Probabilistic Characterization of Rock Mass from Limited Laboratory Tests and Field Data: Associated Reliability Analysis and Its Interpretation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-14

AUTHORS

Bhardwaj Pandit, Gaurav Tiwari, Gali Madhavi Latha, G. L. Sivakumar Babu

ABSTRACT

Probabilistic methods are the most efficient methods to account for different types of uncertainties encountered in the estimated rock properties required for the stability analysis of rock slopes and tunnels. These methods require estimation of various parameters of probability distributions like mean, standard deviation (SD) and distributions types of rock properties, which requires large amount of data from laboratory and field investigations. However, in rock mechanics, the data available on rock properties for a project are often limited since the extents of projects are usually large and the test data are minimal due to cost constraints. Due to the unavailability of adequate test data, parameters (mean and SD) of probability distributions of rock properties themselves contain uncertainties. Since traditional reliability analysis uses these uncertain parameters (mean and SD) of probability distributions of rock properties, they may give incorrect estimation of the reliability of rock slope stability. This paper presents a method to overcome this limitation of traditional reliability analysis and outlines a new approach of rock mass characterization for the cases with limited data. This approach uses Sobol’s global sensitivity analysis and bootstrap method coupled with augmented radial basis function based response surface. This method is capable of handling the uncertainties in the parameters (mean and SD) of probability distributions of rock properties and can include their effect in the stability estimates of rock slopes. The proposed method is more practical and efficient, since it considers uncertainty in the statistical parameters of most commonly and easily available rock properties, i.e. uniaxial compressive strength and Geological Strength Index. Further, computational effort involved in the reliability analysis of rock slopes of large dimensions is comparatively smaller in this method. Present study also demonstrates this method through reliability analysis of a large rock slope of an open pit gold mine in Karnataka region of India. Results are compared with the results from traditional reliability analysis to highlight the advantages of the proposed method. It is observed that uncertainties in probability distribution type and its parameters (mean and SD) of rock properties have considerable effect on the estimated reliability index of the rock slope and hence traditional reliability methods based on the parameters of probability distributions estimated using limited data can make incorrect estimation of rock slope stability. Further, stability of the rock slope determined from proposed approach based on bootstrap method is represented by confidence interval of reliability index instead of a fixed value of reliability index as in traditional methods, providing more realistic estimates of rock slope stability. More... »

PAGES

1-17

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00603-019-01780-1

DOI

http://dx.doi.org/10.1007/s00603-019-01780-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112767250


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Indian Institute of Science Bangalore", 
          "id": "https://www.grid.ac/institutes/grid.34980.36", 
          "name": [
            "Department of Civil Engineering, Indian Institute of Science, Bangalore, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pandit", 
        "givenName": "Bhardwaj", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Indian Institute of Technology Kanpur", 
          "id": "https://www.grid.ac/institutes/grid.417965.8", 
          "name": [
            "Department of Civil Engineering, Indian Institute of Technology, Kanpur, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tiwari", 
        "givenName": "Gaurav", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Indian Institute of Science Bangalore", 
          "id": "https://www.grid.ac/institutes/grid.34980.36", 
          "name": [
            "Department of Civil Engineering, Indian Institute of Science, Bangalore, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Latha", 
        "givenName": "Gali Madhavi", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Indian Institute of Science Bangalore", 
          "id": "https://www.grid.ac/institutes/grid.34980.36", 
          "name": [
            "Department of Civil Engineering, Indian Institute of Science, Bangalore, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Babu", 
        "givenName": "G. L. Sivakumar", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00603-008-0011-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000790963", 
          "https://doi.org/10.1007/s00603-008-0011-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00603-008-0011-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000790963", 
          "https://doi.org/10.1007/s00603-008-0011-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/9781315388502-196", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002515864"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijrmms.2005.06.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004139961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1365-1609(02)00050-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011236743"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1176344552", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012894299"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ress.2015.03.034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018431301"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00603-016-0913-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019808653", 
          "https://doi.org/10.1007/s00603-016-0913-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.tust.2016.02.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020281022"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0951-8320(93)90097-i", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030334813"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0951-8320(93)90097-i", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030334813"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/03052150500422294", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031689605"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0148-9062(96)83505-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035653551"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2514/6.2003-1748", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035697659"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compgeo.2009.11.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037377307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0010-4655(02)00280-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041120034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10706-015-9880-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041125870", 
          "https://doi.org/10.1007/s10706-015-9880-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10706-015-9880-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041125870", 
          "https://doi.org/10.1007/s10706-015-9880-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/09208119508964707", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043060414"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00603-016-1054-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045135409", 
          "https://doi.org/10.1007/s00603-016-1054-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00603-016-1054-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045135409", 
          "https://doi.org/10.1007/s00603-016-1054-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enggeo.2015.06.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046493252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03177517", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051151493", 
          "https://doi.org/10.1007/bf03177517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03177517", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051151493", 
          "https://doi.org/10.1007/bf03177517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)gt.1943-5606.0000734", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057632704"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.13031/2013.23153", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064892822"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00603-017-1206-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084022881", 
          "https://doi.org/10.1007/s00603-017-1206-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00603-017-1206-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084022881", 
          "https://doi.org/10.1007/s00603-017-1206-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10064-017-1141-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091211961", 
          "https://doi.org/10.1007/s10064-017-1141-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10064-017-1141-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091211961", 
          "https://doi.org/10.1007/s10064-017-1141-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10064-017-1141-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091211961", 
          "https://doi.org/10.1007/s10064-017-1141-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10064-017-1141-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091211961", 
          "https://doi.org/10.1007/s10064-017-1141-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/17499518.2017.1407800", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093077393"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00603-018-1465-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103177849", 
          "https://doi.org/10.1007/s00603-018-1465-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00603-018-1465-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103177849", 
          "https://doi.org/10.1007/s00603-018-1465-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00603-018-1465-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103177849", 
          "https://doi.org/10.1007/s00603-018-1465-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1109701513", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/min8110530", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109906344"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03-14", 
    "datePublishedReg": "2019-03-14", 
    "description": "Probabilistic methods are the most efficient methods to account for different types of uncertainties encountered in the estimated rock properties required for the stability analysis of rock slopes and tunnels. These methods require estimation of various parameters of probability distributions like mean, standard deviation (SD) and distributions types of rock properties, which requires large amount of data from laboratory and field investigations. However, in rock mechanics, the data available on rock properties for a project are often limited since the extents of projects are usually large and the test data are minimal due to cost constraints. Due to the unavailability of adequate test data, parameters (mean and SD) of probability distributions of rock properties themselves contain uncertainties. Since traditional reliability analysis uses these uncertain parameters (mean and SD) of probability distributions of rock properties, they may give incorrect estimation of the reliability of rock slope stability. This paper presents a method to overcome this limitation of traditional reliability analysis and outlines a new approach of rock mass characterization for the cases with limited data. This approach uses Sobol\u2019s global sensitivity analysis and bootstrap method coupled with augmented radial basis function based response surface. This method is capable of handling the uncertainties in the parameters (mean and SD) of probability distributions of rock properties and can include their effect in the stability estimates of rock slopes. The proposed method is more practical and efficient, since it considers uncertainty in the statistical parameters of most commonly and easily available rock properties, i.e. uniaxial compressive strength and Geological Strength Index. Further, computational effort involved in the reliability analysis of rock slopes of large dimensions is comparatively smaller in this method. Present study also demonstrates this method through reliability analysis of a large rock slope of an open pit gold mine in Karnataka region of India. Results are compared with the results from traditional reliability analysis to highlight the advantages of the proposed method. It is observed that uncertainties in probability distribution type and its parameters (mean and SD) of rock properties have considerable effect on the estimated reliability index of the rock slope and hence traditional reliability methods based on the parameters of probability distributions estimated using limited data can make incorrect estimation of rock slope stability. Further, stability of the rock slope determined from proposed approach based on bootstrap method is represented by confidence interval of reliability index instead of a fixed value of reliability index as in traditional methods, providing more realistic estimates of rock slope stability.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00603-019-01780-1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1052698", 
        "issn": [
          "0723-2632", 
          "1434-453X"
        ], 
        "name": "Rock Mechanics and Rock Engineering", 
        "type": "Periodical"
      }
    ], 
    "name": "Probabilistic Characterization of Rock Mass from Limited Laboratory Tests and Field Data: Associated Reliability Analysis and Its Interpretation", 
    "pagination": "1-17", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d32d356916897777b52ee8e74224ddaf06bf18a97b10f455878fb66bdc5338c4"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00603-019-01780-1"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112767250"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00603-019-01780-1", 
      "https://app.dimensions.ai/details/publication/pub.1112767250"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:51", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000359_0000000359/records_29182_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00603-019-01780-1"
  }
]
 

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/s00603-019-01780-1'

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/s00603-019-01780-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00603-019-01780-1'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00603-019-01780-1'


 

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

163 TRIPLES      21 PREDICATES      51 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00603-019-01780-1 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N3274cd728da2453e8927db619446951e
4 schema:citation sg:pub.10.1007/bf03177517
5 sg:pub.10.1007/s00603-008-0011-3
6 sg:pub.10.1007/s00603-016-0913-4
7 sg:pub.10.1007/s00603-016-1054-5
8 sg:pub.10.1007/s00603-017-1206-2
9 sg:pub.10.1007/s00603-018-1465-6
10 sg:pub.10.1007/s10064-017-1141-1
11 sg:pub.10.1007/s10706-015-9880-x
12 https://app.dimensions.ai/details/publication/pub.1109701513
13 https://doi.org/10.1016/0148-9062(96)83505-4
14 https://doi.org/10.1016/0951-8320(93)90097-i
15 https://doi.org/10.1016/j.compgeo.2009.11.003
16 https://doi.org/10.1016/j.enggeo.2015.06.007
17 https://doi.org/10.1016/j.ijrmms.2005.06.005
18 https://doi.org/10.1016/j.ress.2015.03.034
19 https://doi.org/10.1016/j.tust.2016.02.007
20 https://doi.org/10.1016/s0010-4655(02)00280-1
21 https://doi.org/10.1016/s1365-1609(02)00050-3
22 https://doi.org/10.1061/(asce)gt.1943-5606.0000734
23 https://doi.org/10.1080/03052150500422294
24 https://doi.org/10.1080/09208119508964707
25 https://doi.org/10.1080/17499518.2017.1407800
26 https://doi.org/10.1201/9781315388502-196
27 https://doi.org/10.1214/aos/1176344552
28 https://doi.org/10.13031/2013.23153
29 https://doi.org/10.2514/6.2003-1748
30 https://doi.org/10.3390/min8110530
31 schema:datePublished 2019-03-14
32 schema:datePublishedReg 2019-03-14
33 schema:description Probabilistic methods are the most efficient methods to account for different types of uncertainties encountered in the estimated rock properties required for the stability analysis of rock slopes and tunnels. These methods require estimation of various parameters of probability distributions like mean, standard deviation (SD) and distributions types of rock properties, which requires large amount of data from laboratory and field investigations. However, in rock mechanics, the data available on rock properties for a project are often limited since the extents of projects are usually large and the test data are minimal due to cost constraints. Due to the unavailability of adequate test data, parameters (mean and SD) of probability distributions of rock properties themselves contain uncertainties. Since traditional reliability analysis uses these uncertain parameters (mean and SD) of probability distributions of rock properties, they may give incorrect estimation of the reliability of rock slope stability. This paper presents a method to overcome this limitation of traditional reliability analysis and outlines a new approach of rock mass characterization for the cases with limited data. This approach uses Sobol’s global sensitivity analysis and bootstrap method coupled with augmented radial basis function based response surface. This method is capable of handling the uncertainties in the parameters (mean and SD) of probability distributions of rock properties and can include their effect in the stability estimates of rock slopes. The proposed method is more practical and efficient, since it considers uncertainty in the statistical parameters of most commonly and easily available rock properties, i.e. uniaxial compressive strength and Geological Strength Index. Further, computational effort involved in the reliability analysis of rock slopes of large dimensions is comparatively smaller in this method. Present study also demonstrates this method through reliability analysis of a large rock slope of an open pit gold mine in Karnataka region of India. Results are compared with the results from traditional reliability analysis to highlight the advantages of the proposed method. It is observed that uncertainties in probability distribution type and its parameters (mean and SD) of rock properties have considerable effect on the estimated reliability index of the rock slope and hence traditional reliability methods based on the parameters of probability distributions estimated using limited data can make incorrect estimation of rock slope stability. Further, stability of the rock slope determined from proposed approach based on bootstrap method is represented by confidence interval of reliability index instead of a fixed value of reliability index as in traditional methods, providing more realistic estimates of rock slope stability.
34 schema:genre research_article
35 schema:inLanguage en
36 schema:isAccessibleForFree false
37 schema:isPartOf sg:journal.1052698
38 schema:name Probabilistic Characterization of Rock Mass from Limited Laboratory Tests and Field Data: Associated Reliability Analysis and Its Interpretation
39 schema:pagination 1-17
40 schema:productId N3178f02c87f449689df909aef1ac213f
41 N6c284aede3404a12af163b79f1cb2f9b
42 Nb3b4cec000544b288e3e730b39744c86
43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112767250
44 https://doi.org/10.1007/s00603-019-01780-1
45 schema:sdDatePublished 2019-04-11T11:51
46 schema:sdLicense https://scigraph.springernature.com/explorer/license/
47 schema:sdPublisher N4c1f70c31315471f9c4ca67f47a3aa23
48 schema:url https://link.springer.com/10.1007%2Fs00603-019-01780-1
49 sgo:license sg:explorer/license/
50 sgo:sdDataset articles
51 rdf:type schema:ScholarlyArticle
52 N1a6fcf51730945b39f53b5c995b8e409 rdf:first Nbf16cfdfce7647b5867035a59ddc9e39
53 rdf:rest N37bafb06b8914e73971b4b3f8c99b6da
54 N3178f02c87f449689df909aef1ac213f schema:name readcube_id
55 schema:value d32d356916897777b52ee8e74224ddaf06bf18a97b10f455878fb66bdc5338c4
56 rdf:type schema:PropertyValue
57 N3274cd728da2453e8927db619446951e rdf:first Ne9606e28b0ed4c4b8bce5411ef1b0f1a
58 rdf:rest N1a6fcf51730945b39f53b5c995b8e409
59 N37bafb06b8914e73971b4b3f8c99b6da rdf:first N99eb00bc6fa74e4d84825c69df948f80
60 rdf:rest N691f37c6c0614d89ab5cbb8b075ac173
61 N4c1f70c31315471f9c4ca67f47a3aa23 schema:name Springer Nature - SN SciGraph project
62 rdf:type schema:Organization
63 N691f37c6c0614d89ab5cbb8b075ac173 rdf:first N78a1029dcf124956bba3a4a4021fd08a
64 rdf:rest rdf:nil
65 N6c284aede3404a12af163b79f1cb2f9b schema:name dimensions_id
66 schema:value pub.1112767250
67 rdf:type schema:PropertyValue
68 N78a1029dcf124956bba3a4a4021fd08a schema:affiliation https://www.grid.ac/institutes/grid.34980.36
69 schema:familyName Babu
70 schema:givenName G. L. Sivakumar
71 rdf:type schema:Person
72 N99eb00bc6fa74e4d84825c69df948f80 schema:affiliation https://www.grid.ac/institutes/grid.34980.36
73 schema:familyName Latha
74 schema:givenName Gali Madhavi
75 rdf:type schema:Person
76 Nb3b4cec000544b288e3e730b39744c86 schema:name doi
77 schema:value 10.1007/s00603-019-01780-1
78 rdf:type schema:PropertyValue
79 Nbf16cfdfce7647b5867035a59ddc9e39 schema:affiliation https://www.grid.ac/institutes/grid.417965.8
80 schema:familyName Tiwari
81 schema:givenName Gaurav
82 rdf:type schema:Person
83 Ne9606e28b0ed4c4b8bce5411ef1b0f1a schema:affiliation https://www.grid.ac/institutes/grid.34980.36
84 schema:familyName Pandit
85 schema:givenName Bhardwaj
86 rdf:type schema:Person
87 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
88 schema:name Mathematical Sciences
89 rdf:type schema:DefinedTerm
90 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
91 schema:name Statistics
92 rdf:type schema:DefinedTerm
93 sg:journal.1052698 schema:issn 0723-2632
94 1434-453X
95 schema:name Rock Mechanics and Rock Engineering
96 rdf:type schema:Periodical
97 sg:pub.10.1007/bf03177517 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051151493
98 https://doi.org/10.1007/bf03177517
99 rdf:type schema:CreativeWork
100 sg:pub.10.1007/s00603-008-0011-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000790963
101 https://doi.org/10.1007/s00603-008-0011-3
102 rdf:type schema:CreativeWork
103 sg:pub.10.1007/s00603-016-0913-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019808653
104 https://doi.org/10.1007/s00603-016-0913-4
105 rdf:type schema:CreativeWork
106 sg:pub.10.1007/s00603-016-1054-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045135409
107 https://doi.org/10.1007/s00603-016-1054-5
108 rdf:type schema:CreativeWork
109 sg:pub.10.1007/s00603-017-1206-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084022881
110 https://doi.org/10.1007/s00603-017-1206-2
111 rdf:type schema:CreativeWork
112 sg:pub.10.1007/s00603-018-1465-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103177849
113 https://doi.org/10.1007/s00603-018-1465-6
114 rdf:type schema:CreativeWork
115 sg:pub.10.1007/s10064-017-1141-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091211961
116 https://doi.org/10.1007/s10064-017-1141-1
117 rdf:type schema:CreativeWork
118 sg:pub.10.1007/s10706-015-9880-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1041125870
119 https://doi.org/10.1007/s10706-015-9880-x
120 rdf:type schema:CreativeWork
121 https://app.dimensions.ai/details/publication/pub.1109701513 schema:CreativeWork
122 https://doi.org/10.1016/0148-9062(96)83505-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035653551
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/0951-8320(93)90097-i schema:sameAs https://app.dimensions.ai/details/publication/pub.1030334813
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/j.compgeo.2009.11.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037377307
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/j.enggeo.2015.06.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046493252
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/j.ijrmms.2005.06.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004139961
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/j.ress.2015.03.034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018431301
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/j.tust.2016.02.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020281022
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/s0010-4655(02)00280-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041120034
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/s1365-1609(02)00050-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011236743
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1061/(asce)gt.1943-5606.0000734 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057632704
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1080/03052150500422294 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031689605
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1080/09208119508964707 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043060414
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1080/17499518.2017.1407800 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093077393
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1201/9781315388502-196 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002515864
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1214/aos/1176344552 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012894299
151 rdf:type schema:CreativeWork
152 https://doi.org/10.13031/2013.23153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064892822
153 rdf:type schema:CreativeWork
154 https://doi.org/10.2514/6.2003-1748 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035697659
155 rdf:type schema:CreativeWork
156 https://doi.org/10.3390/min8110530 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109906344
157 rdf:type schema:CreativeWork
158 https://www.grid.ac/institutes/grid.34980.36 schema:alternateName Indian Institute of Science Bangalore
159 schema:name Department of Civil Engineering, Indian Institute of Science, Bangalore, India
160 rdf:type schema:Organization
161 https://www.grid.ac/institutes/grid.417965.8 schema:alternateName Indian Institute of Technology Kanpur
162 schema:name Department of Civil Engineering, Indian Institute of Technology, Kanpur, India
163 rdf:type schema:Organization
 




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


...