Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-10-14

AUTHORS

Danial Jahed Armaghani, Raja Shahrom Nizam Shah Bin Raja Shoib, Koohyar Faizi, Ahmad Safuan A. Rashid

ABSTRACT

Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research, design and construction. The accurate prediction of the ultimate bearing capacity (Qu) of rock-socketed piles is a difficult task due to the uncertainty surrounding the various factors that affect this capacity. This study was aimed at developing an artificial neural network (ANN) model, as well as a hybrid model based on both particle swarm optimisation (PSO) and ANN, with which to predict the Qu of rock-socketed piles. PSO, a powerful population-based algorithm used in solving continuous and discrete optimisation problems, was here employed as a robust global search algorithm to determine ANN weights and biases and thereby improve model performance. To achieve the study aims, 132 piles socketed in various rock types as part of the Klang Valley Mass Rapid Transit project, Malaysia, were investigated. Based on previous related investigations, parameters with the most influence on Qu were identified and utilised in the modelling procedure of the intelligent systems. After constructing and modelling these systems, selected performance indices including the coefficient of determination (R2), root-mean-square error, variance account for and total ranking were used to identify the best models and compare the obtained results. This analysis revealed that the hybrid PSO–ANN model offers a higher degree of accuracy compared to conventional ANN for predicting the Qu of rock-socketed piles. However, the developed model would be most useful in the preliminary stages of pile design and should be used with caution. More... »

PAGES

391-405

References to SciGraph publications

  • 2013-01-08. Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes in ENVIRONMENTAL EARTH SCIENCES
  • 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
  • 2014-10-18. An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2012-04-03. Evaluation of effect of blast design parameters on flyrock using artificial neural networks in NEURAL COMPUTING AND APPLICATIONS
  • 1943-12. A logical calculus of the ideas immanent in nervous activity in BULLETIN OF MATHEMATICAL BIOLOGY
  • 2015-08-19. Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances in ENGINEERING WITH COMPUTERS
  • 2014-07-10. Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2010-07-07. Intelligent systems for ground vibration measurement: a comparative study in ENGINEERING WITH COMPUTERS
  • 2012-07-03. Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks in ENVIRONMENTAL EARTH SCIENCES
  • 2012-07-12. Backbreak prediction in the Chadormalu iron mine using artificial neural network in NEURAL COMPUTING AND APPLICATIONS
  • 2015-04-17. Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation in ENGINEERING WITH COMPUTERS
  • 2012-04-28. A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks in NEURAL COMPUTING AND APPLICATIONS
  • 1993. Statistical aspects of neural networks in NETWORKS AND CHAOS — STATISTICAL AND PROBABILISTIC ASPECTS
  • 2015-03-20. Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods in ENGINEERING WITH COMPUTERS
  • 1997-10. A hierarchical analysis for rock engineering using artificial neural networks in ROCK MECHANICS AND ROCK ENGINEERING
  • 2012-02-18. Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network in NEURAL COMPUTING AND APPLICATIONS
  • 2015-02-18. Prediction of seismic slope stability through combination of particle swarm optimization and neural network in ENGINEERING WITH COMPUTERS
  • 2007-06. Particle swarm optimization in SWARM INTELLIGENCE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00521-015-2072-z

    DOI

    http://dx.doi.org/10.1007/s00521-015-2072-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1023467671


    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/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "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"
          }, 
          {
            "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/2008/0905", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Civil Engineering", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia", 
              "id": "http://www.grid.ac/institutes/grid.410877.d", 
              "name": [
                "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Jahed Armaghani", 
            "givenName": "Danial", 
            "id": "sg:person.012214152011.74", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012214152011.74"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia", 
              "id": "http://www.grid.ac/institutes/grid.410877.d", 
              "name": [
                "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Shoib", 
            "givenName": "Raja Shahrom Nizam Shah Bin Raja", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of Civil Engineering, University of Birmingham, Birmingham, UK", 
              "id": "http://www.grid.ac/institutes/grid.6572.6", 
              "name": [
                "School of Civil Engineering, University of Birmingham, Birmingham, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Faizi", 
            "givenName": "Koohyar", 
            "id": "sg:person.07673563311.96", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07673563311.96"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia", 
              "id": "http://www.grid.ac/institutes/grid.410877.d", 
              "name": [
                "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Rashid", 
            "givenName": "Ahmad Safuan A.", 
            "id": "sg:person.011302274665.84", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011302274665.84"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00366-015-0410-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041240827", 
              "https://doi.org/10.1007/s00366-015-0410-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02478259", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028715170", 
              "https://doi.org/10.1007/bf02478259"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00366-015-0404-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029547379", 
              "https://doi.org/10.1007/s00366-015-0404-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11721-007-0002-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033319589", 
              "https://doi.org/10.1007/s11721-007-0002-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10064-014-0638-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012839375", 
              "https://doi.org/10.1007/s10064-014-0638-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00366-010-0193-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044004182", 
              "https://doi.org/10.1007/s00366-010-0193-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12665-012-2214-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020162888", 
              "https://doi.org/10.1007/s12665-012-2214-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-012-0917-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044702409", 
              "https://doi.org/10.1007/s00521-012-0917-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10064-014-0687-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038842643", 
              "https://doi.org/10.1007/s10064-014-0687-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4899-3099-6_2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1089745563", 
              "https://doi.org/10.1007/978-1-4899-3099-6_2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf01045717", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046314392", 
              "https://doi.org/10.1007/bf01045717"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00366-015-0400-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003281017", 
              "https://doi.org/10.1007/s00366-015-0400-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12665-012-1783-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017168196", 
              "https://doi.org/10.1007/s12665-012-1783-z"
            ], 
            "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/s00521-012-0944-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010393270", 
              "https://doi.org/10.1007/s00521-012-0944-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-012-1038-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016800802", 
              "https://doi.org/10.1007/s00521-012-1038-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-012-0856-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006773113", 
              "https://doi.org/10.1007/s00521-012-0856-y"
            ], 
            "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"
          }
        ], 
        "datePublished": "2015-10-14", 
        "datePublishedReg": "2015-10-14", 
        "description": "Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research, design and construction. The accurate prediction of the ultimate bearing capacity (Qu) of rock-socketed piles is a difficult task due to the uncertainty surrounding the various factors that affect this capacity. This study was aimed at developing an artificial neural network (ANN) model, as well as a hybrid model based on both particle swarm optimisation (PSO) and ANN, with which to predict the Qu of rock-socketed piles. PSO, a powerful population-based algorithm used in solving continuous and discrete optimisation problems, was here employed as a robust global search algorithm to determine ANN weights and biases and thereby improve model performance. To achieve the study aims, 132 piles socketed in various rock types as part of the Klang Valley Mass Rapid Transit project, Malaysia, were investigated. Based on previous related investigations, parameters with the most influence on Qu were identified and utilised in the modelling procedure of the intelligent systems. After constructing and modelling these systems, selected performance indices including the coefficient of determination (R2), root-mean-square error, variance account for and total ranking were used to identify the best models and compare the obtained results. This analysis revealed that the hybrid PSO\u2013ANN model offers a higher degree of accuracy compared to conventional ANN for predicting the Qu of rock-socketed piles. However, the developed model would be most useful in the preliminary stages of pile design and should be used with caution.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s00521-015-2072-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": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "28"
          }
        ], 
        "keywords": [
          "rock-socketed pile", 
          "hybrid PSO\u2013ANN model", 
          "ultimate bearing capacity", 
          "bearing capacity", 
          "PSO-ANN model", 
          "particle swarm optimization", 
          "soft ground", 
          "pile design", 
          "artificial neural network model", 
          "variance account", 
          "population-based algorithms", 
          "Mass Rapid Transit project", 
          "piles", 
          "neural network model", 
          "discrete optimization problems", 
          "accurate prediction", 
          "global search algorithm", 
          "powerful population-based algorithm", 
          "modelling procedure", 
          "intelligent systems", 
          "ANN weights", 
          "conventional ANN", 
          "search algorithm", 
          "Rapid Transit project", 
          "swarm optimization", 
          "performance index", 
          "hybrid model", 
          "coefficient of determination", 
          "network model", 
          "transit projects", 
          "optimization problem", 
          "square error", 
          "difficult task", 
          "model performance", 
          "design", 
          "ANN", 
          "preliminary stage", 
          "algorithm", 
          "total ranking", 
          "key issues", 
          "most influence", 
          "capacity", 
          "model", 
          "rock types", 
          "optimization", 
          "system", 
          "performance", 
          "Qu", 
          "coefficient", 
          "parameters", 
          "accuracy", 
          "prediction", 
          "task", 
          "influence", 
          "high degree", 
          "uncertainty", 
          "ground", 
          "construction", 
          "investigation", 
          "error", 
          "best model", 
          "ranking", 
          "account", 
          "project", 
          "results", 
          "issues", 
          "determination", 
          "problem", 
          "foundation", 
          "universal concern", 
          "procedure", 
          "analysis", 
          "part", 
          "types", 
          "degree", 
          "study", 
          "research", 
          "stage", 
          "concern", 
          "factors", 
          "biases", 
          "index", 
          "Malaysia", 
          "weight", 
          "caution", 
          "robust global search algorithm", 
          "Klang Valley Mass Rapid Transit project", 
          "Valley Mass Rapid Transit project"
        ], 
        "name": "Developing a hybrid PSO\u2013ANN model for estimating the ultimate bearing capacity of rock-socketed piles", 
        "pagination": "391-405", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1023467671"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00521-015-2072-z"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00521-015-2072-z", 
          "https://app.dimensions.ai/details/publication/pub.1023467671"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2021-12-01T19:34", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/article/article_678.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s00521-015-2072-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/s00521-015-2072-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/s00521-015-2072-z'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00521-015-2072-z'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00521-015-2072-z'


     

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

    249 TRIPLES      22 PREDICATES      133 URIs      105 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00521-015-2072-z schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 anzsrc-for:09
    4 anzsrc-for:0905
    5 schema:author N8d7ecf49da5346a992c54799b1c5eda4
    6 schema:citation sg:pub.10.1007/978-1-4899-3099-6_2
    7 sg:pub.10.1007/bf01045717
    8 sg:pub.10.1007/bf02478259
    9 sg:pub.10.1007/s00366-010-0193-7
    10 sg:pub.10.1007/s00366-015-0400-7
    11 sg:pub.10.1007/s00366-015-0402-5
    12 sg:pub.10.1007/s00366-015-0404-3
    13 sg:pub.10.1007/s00366-015-0410-5
    14 sg:pub.10.1007/s00521-012-0856-y
    15 sg:pub.10.1007/s00521-012-0917-2
    16 sg:pub.10.1007/s00521-012-0944-z
    17 sg:pub.10.1007/s00521-012-1038-7
    18 sg:pub.10.1007/s10064-014-0638-0
    19 sg:pub.10.1007/s10064-014-0687-4
    20 sg:pub.10.1007/s11721-007-0002-0
    21 sg:pub.10.1007/s12517-013-1174-0
    22 sg:pub.10.1007/s12665-012-1783-z
    23 sg:pub.10.1007/s12665-012-2214-x
    24 schema:datePublished 2015-10-14
    25 schema:datePublishedReg 2015-10-14
    26 schema:description Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research, design and construction. The accurate prediction of the ultimate bearing capacity (Qu) of rock-socketed piles is a difficult task due to the uncertainty surrounding the various factors that affect this capacity. This study was aimed at developing an artificial neural network (ANN) model, as well as a hybrid model based on both particle swarm optimisation (PSO) and ANN, with which to predict the Qu of rock-socketed piles. PSO, a powerful population-based algorithm used in solving continuous and discrete optimisation problems, was here employed as a robust global search algorithm to determine ANN weights and biases and thereby improve model performance. To achieve the study aims, 132 piles socketed in various rock types as part of the Klang Valley Mass Rapid Transit project, Malaysia, were investigated. Based on previous related investigations, parameters with the most influence on Qu were identified and utilised in the modelling procedure of the intelligent systems. After constructing and modelling these systems, selected performance indices including the coefficient of determination (R2), root-mean-square error, variance account for and total ranking were used to identify the best models and compare the obtained results. This analysis revealed that the hybrid PSO–ANN model offers a higher degree of accuracy compared to conventional ANN for predicting the Qu of rock-socketed piles. However, the developed model would be most useful in the preliminary stages of pile design and should be used with caution.
    27 schema:genre article
    28 schema:inLanguage en
    29 schema:isAccessibleForFree false
    30 schema:isPartOf N713007bc130f4d3da794f0a49707bec9
    31 Nc5029b57599f4f7989a25bb68d7f0c18
    32 sg:journal.1104357
    33 schema:keywords ANN
    34 ANN weights
    35 Klang Valley Mass Rapid Transit project
    36 Malaysia
    37 Mass Rapid Transit project
    38 PSO-ANN model
    39 Qu
    40 Rapid Transit project
    41 Valley Mass Rapid Transit project
    42 account
    43 accuracy
    44 accurate prediction
    45 algorithm
    46 analysis
    47 artificial neural network model
    48 bearing capacity
    49 best model
    50 biases
    51 capacity
    52 caution
    53 coefficient
    54 coefficient of determination
    55 concern
    56 construction
    57 conventional ANN
    58 degree
    59 design
    60 determination
    61 difficult task
    62 discrete optimization problems
    63 error
    64 factors
    65 foundation
    66 global search algorithm
    67 ground
    68 high degree
    69 hybrid PSO–ANN model
    70 hybrid model
    71 index
    72 influence
    73 intelligent systems
    74 investigation
    75 issues
    76 key issues
    77 model
    78 model performance
    79 modelling procedure
    80 most influence
    81 network model
    82 neural network model
    83 optimization
    84 optimization problem
    85 parameters
    86 part
    87 particle swarm optimization
    88 performance
    89 performance index
    90 pile design
    91 piles
    92 population-based algorithms
    93 powerful population-based algorithm
    94 prediction
    95 preliminary stage
    96 problem
    97 procedure
    98 project
    99 ranking
    100 research
    101 results
    102 robust global search algorithm
    103 rock types
    104 rock-socketed pile
    105 search algorithm
    106 soft ground
    107 square error
    108 stage
    109 study
    110 swarm optimization
    111 system
    112 task
    113 total ranking
    114 transit projects
    115 types
    116 ultimate bearing capacity
    117 uncertainty
    118 universal concern
    119 variance account
    120 weight
    121 schema:name Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles
    122 schema:pagination 391-405
    123 schema:productId N14b0d66f561b4431bd71157ca41fa883
    124 N36d3e4db9f5d4d0791e0d0f6e5ea800d
    125 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023467671
    126 https://doi.org/10.1007/s00521-015-2072-z
    127 schema:sdDatePublished 2021-12-01T19:34
    128 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    129 schema:sdPublisher N338699501cd448f68b90b71bae40c2ef
    130 schema:url https://doi.org/10.1007/s00521-015-2072-z
    131 sgo:license sg:explorer/license/
    132 sgo:sdDataset articles
    133 rdf:type schema:ScholarlyArticle
    134 N14b0d66f561b4431bd71157ca41fa883 schema:name doi
    135 schema:value 10.1007/s00521-015-2072-z
    136 rdf:type schema:PropertyValue
    137 N2fc87e80de474d96a252b84a2ddf6dcb schema:affiliation grid-institutes:grid.410877.d
    138 schema:familyName Shoib
    139 schema:givenName Raja Shahrom Nizam Shah Bin Raja
    140 rdf:type schema:Person
    141 N338699501cd448f68b90b71bae40c2ef schema:name Springer Nature - SN SciGraph project
    142 rdf:type schema:Organization
    143 N36d3e4db9f5d4d0791e0d0f6e5ea800d schema:name dimensions_id
    144 schema:value pub.1023467671
    145 rdf:type schema:PropertyValue
    146 N713007bc130f4d3da794f0a49707bec9 schema:volumeNumber 28
    147 rdf:type schema:PublicationVolume
    148 N80920626d66d4eef87f8ec0e777e54a2 rdf:first N2fc87e80de474d96a252b84a2ddf6dcb
    149 rdf:rest Nd571369646284d18a0c4ce7a5ec9ecb7
    150 N8d7ecf49da5346a992c54799b1c5eda4 rdf:first sg:person.012214152011.74
    151 rdf:rest N80920626d66d4eef87f8ec0e777e54a2
    152 Nc5029b57599f4f7989a25bb68d7f0c18 schema:issueNumber 2
    153 rdf:type schema:PublicationIssue
    154 Nd571369646284d18a0c4ce7a5ec9ecb7 rdf:first sg:person.07673563311.96
    155 rdf:rest Nf276defdf2854b9da7eafe47903a75c8
    156 Nf276defdf2854b9da7eafe47903a75c8 rdf:first sg:person.011302274665.84
    157 rdf:rest rdf:nil
    158 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    159 schema:name Information and Computing Sciences
    160 rdf:type schema:DefinedTerm
    161 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    162 schema:name Artificial Intelligence and Image Processing
    163 rdf:type schema:DefinedTerm
    164 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    165 schema:name Engineering
    166 rdf:type schema:DefinedTerm
    167 anzsrc-for:0905 schema:inDefinedTermSet anzsrc-for:
    168 schema:name Civil Engineering
    169 rdf:type schema:DefinedTerm
    170 sg:journal.1104357 schema:issn 0941-0643
    171 1433-3058
    172 schema:name Neural Computing and Applications
    173 schema:publisher Springer Nature
    174 rdf:type schema:Periodical
    175 sg:person.011302274665.84 schema:affiliation grid-institutes:grid.410877.d
    176 schema:familyName Rashid
    177 schema:givenName Ahmad Safuan A.
    178 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011302274665.84
    179 rdf:type schema:Person
    180 sg:person.012214152011.74 schema:affiliation grid-institutes:grid.410877.d
    181 schema:familyName Jahed Armaghani
    182 schema:givenName Danial
    183 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012214152011.74
    184 rdf:type schema:Person
    185 sg:person.07673563311.96 schema:affiliation grid-institutes:grid.6572.6
    186 schema:familyName Faizi
    187 schema:givenName Koohyar
    188 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07673563311.96
    189 rdf:type schema:Person
    190 sg:pub.10.1007/978-1-4899-3099-6_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1089745563
    191 https://doi.org/10.1007/978-1-4899-3099-6_2
    192 rdf:type schema:CreativeWork
    193 sg:pub.10.1007/bf01045717 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046314392
    194 https://doi.org/10.1007/bf01045717
    195 rdf:type schema:CreativeWork
    196 sg:pub.10.1007/bf02478259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028715170
    197 https://doi.org/10.1007/bf02478259
    198 rdf:type schema:CreativeWork
    199 sg:pub.10.1007/s00366-010-0193-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044004182
    200 https://doi.org/10.1007/s00366-010-0193-7
    201 rdf:type schema:CreativeWork
    202 sg:pub.10.1007/s00366-015-0400-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003281017
    203 https://doi.org/10.1007/s00366-015-0400-7
    204 rdf:type schema:CreativeWork
    205 sg:pub.10.1007/s00366-015-0402-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022416262
    206 https://doi.org/10.1007/s00366-015-0402-5
    207 rdf:type schema:CreativeWork
    208 sg:pub.10.1007/s00366-015-0404-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029547379
    209 https://doi.org/10.1007/s00366-015-0404-3
    210 rdf:type schema:CreativeWork
    211 sg:pub.10.1007/s00366-015-0410-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041240827
    212 https://doi.org/10.1007/s00366-015-0410-5
    213 rdf:type schema:CreativeWork
    214 sg:pub.10.1007/s00521-012-0856-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1006773113
    215 https://doi.org/10.1007/s00521-012-0856-y
    216 rdf:type schema:CreativeWork
    217 sg:pub.10.1007/s00521-012-0917-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044702409
    218 https://doi.org/10.1007/s00521-012-0917-2
    219 rdf:type schema:CreativeWork
    220 sg:pub.10.1007/s00521-012-0944-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1010393270
    221 https://doi.org/10.1007/s00521-012-0944-z
    222 rdf:type schema:CreativeWork
    223 sg:pub.10.1007/s00521-012-1038-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016800802
    224 https://doi.org/10.1007/s00521-012-1038-7
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1007/s10064-014-0638-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012839375
    227 https://doi.org/10.1007/s10064-014-0638-0
    228 rdf:type schema:CreativeWork
    229 sg:pub.10.1007/s10064-014-0687-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038842643
    230 https://doi.org/10.1007/s10064-014-0687-4
    231 rdf:type schema:CreativeWork
    232 sg:pub.10.1007/s11721-007-0002-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033319589
    233 https://doi.org/10.1007/s11721-007-0002-0
    234 rdf:type schema:CreativeWork
    235 sg:pub.10.1007/s12517-013-1174-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038954491
    236 https://doi.org/10.1007/s12517-013-1174-0
    237 rdf:type schema:CreativeWork
    238 sg:pub.10.1007/s12665-012-1783-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1017168196
    239 https://doi.org/10.1007/s12665-012-1783-z
    240 rdf:type schema:CreativeWork
    241 sg:pub.10.1007/s12665-012-2214-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1020162888
    242 https://doi.org/10.1007/s12665-012-2214-x
    243 rdf:type schema:CreativeWork
    244 grid-institutes:grid.410877.d schema:alternateName Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia
    245 schema:name Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia
    246 rdf:type schema:Organization
    247 grid-institutes:grid.6572.6 schema:alternateName School of Civil Engineering, University of Birmingham, Birmingham, UK
    248 schema:name School of Civil Engineering, University of Birmingham, Birmingham, UK
    249 rdf:type schema:Organization
     




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


    ...