Neuro-fuzzy technique to predict air-overpressure induced by blasting View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-06-10

AUTHORS

Danial Jahed Armaghani, Mohsen Hajihassani, Houman Sohaei, Edy Tonnizam Mohamad, Aminaton Marto, Hossein Motaghedi, Mohammad Reza Moghaddam

ABSTRACT

In addition to all benefits of blasting in mining and civil engineering applications, blasting has some undesirable impacts on surrounding areas. Blast-induced air-overpressure (AOp) is one of the most important environmental impacts of blasting operation which may cause severe damage to nearby residents and structures. Hence, it is a major concern to predict and subsequently control the AOp due to blasting. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) model for prediction of blast-induced AOp in quarry blasting sites. For this purpose, 128 blasting operations were monitored in three quarry sites, Malaysia. Several models were constructed to obtain the optimum model in which each model involved five inputs and one output. Values of maximum charge per delay, powder factor, burden to spacing ratio, stemming length, and distance between monitoring station and blast face were set as input parameters to predict AOp. For comparison purposes, considering the same data, AOp values were predicted through the pre-developed artificial neural network (ANN) model and multiple regression (MR) technique. The results demonstrated the superiority of the ANFIS model to predict AOp compared to other methods. Moreover, results of sensitivity analysis indicated that the maximum charge per delay and powder factor and distance from the blast face are the most influential parameters on AOp. More... »

PAGES

10937-10950

References to SciGraph publications

  • 2010-03-10. Prediction of rock fragmentation due to blasting using artificial neural network in ENGINEERING WITH COMPUTERS
  • 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
  • 2010-08-11. Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach in ARABIAN JOURNAL OF GEOSCIENCES
  • 2012-04-17. A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2007-12-05. Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system in ENVIRONMENTAL GEOLOGY
  • 2015-01-30. Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2009-10-07. Prediction of blast-induced air overpressure using support vector machine in ARABIAN JOURNAL OF GEOSCIENCES
  • 2011-01-07. Burden prediction in blasting operation using rock geomechanical properties in ARABIAN JOURNAL OF GEOSCIENCES
  • 2015-04-17. Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation 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
  • 1993. Statistical aspects of neural networks in NETWORKS AND CHAOS — STATISTICAL AND PROBABILISTIC ASPECTS
  • 2015-03-17. Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach in ENVIRONMENTAL EARTH SCIENCES
  • 2009-10-30. Prediction and controlling of flyrock in blasting operation using artificial neural network in ARABIAN JOURNAL OF GEOSCIENCES
  • 2015-04-25. Application of two intelligent systems in predicting environmental impacts of quarry blasting in ARABIAN JOURNAL OF GEOSCIENCES
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12517-015-1984-3

    DOI

    http://dx.doi.org/10.1007/s12517-015-1984-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1005193044


    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/2008/04", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Earth Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0403", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Geology", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, 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), 81310, Skudai, 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": "Construction Research Alliance, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia", 
              "id": "http://www.grid.ac/institutes/grid.410877.d", 
              "name": [
                "Construction Research Alliance, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hajihassani", 
            "givenName": "Mohsen", 
            "id": "sg:person.01213305202.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213305202.05"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, 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), 81310, Skudai, Johor, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sohaei", 
            "givenName": "Houman", 
            "id": "sg:person.012735376637.30", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012735376637.30"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, 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), 81310, Skudai, Johor, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Tonnizam Mohamad", 
            "givenName": "Edy", 
            "id": "sg:person.010532067637.20", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010532067637.20"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, 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), 81310, Skudai, Johor, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Marto", 
            "givenName": "Aminaton", 
            "id": "sg:person.01200512774.84", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01200512774.84"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Civil Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran", 
              "id": "http://www.grid.ac/institutes/grid.411463.5", 
              "name": [
                "Department of Civil Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Motaghedi", 
            "givenName": "Hossein", 
            "id": "sg:person.016537667057.91", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016537667057.91"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Saman Zamin Hamgam Engineering Company, Tehran, Iran", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "South Tehran Branch, Islamic Azad University, Tehran, Iran", 
                "Saman Zamin Hamgam Engineering Company, Tehran, Iran"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Moghaddam", 
            "givenName": "Mohammad Reza", 
            "id": "sg:person.010022006445.19", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010022006445.19"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "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/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/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/s12665-015-4274-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030000705", 
              "https://doi.org/10.1007/s12665-015-4274-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12517-010-0185-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044029557", 
              "https://doi.org/10.1007/s12517-010-0185-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12517-010-0269-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034250952", 
              "https://doi.org/10.1007/s12517-010-0269-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12517-009-0092-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030978164", 
              "https://doi.org/10.1007/s12517-009-0092-7"
            ], 
            "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/s12517-009-0091-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003408679", 
              "https://doi.org/10.1007/s12517-009-0091-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00254-007-1143-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041344277", 
              "https://doi.org/10.1007/s00254-007-1143-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10064-015-0720-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042960605", 
              "https://doi.org/10.1007/s10064-015-0720-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10706-012-9510-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024901061", 
              "https://doi.org/10.1007/s10706-012-9510-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12517-015-1908-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025927733", 
              "https://doi.org/10.1007/s12517-015-1908-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00366-010-0187-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035214031", 
              "https://doi.org/10.1007/s00366-010-0187-5"
            ], 
            "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"
          }
        ], 
        "datePublished": "2015-06-10", 
        "datePublishedReg": "2015-06-10", 
        "description": "In addition to all benefits of blasting in mining and civil engineering applications, blasting has some undesirable impacts on surrounding areas. Blast-induced air-overpressure (AOp) is one of the most important environmental impacts of blasting operation which may cause severe damage to nearby residents and structures. Hence, it is a major concern to predict and subsequently control the AOp due to blasting. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) model for prediction of blast-induced AOp in quarry blasting sites. For this purpose, 128 blasting operations were monitored in three quarry sites, Malaysia. Several models were constructed to obtain the optimum model in which each model involved five inputs and one output. Values of maximum charge per delay, powder factor, burden to spacing ratio, stemming length, and distance between monitoring station and blast face were set as input parameters to predict AOp. For comparison purposes, considering the same data, AOp values were predicted through the pre-developed artificial neural network (ANN) model and multiple regression (MR) technique. The results demonstrated the superiority of the ANFIS model to predict AOp compared to other methods. Moreover, results of sensitivity analysis indicated that the maximum charge per delay and powder factor and distance from the blast face are the most influential parameters on AOp.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s12517-015-1984-3", 
        "inLanguage": "en", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1135927", 
            "issn": [
              "1866-7511", 
              "1866-7538"
            ], 
            "name": "Arabian Journal of Geosciences", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "12", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "8"
          }
        ], 
        "keywords": [
          "blast face", 
          "powder factor", 
          "civil engineering applications", 
          "maximum charge", 
          "adaptive neuro-fuzzy inference system (ANFIS) model", 
          "neuro-fuzzy inference system model", 
          "engineering applications", 
          "blasting operations", 
          "artificial neural network model", 
          "influential parameters", 
          "neuro-fuzzy technique", 
          "ANFIS model", 
          "important environmental impacts", 
          "input parameters", 
          "environmental impacts", 
          "comparison purposes", 
          "system model", 
          "optimum model", 
          "sensitivity analysis", 
          "blasting", 
          "quarry sites", 
          "operation", 
          "neural network model", 
          "undesirable impacts", 
          "severe damage", 
          "nearby residents", 
          "parameters", 
          "network model", 
          "major concern", 
          "charge", 
          "model", 
          "technique", 
          "AOP", 
          "applications", 
          "stations", 
          "output", 
          "results", 
          "prediction", 
          "distance", 
          "delay", 
          "values", 
          "structure", 
          "AOP values", 
          "ratio", 
          "input", 
          "quarry", 
          "method", 
          "regression techniques", 
          "superiority", 
          "impact", 
          "length", 
          "purpose", 
          "damage", 
          "addition", 
          "multiple regression techniques", 
          "area", 
          "mining", 
          "analysis", 
          "face", 
          "same data", 
          "factors", 
          "sites", 
          "data", 
          "benefits", 
          "concern", 
          "Malaysia", 
          "burden", 
          "residents", 
          "paper"
        ], 
        "name": "Neuro-fuzzy technique to predict air-overpressure induced by blasting", 
        "pagination": "10937-10950", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1005193044"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s12517-015-1984-3"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s12517-015-1984-3", 
          "https://app.dimensions.ai/details/publication/pub.1005193044"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-05-10T10:08", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_663.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s12517-015-1984-3"
      }
    ]
     

    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/s12517-015-1984-3'

    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/s12517-015-1984-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12517-015-1984-3'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12517-015-1984-3'


     

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

    238 TRIPLES      22 PREDICATES      109 URIs      86 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s12517-015-1984-3 schema:about anzsrc-for:04
    2 anzsrc-for:0403
    3 schema:author Ndffb52dfe50c4763be5b0c0698263675
    4 schema:citation sg:pub.10.1007/978-1-4899-3099-6_2
    5 sg:pub.10.1007/s00254-007-1143-6
    6 sg:pub.10.1007/s00366-010-0187-5
    7 sg:pub.10.1007/s00366-015-0404-3
    8 sg:pub.10.1007/s10064-014-0638-0
    9 sg:pub.10.1007/s10064-014-0687-4
    10 sg:pub.10.1007/s10064-015-0720-2
    11 sg:pub.10.1007/s10706-012-9510-9
    12 sg:pub.10.1007/s12517-009-0091-8
    13 sg:pub.10.1007/s12517-009-0092-7
    14 sg:pub.10.1007/s12517-010-0185-3
    15 sg:pub.10.1007/s12517-010-0269-0
    16 sg:pub.10.1007/s12517-013-1174-0
    17 sg:pub.10.1007/s12517-015-1908-2
    18 sg:pub.10.1007/s12665-015-4274-1
    19 schema:datePublished 2015-06-10
    20 schema:datePublishedReg 2015-06-10
    21 schema:description In addition to all benefits of blasting in mining and civil engineering applications, blasting has some undesirable impacts on surrounding areas. Blast-induced air-overpressure (AOp) is one of the most important environmental impacts of blasting operation which may cause severe damage to nearby residents and structures. Hence, it is a major concern to predict and subsequently control the AOp due to blasting. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) model for prediction of blast-induced AOp in quarry blasting sites. For this purpose, 128 blasting operations were monitored in three quarry sites, Malaysia. Several models were constructed to obtain the optimum model in which each model involved five inputs and one output. Values of maximum charge per delay, powder factor, burden to spacing ratio, stemming length, and distance between monitoring station and blast face were set as input parameters to predict AOp. For comparison purposes, considering the same data, AOp values were predicted through the pre-developed artificial neural network (ANN) model and multiple regression (MR) technique. The results demonstrated the superiority of the ANFIS model to predict AOp compared to other methods. Moreover, results of sensitivity analysis indicated that the maximum charge per delay and powder factor and distance from the blast face are the most influential parameters on AOp.
    22 schema:genre article
    23 schema:inLanguage en
    24 schema:isAccessibleForFree false
    25 schema:isPartOf N486d20385c214a698f59e8a41e577039
    26 Nc991fd64f8b641d79a5c05e75f87f58b
    27 sg:journal.1135927
    28 schema:keywords ANFIS model
    29 AOP
    30 AOP values
    31 Malaysia
    32 adaptive neuro-fuzzy inference system (ANFIS) model
    33 addition
    34 analysis
    35 applications
    36 area
    37 artificial neural network model
    38 benefits
    39 blast face
    40 blasting
    41 blasting operations
    42 burden
    43 charge
    44 civil engineering applications
    45 comparison purposes
    46 concern
    47 damage
    48 data
    49 delay
    50 distance
    51 engineering applications
    52 environmental impacts
    53 face
    54 factors
    55 impact
    56 important environmental impacts
    57 influential parameters
    58 input
    59 input parameters
    60 length
    61 major concern
    62 maximum charge
    63 method
    64 mining
    65 model
    66 multiple regression techniques
    67 nearby residents
    68 network model
    69 neural network model
    70 neuro-fuzzy inference system model
    71 neuro-fuzzy technique
    72 operation
    73 optimum model
    74 output
    75 paper
    76 parameters
    77 powder factor
    78 prediction
    79 purpose
    80 quarry
    81 quarry sites
    82 ratio
    83 regression techniques
    84 residents
    85 results
    86 same data
    87 sensitivity analysis
    88 severe damage
    89 sites
    90 stations
    91 structure
    92 superiority
    93 system model
    94 technique
    95 undesirable impacts
    96 values
    97 schema:name Neuro-fuzzy technique to predict air-overpressure induced by blasting
    98 schema:pagination 10937-10950
    99 schema:productId N986f0133586c4bf399e229d2a6b3bb97
    100 Nd969e1dd71b94611bdcf1b282c55f1a7
    101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005193044
    102 https://doi.org/10.1007/s12517-015-1984-3
    103 schema:sdDatePublished 2022-05-10T10:08
    104 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    105 schema:sdPublisher N401ec0f640984789b3c817634b1d38b2
    106 schema:url https://doi.org/10.1007/s12517-015-1984-3
    107 sgo:license sg:explorer/license/
    108 sgo:sdDataset articles
    109 rdf:type schema:ScholarlyArticle
    110 N0a7ad8a50bc04b14829254b53f78f25f rdf:first sg:person.01200512774.84
    111 rdf:rest Naf7e77ea49ea4cc7af09071875885399
    112 N26efe590ae974e20bbe8ad1c93b61e8c rdf:first sg:person.010022006445.19
    113 rdf:rest rdf:nil
    114 N316a9266ad9443218d2c410b70c065a2 rdf:first sg:person.010532067637.20
    115 rdf:rest N0a7ad8a50bc04b14829254b53f78f25f
    116 N401ec0f640984789b3c817634b1d38b2 schema:name Springer Nature - SN SciGraph project
    117 rdf:type schema:Organization
    118 N4308804a9c35427db6c92b2be61ab94d rdf:first sg:person.01213305202.05
    119 rdf:rest Nd3b63a31b6ae4e3b99cd02589c422331
    120 N486d20385c214a698f59e8a41e577039 schema:volumeNumber 8
    121 rdf:type schema:PublicationVolume
    122 N986f0133586c4bf399e229d2a6b3bb97 schema:name doi
    123 schema:value 10.1007/s12517-015-1984-3
    124 rdf:type schema:PropertyValue
    125 Naf7e77ea49ea4cc7af09071875885399 rdf:first sg:person.016537667057.91
    126 rdf:rest N26efe590ae974e20bbe8ad1c93b61e8c
    127 Nc991fd64f8b641d79a5c05e75f87f58b schema:issueNumber 12
    128 rdf:type schema:PublicationIssue
    129 Nd3b63a31b6ae4e3b99cd02589c422331 rdf:first sg:person.012735376637.30
    130 rdf:rest N316a9266ad9443218d2c410b70c065a2
    131 Nd969e1dd71b94611bdcf1b282c55f1a7 schema:name dimensions_id
    132 schema:value pub.1005193044
    133 rdf:type schema:PropertyValue
    134 Ndffb52dfe50c4763be5b0c0698263675 rdf:first sg:person.012214152011.74
    135 rdf:rest N4308804a9c35427db6c92b2be61ab94d
    136 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
    137 schema:name Earth Sciences
    138 rdf:type schema:DefinedTerm
    139 anzsrc-for:0403 schema:inDefinedTermSet anzsrc-for:
    140 schema:name Geology
    141 rdf:type schema:DefinedTerm
    142 sg:journal.1135927 schema:issn 1866-7511
    143 1866-7538
    144 schema:name Arabian Journal of Geosciences
    145 schema:publisher Springer Nature
    146 rdf:type schema:Periodical
    147 sg:person.010022006445.19 schema:affiliation grid-institutes:None
    148 schema:familyName Moghaddam
    149 schema:givenName Mohammad Reza
    150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010022006445.19
    151 rdf:type schema:Person
    152 sg:person.010532067637.20 schema:affiliation grid-institutes:grid.410877.d
    153 schema:familyName Tonnizam Mohamad
    154 schema:givenName Edy
    155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010532067637.20
    156 rdf:type schema:Person
    157 sg:person.01200512774.84 schema:affiliation grid-institutes:grid.410877.d
    158 schema:familyName Marto
    159 schema:givenName Aminaton
    160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01200512774.84
    161 rdf:type schema:Person
    162 sg:person.01213305202.05 schema:affiliation grid-institutes:grid.410877.d
    163 schema:familyName Hajihassani
    164 schema:givenName Mohsen
    165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213305202.05
    166 rdf:type schema:Person
    167 sg:person.012214152011.74 schema:affiliation grid-institutes:grid.410877.d
    168 schema:familyName Jahed Armaghani
    169 schema:givenName Danial
    170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012214152011.74
    171 rdf:type schema:Person
    172 sg:person.012735376637.30 schema:affiliation grid-institutes:grid.410877.d
    173 schema:familyName Sohaei
    174 schema:givenName Houman
    175 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012735376637.30
    176 rdf:type schema:Person
    177 sg:person.016537667057.91 schema:affiliation grid-institutes:grid.411463.5
    178 schema:familyName Motaghedi
    179 schema:givenName Hossein
    180 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016537667057.91
    181 rdf:type schema:Person
    182 sg:pub.10.1007/978-1-4899-3099-6_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1089745563
    183 https://doi.org/10.1007/978-1-4899-3099-6_2
    184 rdf:type schema:CreativeWork
    185 sg:pub.10.1007/s00254-007-1143-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041344277
    186 https://doi.org/10.1007/s00254-007-1143-6
    187 rdf:type schema:CreativeWork
    188 sg:pub.10.1007/s00366-010-0187-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035214031
    189 https://doi.org/10.1007/s00366-010-0187-5
    190 rdf:type schema:CreativeWork
    191 sg:pub.10.1007/s00366-015-0404-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029547379
    192 https://doi.org/10.1007/s00366-015-0404-3
    193 rdf:type schema:CreativeWork
    194 sg:pub.10.1007/s10064-014-0638-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012839375
    195 https://doi.org/10.1007/s10064-014-0638-0
    196 rdf:type schema:CreativeWork
    197 sg:pub.10.1007/s10064-014-0687-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038842643
    198 https://doi.org/10.1007/s10064-014-0687-4
    199 rdf:type schema:CreativeWork
    200 sg:pub.10.1007/s10064-015-0720-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042960605
    201 https://doi.org/10.1007/s10064-015-0720-2
    202 rdf:type schema:CreativeWork
    203 sg:pub.10.1007/s10706-012-9510-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024901061
    204 https://doi.org/10.1007/s10706-012-9510-9
    205 rdf:type schema:CreativeWork
    206 sg:pub.10.1007/s12517-009-0091-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003408679
    207 https://doi.org/10.1007/s12517-009-0091-8
    208 rdf:type schema:CreativeWork
    209 sg:pub.10.1007/s12517-009-0092-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030978164
    210 https://doi.org/10.1007/s12517-009-0092-7
    211 rdf:type schema:CreativeWork
    212 sg:pub.10.1007/s12517-010-0185-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044029557
    213 https://doi.org/10.1007/s12517-010-0185-3
    214 rdf:type schema:CreativeWork
    215 sg:pub.10.1007/s12517-010-0269-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034250952
    216 https://doi.org/10.1007/s12517-010-0269-0
    217 rdf:type schema:CreativeWork
    218 sg:pub.10.1007/s12517-013-1174-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038954491
    219 https://doi.org/10.1007/s12517-013-1174-0
    220 rdf:type schema:CreativeWork
    221 sg:pub.10.1007/s12517-015-1908-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025927733
    222 https://doi.org/10.1007/s12517-015-1908-2
    223 rdf:type schema:CreativeWork
    224 sg:pub.10.1007/s12665-015-4274-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030000705
    225 https://doi.org/10.1007/s12665-015-4274-1
    226 rdf:type schema:CreativeWork
    227 grid-institutes:None schema:alternateName Saman Zamin Hamgam Engineering Company, Tehran, Iran
    228 schema:name Saman Zamin Hamgam Engineering Company, Tehran, Iran
    229 South Tehran Branch, Islamic Azad University, Tehran, Iran
    230 rdf:type schema:Organization
    231 grid-institutes:grid.410877.d schema:alternateName Construction Research Alliance, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia
    232 Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia
    233 schema:name Construction Research Alliance, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia
    234 Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia
    235 rdf:type schema:Organization
    236 grid-institutes:grid.411463.5 schema:alternateName Department of Civil Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
    237 schema:name Department of Civil Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
    238 rdf:type schema:Organization
     




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


    ...