiency of hybrid fuzzy-GMDH-GSA model. Moreover, results of sensitivity analysis showed that the point load index is the most effective input on output of this study.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s00521-020-04803-z", 
        "inLanguage": "en", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1104357", 
            "issn": [
              "0941-0643", 
              "1433-3058"
            ], 
            "name": "Neural Computing and Applications", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "17", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "32"
          }
        ], 
        "keywords": [
          "tensile strength", 
          "rock tensile strength", 
          "gravitational search algorithm", 
          "GSA model", 
          "fuzzy model", 
          "fuzzy group method", 
          "hybrid intelligent model", 
          "point load index", 
          "tunnel structure", 
          "key parameters", 
          "powerful optimization algorithm", 
          "comparison purposes", 
          "GMDH model", 
          "intelligent model", 
          "tunnel site", 
          "experimental datasets", 
          "predictive model", 
          "rock samples", 
          "optimization algorithm", 
          "sensitivity analysis", 
          "strength", 
          "performance", 
          "effective input", 
          "constructed model", 
          "direct technique", 
          "statistical metrics", 
          "great prediction performance", 
          "prediction performance", 
          "data handling", 
          "model", 
          "efficiency", 
          "modelling", 
          "algorithm", 
          "R value", 
          "rocks", 
          "effective role", 
          "BTS", 
          "coefficient", 
          "search algorithm", 
          "parameters", 
          "GMDH", 
          "output", 
          "results", 
          "cost", 
          "process of foundation", 
          "prediction", 
          "structure", 
          "values", 
          "process", 
          "technique", 
          "load index", 
          "input", 
          "investigation", 
          "method", 
          "handling", 
          "T determinations", 
          "correlation coefficient", 
          "respect", 
          "laboratory", 
          "determination", 
          "addition", 
          "time", 
          "foundation", 
          "analysis", 
          "samples", 
          "development", 
          "purpose", 
          "dataset", 
          "metrics", 
          "study", 
          "index", 
          "sites", 
          "database", 
          "role"
        ], 
        "name": "Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets", 
        "pagination": "14047-14067", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1125135148"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00521-020-04803-z"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00521-020-04803-z", 
          "https://app.dimensions.ai/details/publication/pub.1125135148"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-05-20T07:36", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_837.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s00521-020-04803-z"
      }
    ]
     

    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/s00521-020-04803-z'

    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/s00521-020-04803-z'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00521-020-04803-z'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00521-020-04803-z'


     

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

    244 TRIPLES      22 PREDICATES      122 URIs      91 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00521-020-04803-z schema:about anzsrc-for:09
    2 anzsrc-for:0905
    3 schema:author N1cdae42d40d24d699b21c2d7a7061baa
    4 schema:citation sg:pub.10.1007/s00366-015-0402-5
    5 sg:pub.10.1007/s00366-016-0452-3
    6 sg:pub.10.1007/s00366-019-00770-9
    7 sg:pub.10.1007/s00366-019-00808-y
    8 sg:pub.10.1007/s00500-019-03877-9
    9 sg:pub.10.1007/s00521-016-2359-8
    10 sg:pub.10.1007/s00521-016-2577-0
    11 sg:pub.10.1007/s00521-016-2598-8
    12 sg:pub.10.1007/s00521-016-2618-8
    13 sg:pub.10.1007/s00521-016-2728-3
    14 sg:pub.10.1007/s00521-017-3007-7
    15 sg:pub.10.1007/s00521-018-03965-1
    16 sg:pub.10.1007/s00521-019-04663-2
    17 sg:pub.10.1007/s00603-011-0196-8
    18 sg:pub.10.1007/s10064-018-1349-8
    19 sg:pub.10.1007/s10064-018-1405-4
    20 sg:pub.10.1007/s10706-014-9732-0
    21 sg:pub.10.1007/s11047-009-9175-3
    22 sg:pub.10.1007/s11269-014-0738-4
    23 sg:pub.10.1007/s12145-014-0144-8
    24 sg:pub.10.1007/s12517-013-1174-0
    25 sg:pub.10.1007/s12517-015-1984-3
    26 sg:pub.10.1007/s12665-015-4877-6
    27 schema:datePublished 2020-02-27
    28 schema:datePublishedReg 2020-02-27
    29 schema:description The tensile strength (TS) of the rock is one the most key parameters in designing process of foundations and tunnels structures. However, direct techniques for TS determination (laboratory investigations) are not efficient with respect to cost and time. This investigation attempts to develop an innovative hybrid intelligent model, i.e. fuzzy-group method of data handling (GMDH) optimized by the gravitational search algorithm (GSA), fuzzy-GMDH-GSA, for prediction of the rock TS. To establish a database, the rock samples collected from a tunnel site were evaluated in the laboratory and a database (with the Schmidt hammer test, dry density test, and point load test as inputs and Brazilian tensile strength, BTS, as output) was prepared for modelling. Then, a fuzzy-GMDH-GSA model was developed to predict BTS of the rock considering the most influential of this predictive model. In addition, a fuzzy model as well as a GMDH model were constructed to predict BTS for comparison purposes. The performances of the proposed predictive models were evaluated by comparing the values of several statistical metrics such as correlation coefficient (R). R values of 0.90, 0.86, and 0.86 were obtained for testing datasets of fuzzy-GMDH-GSA, GMDH, and fuzzy models, respectively, which show that the fuzzy-GMDH-GSA predictive model is able to deliver greater prediction performance compared to other constructed models. The results confirmed the effective role of the GSA, as a powerful optimization algorithm in efficiency of hybrid fuzzy-GMDH-GSA model. Moreover, results of sensitivity analysis showed that the point load index is the most effective input on output of this study.
    30 schema:genre article
    31 schema:inLanguage en
    32 schema:isAccessibleForFree false
    33 schema:isPartOf N29d9538c79284535b3a21a877537fbf6
    34 Nbe3a8d395e9d4809b4e10f9145153939
    35 sg:journal.1104357
    36 schema:keywords BTS
    37 GMDH
    38 GMDH model
    39 GSA model
    40 R value
    41 T determinations
    42 addition
    43 algorithm
    44 analysis
    45 coefficient
    46 comparison purposes
    47 constructed model
    48 correlation coefficient
    49 cost
    50 data handling
    51 database
    52 dataset
    53 determination
    54 development
    55 direct technique
    56 effective input
    57 effective role
    58 efficiency
    59 experimental datasets
    60 foundation
    61 fuzzy group method
    62 fuzzy model
    63 gravitational search algorithm
    64 great prediction performance
    65 handling
    66 hybrid intelligent model
    67 index
    68 input
    69 intelligent model
    70 investigation
    71 key parameters
    72 laboratory
    73 load index
    74 method
    75 metrics
    76 model
    77 modelling
    78 optimization algorithm
    79 output
    80 parameters
    81 performance
    82 point load index
    83 powerful optimization algorithm
    84 prediction
    85 prediction performance
    86 predictive model
    87 process
    88 process of foundation
    89 purpose
    90 respect
    91 results
    92 rock samples
    93 rock tensile strength
    94 rocks
    95 role
    96 samples
    97 search algorithm
    98 sensitivity analysis
    99 sites
    100 statistical metrics
    101 strength
    102 structure
    103 study
    104 technique
    105 tensile strength
    106 time
    107 tunnel site
    108 tunnel structure
    109 values
    110 schema:name Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets
    111 schema:pagination 14047-14067
    112 schema:productId N4ba1b65fec6749b4b50eab3953a1aa00
    113 N8d0fb93cec524aeeb83fcd4af2a171d1
    114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1125135148
    115 https://doi.org/10.1007/s00521-020-04803-z
    116 schema:sdDatePublished 2022-05-20T07:36
    117 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    118 schema:sdPublisher Nda545c91ecbb404bb5bc3feacb4e649b
    119 schema:url https://doi.org/10.1007/s00521-020-04803-z
    120 sgo:license sg:explorer/license/
    121 sgo:sdDataset articles
    122 rdf:type schema:ScholarlyArticle
    123 N1cdae42d40d24d699b21c2d7a7061baa rdf:first sg:person.015162255210.05
    124 rdf:rest N58edf21492784c83a0c497b4e122877b
    125 N29d9538c79284535b3a21a877537fbf6 schema:issueNumber 17
    126 rdf:type schema:PublicationIssue
    127 N4ba1b65fec6749b4b50eab3953a1aa00 schema:name doi
    128 schema:value 10.1007/s00521-020-04803-z
    129 rdf:type schema:PropertyValue
    130 N58edf21492784c83a0c497b4e122877b rdf:first sg:person.01156522364.57
    131 rdf:rest Nc5e062089a474c3ca834c1631a97e77e
    132 N8d0fb93cec524aeeb83fcd4af2a171d1 schema:name dimensions_id
    133 schema:value pub.1125135148
    134 rdf:type schema:PropertyValue
    135 Nbe3a8d395e9d4809b4e10f9145153939 schema:volumeNumber 32
    136 rdf:type schema:PublicationVolume
    137 Nc5e062089a474c3ca834c1631a97e77e rdf:first sg:person.010264523752.19
    138 rdf:rest rdf:nil
    139 Nda545c91ecbb404bb5bc3feacb4e649b schema:name Springer Nature - SN SciGraph project
    140 rdf:type schema:Organization
    141 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    142 schema:name Engineering
    143 rdf:type schema:DefinedTerm
    144 anzsrc-for:0905 schema:inDefinedTermSet anzsrc-for:
    145 schema:name Civil Engineering
    146 rdf:type schema:DefinedTerm
    147 sg:journal.1104357 schema:issn 0941-0643
    148 1433-3058
    149 schema:name Neural Computing and Applications
    150 schema:publisher Springer Nature
    151 rdf:type schema:Periodical
    152 sg:person.010264523752.19 schema:affiliation grid-institutes:grid.410877.d
    153 schema:familyName Mohamad
    154 schema:givenName Edy Tonnizam
    155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010264523752.19
    156 rdf:type schema:Person
    157 sg:person.01156522364.57 schema:affiliation grid-institutes:grid.444918.4
    158 schema:familyName Armaghani
    159 schema:givenName Danial Jahed
    160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01156522364.57
    161 rdf:type schema:Person
    162 sg:person.015162255210.05 schema:affiliation grid-institutes:grid.412503.1
    163 schema:familyName Harandizadeh
    164 schema:givenName Hooman
    165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015162255210.05
    166 rdf:type schema:Person
    167 sg:pub.10.1007/s00366-015-0402-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022416262
    168 https://doi.org/10.1007/s00366-015-0402-5
    169 rdf:type schema:CreativeWork
    170 sg:pub.10.1007/s00366-016-0452-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050462131
    171 https://doi.org/10.1007/s00366-016-0452-3
    172 rdf:type schema:CreativeWork
    173 sg:pub.10.1007/s00366-019-00770-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1114039927
    174 https://doi.org/10.1007/s00366-019-00770-9
    175 rdf:type schema:CreativeWork
    176 sg:pub.10.1007/s00366-019-00808-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1117495481
    177 https://doi.org/10.1007/s00366-019-00808-y
    178 rdf:type schema:CreativeWork
    179 sg:pub.10.1007/s00500-019-03877-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112634067
    180 https://doi.org/10.1007/s00500-019-03877-9
    181 rdf:type schema:CreativeWork
    182 sg:pub.10.1007/s00521-016-2359-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007611346
    183 https://doi.org/10.1007/s00521-016-2359-8
    184 rdf:type schema:CreativeWork
    185 sg:pub.10.1007/s00521-016-2577-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019069579
    186 https://doi.org/10.1007/s00521-016-2577-0
    187 rdf:type schema:CreativeWork
    188 sg:pub.10.1007/s00521-016-2598-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042598741
    189 https://doi.org/10.1007/s00521-016-2598-8
    190 rdf:type schema:CreativeWork
    191 sg:pub.10.1007/s00521-016-2618-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044893429
    192 https://doi.org/10.1007/s00521-016-2618-8
    193 rdf:type schema:CreativeWork
    194 sg:pub.10.1007/s00521-016-2728-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015743528
    195 https://doi.org/10.1007/s00521-016-2728-3
    196 rdf:type schema:CreativeWork
    197 sg:pub.10.1007/s00521-017-3007-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085086275
    198 https://doi.org/10.1007/s00521-017-3007-7
    199 rdf:type schema:CreativeWork
    200 sg:pub.10.1007/s00521-018-03965-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111154918
    201 https://doi.org/10.1007/s00521-018-03965-1
    202 rdf:type schema:CreativeWork
    203 sg:pub.10.1007/s00521-019-04663-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1123266113
    204 https://doi.org/10.1007/s00521-019-04663-2
    205 rdf:type schema:CreativeWork
    206 sg:pub.10.1007/s00603-011-0196-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014798387
    207 https://doi.org/10.1007/s00603-011-0196-8
    208 rdf:type schema:CreativeWork
    209 sg:pub.10.1007/s10064-018-1349-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105883247
    210 https://doi.org/10.1007/s10064-018-1349-8
    211 rdf:type schema:CreativeWork
    212 sg:pub.10.1007/s10064-018-1405-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107705934
    213 https://doi.org/10.1007/s10064-018-1405-4
    214 rdf:type schema:CreativeWork
    215 sg:pub.10.1007/s10706-014-9732-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047239750
    216 https://doi.org/10.1007/s10706-014-9732-0
    217 rdf:type schema:CreativeWork
    218 sg:pub.10.1007/s11047-009-9175-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003181811
    219 https://doi.org/10.1007/s11047-009-9175-3
    220 rdf:type schema:CreativeWork
    221 sg:pub.10.1007/s11269-014-0738-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003597238
    222 https://doi.org/10.1007/s11269-014-0738-4
    223 rdf:type schema:CreativeWork
    224 sg:pub.10.1007/s12145-014-0144-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014032354
    225 https://doi.org/10.1007/s12145-014-0144-8
    226 rdf:type schema:CreativeWork
    227 sg:pub.10.1007/s12517-013-1174-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038954491
    228 https://doi.org/10.1007/s12517-013-1174-0
    229 rdf:type schema:CreativeWork
    230 sg:pub.10.1007/s12517-015-1984-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005193044
    231 https://doi.org/10.1007/s12517-015-1984-3
    232 rdf:type schema:CreativeWork
    233 sg:pub.10.1007/s12665-015-4877-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022107356
    234 https://doi.org/10.1007/s12665-015-4877-6
    235 rdf:type schema:CreativeWork
    236 grid-institutes:grid.410877.d schema:alternateName Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
    237 schema:name Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
    238 rdf:type schema:Organization
    239 grid-institutes:grid.412503.1 schema:alternateName Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Pajoohesh Sq., Imam Khomeni Highway, P.O. Box 76169133, Kerman, Iran
    240 schema:name Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Pajoohesh Sq., Imam Khomeni Highway, P.O. Box 76169133, Kerman, Iran
    241 rdf:type schema:Organization
    242 grid-institutes:grid.444918.4 schema:alternateName Institute of Research and Development, Duy Tan University, 550000, Da Nang, Vietnam
    243 schema:name Institute of Research and Development, Duy Tan University, 550000, Da Nang, Vietnam
    244 rdf:type schema:Organization
     




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


    ...