Prediction of blast-induced air overpressure: a hybrid AI-based predictive model View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-10-04

AUTHORS

Danial Jahed Armaghani, Mohsen Hajihassani, Aminaton Marto, Roohollah Shirani Faradonbeh, Edy Tonnizam Mohamad

ABSTRACT

Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques. More... »

PAGES

666

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10661-015-4895-6

DOI

http://dx.doi.org/10.1007/s10661-015-4895-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012955740

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26433903


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Environmental Monitoring", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Explosions", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Malaysia", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Theoretical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neural Networks, Computer", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia", 
          "id": "http://www.grid.ac/institutes/grid.410877.d", 
          "name": [
            "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, 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, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia", 
          "id": "http://www.grid.ac/institutes/grid.410877.d", 
          "name": [
            "Construction Research Alliance, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, 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, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia", 
          "id": "http://www.grid.ac/institutes/grid.410877.d", 
          "name": [
            "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, 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": "Young Researchers and Elit Club, South Tehran Branch, Islamic Azad University, Tehran, Iran", 
          "id": "http://www.grid.ac/institutes/grid.411463.5", 
          "name": [
            "Young Researchers and Elit Club, South Tehran Branch, Islamic Azad University, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shirani Faradonbeh", 
        "givenName": "Roohollah", 
        "id": "sg:person.01367250355.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01367250355.41"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia", 
          "id": "http://www.grid.ac/institutes/grid.410877.d", 
          "name": [
            "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mohamad", 
        "givenName": "Edy Tonnizam", 
        "id": "sg:person.010264523752.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010264523752.19"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/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/bf02478259", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028715170", 
          "https://doi.org/10.1007/bf02478259"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02459570", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026163953", 
          "https://doi.org/10.1007/bf02459570"
        ], 
        "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.10.1023/a:1005907720627", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035950620", 
          "https://doi.org/10.1023/a:1005907720627"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-69848-7_55", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035284691", 
          "https://doi.org/10.1007/978-3-540-69848-7_55"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12665-015-4305-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010124461", 
          "https://doi.org/10.1007/s12665-015-4305-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10661-010-1470-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027134704", 
          "https://doi.org/10.1007/s10661-010-1470-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10064-014-0657-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016555821", 
          "https://doi.org/10.1007/s10064-014-0657-x"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015-10-04", 
    "datePublishedReg": "2015-10-04", 
    "description": "Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s10661-015-4895-6", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1095684", 
        "issn": [
          "0167-6369", 
          "1573-2959"
        ], 
        "name": "Environmental Monitoring and Assessment", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "11", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "187"
      }
    ], 
    "keywords": [
      "blast-induced air overpressure", 
      "air overpressure", 
      "ICA-ANN model", 
      "granite quarry site", 
      "blast operation", 
      "imperialist competitive algorithm", 
      "blasting operations", 
      "artificial neural network", 
      "significant environmental problems", 
      "conventional ANN model", 
      "influential parameters", 
      "maximum charge", 
      "new empirical equation", 
      "monitoring points", 
      "important environmental impacts", 
      "quarry sites", 
      "environmental impacts", 
      "empirical equation", 
      "predictor equation", 
      "comparison purposes", 
      "ANN model", 
      "overpressure", 
      "presented technique", 
      "hybrid AI", 
      "operation", 
      "environmental problems", 
      "severe damage", 
      "competitive algorithm", 
      "equations", 
      "prediction", 
      "model", 
      "residential areas", 
      "predictive model", 
      "neural network", 
      "vicinity", 
      "parameters", 
      "AOP values", 
      "nearby areas", 
      "results", 
      "damage", 
      "charge", 
      "quarry", 
      "technique", 
      "area", 
      "algorithm", 
      "purpose", 
      "problem", 
      "values", 
      "distance", 
      "point", 
      "network", 
      "impact", 
      "delay", 
      "potential risk", 
      "data", 
      "sites", 
      "Malaysia", 
      "AI", 
      "risk", 
      "paper", 
      "prediction of AOp", 
      "generalized predictor equation", 
      "ICA-ANN results"
    ], 
    "name": "Prediction of blast-induced air overpressure: a hybrid AI-based predictive model", 
    "pagination": "666", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1012955740"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10661-015-4895-6"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "26433903"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10661-015-4895-6", 
      "https://app.dimensions.ai/details/publication/pub.1012955740"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-11-01T18:25", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/article/article_669.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s10661-015-4895-6"
  }
]
 

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/s10661-015-4895-6'

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/s10661-015-4895-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10661-015-4895-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10661-015-4895-6'


 

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

218 TRIPLES      22 PREDICATES      104 URIs      87 LITERALS      13 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10661-015-4895-6 schema:about N1535f2b548b1482e9664c3bb6147b3d7
2 N215dbce3e27042b5b28fb11468aaa9dc
3 N6ea27399321a41969246a00396d99093
4 Nce4a648946c743cd84f4b41833198382
5 Nf69854435df64093b804bc4ee68f4d9b
6 Nfa853e25a8624573b6d28b9379f9cee6
7 anzsrc-for:08
8 anzsrc-for:0801
9 schema:author N2a77a1f2d8d74a31aa641ce6a57c1d38
10 schema:citation sg:pub.10.1007/978-3-540-69848-7_55
11 sg:pub.10.1007/bf02459570
12 sg:pub.10.1007/bf02478259
13 sg:pub.10.1007/s10064-014-0657-x
14 sg:pub.10.1007/s10661-010-1470-z
15 sg:pub.10.1007/s12517-009-0092-7
16 sg:pub.10.1007/s12517-013-1174-0
17 sg:pub.10.1007/s12665-015-4305-y
18 sg:pub.10.1023/a:1005907720627
19 schema:datePublished 2015-10-04
20 schema:datePublishedReg 2015-10-04
21 schema:description Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.
22 schema:genre article
23 schema:inLanguage en
24 schema:isAccessibleForFree false
25 schema:isPartOf N0ade84b5f1824dcd99dae7c5d196f8d4
26 N500db74c636c4cc899dbc4f755919d35
27 sg:journal.1095684
28 schema:keywords AI
29 ANN model
30 AOP values
31 ICA-ANN model
32 ICA-ANN results
33 Malaysia
34 air overpressure
35 algorithm
36 area
37 artificial neural network
38 blast operation
39 blast-induced air overpressure
40 blasting operations
41 charge
42 comparison purposes
43 competitive algorithm
44 conventional ANN model
45 damage
46 data
47 delay
48 distance
49 empirical equation
50 environmental impacts
51 environmental problems
52 equations
53 generalized predictor equation
54 granite quarry site
55 hybrid AI
56 impact
57 imperialist competitive algorithm
58 important environmental impacts
59 influential parameters
60 maximum charge
61 model
62 monitoring points
63 nearby areas
64 network
65 neural network
66 new empirical equation
67 operation
68 overpressure
69 paper
70 parameters
71 point
72 potential risk
73 prediction
74 prediction of AOp
75 predictive model
76 predictor equation
77 presented technique
78 problem
79 purpose
80 quarry
81 quarry sites
82 residential areas
83 results
84 risk
85 severe damage
86 significant environmental problems
87 sites
88 technique
89 values
90 vicinity
91 schema:name Prediction of blast-induced air overpressure: a hybrid AI-based predictive model
92 schema:pagination 666
93 schema:productId N0da793f38b7440f4b291a50a076fd710
94 N4b2073b771004c07903cd198f9c2b07e
95 Ne8378e643bd847c9b17f12378611ef11
96 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012955740
97 https://doi.org/10.1007/s10661-015-4895-6
98 schema:sdDatePublished 2021-11-01T18:25
99 schema:sdLicense https://scigraph.springernature.com/explorer/license/
100 schema:sdPublisher N124a01489da54f3886d28c2703893443
101 schema:url https://doi.org/10.1007/s10661-015-4895-6
102 sgo:license sg:explorer/license/
103 sgo:sdDataset articles
104 rdf:type schema:ScholarlyArticle
105 N0ade84b5f1824dcd99dae7c5d196f8d4 schema:volumeNumber 187
106 rdf:type schema:PublicationVolume
107 N0da793f38b7440f4b291a50a076fd710 schema:name doi
108 schema:value 10.1007/s10661-015-4895-6
109 rdf:type schema:PropertyValue
110 N124a01489da54f3886d28c2703893443 schema:name Springer Nature - SN SciGraph project
111 rdf:type schema:Organization
112 N1535f2b548b1482e9664c3bb6147b3d7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
113 schema:name Models, Theoretical
114 rdf:type schema:DefinedTerm
115 N215dbce3e27042b5b28fb11468aaa9dc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name Malaysia
117 rdf:type schema:DefinedTerm
118 N24a149680df744b7890181e3ae0f91e1 rdf:first sg:person.010264523752.19
119 rdf:rest rdf:nil
120 N2a77a1f2d8d74a31aa641ce6a57c1d38 rdf:first sg:person.012214152011.74
121 rdf:rest N6d8c41beeaea4da5bbacea8f6f5bf758
122 N45dc1affab4b415da04a4f5384435cec rdf:first sg:person.01367250355.41
123 rdf:rest N24a149680df744b7890181e3ae0f91e1
124 N4b2073b771004c07903cd198f9c2b07e schema:name dimensions_id
125 schema:value pub.1012955740
126 rdf:type schema:PropertyValue
127 N500db74c636c4cc899dbc4f755919d35 schema:issueNumber 11
128 rdf:type schema:PublicationIssue
129 N6d8c41beeaea4da5bbacea8f6f5bf758 rdf:first sg:person.01213305202.05
130 rdf:rest Nda74062fc6c54bfb9dbdbbca32e6ed21
131 N6ea27399321a41969246a00396d99093 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
132 schema:name Environmental Monitoring
133 rdf:type schema:DefinedTerm
134 Nce4a648946c743cd84f4b41833198382 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Algorithms
136 rdf:type schema:DefinedTerm
137 Nda74062fc6c54bfb9dbdbbca32e6ed21 rdf:first sg:person.01200512774.84
138 rdf:rest N45dc1affab4b415da04a4f5384435cec
139 Ne8378e643bd847c9b17f12378611ef11 schema:name pubmed_id
140 schema:value 26433903
141 rdf:type schema:PropertyValue
142 Nf69854435df64093b804bc4ee68f4d9b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Explosions
144 rdf:type schema:DefinedTerm
145 Nfa853e25a8624573b6d28b9379f9cee6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
146 schema:name Neural Networks, Computer
147 rdf:type schema:DefinedTerm
148 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
149 schema:name Information and Computing Sciences
150 rdf:type schema:DefinedTerm
151 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
152 schema:name Artificial Intelligence and Image Processing
153 rdf:type schema:DefinedTerm
154 sg:journal.1095684 schema:issn 0167-6369
155 1573-2959
156 schema:name Environmental Monitoring and Assessment
157 schema:publisher Springer Nature
158 rdf:type schema:Periodical
159 sg:person.010264523752.19 schema:affiliation grid-institutes:grid.410877.d
160 schema:familyName Mohamad
161 schema:givenName Edy Tonnizam
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010264523752.19
163 rdf:type schema:Person
164 sg:person.01200512774.84 schema:affiliation grid-institutes:grid.410877.d
165 schema:familyName Marto
166 schema:givenName Aminaton
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01200512774.84
168 rdf:type schema:Person
169 sg:person.01213305202.05 schema:affiliation grid-institutes:grid.410877.d
170 schema:familyName Hajihassani
171 schema:givenName Mohsen
172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213305202.05
173 rdf:type schema:Person
174 sg:person.012214152011.74 schema:affiliation grid-institutes:grid.410877.d
175 schema:familyName Jahed Armaghani
176 schema:givenName Danial
177 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012214152011.74
178 rdf:type schema:Person
179 sg:person.01367250355.41 schema:affiliation grid-institutes:grid.411463.5
180 schema:familyName Shirani Faradonbeh
181 schema:givenName Roohollah
182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01367250355.41
183 rdf:type schema:Person
184 sg:pub.10.1007/978-3-540-69848-7_55 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035284691
185 https://doi.org/10.1007/978-3-540-69848-7_55
186 rdf:type schema:CreativeWork
187 sg:pub.10.1007/bf02459570 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026163953
188 https://doi.org/10.1007/bf02459570
189 rdf:type schema:CreativeWork
190 sg:pub.10.1007/bf02478259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028715170
191 https://doi.org/10.1007/bf02478259
192 rdf:type schema:CreativeWork
193 sg:pub.10.1007/s10064-014-0657-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1016555821
194 https://doi.org/10.1007/s10064-014-0657-x
195 rdf:type schema:CreativeWork
196 sg:pub.10.1007/s10661-010-1470-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1027134704
197 https://doi.org/10.1007/s10661-010-1470-z
198 rdf:type schema:CreativeWork
199 sg:pub.10.1007/s12517-009-0092-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030978164
200 https://doi.org/10.1007/s12517-009-0092-7
201 rdf:type schema:CreativeWork
202 sg:pub.10.1007/s12517-013-1174-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038954491
203 https://doi.org/10.1007/s12517-013-1174-0
204 rdf:type schema:CreativeWork
205 sg:pub.10.1007/s12665-015-4305-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1010124461
206 https://doi.org/10.1007/s12665-015-4305-y
207 rdf:type schema:CreativeWork
208 sg:pub.10.1023/a:1005907720627 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035950620
209 https://doi.org/10.1023/a:1005907720627
210 rdf:type schema:CreativeWork
211 grid-institutes:grid.410877.d schema:alternateName Construction Research Alliance, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia
212 Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia
213 schema:name Construction Research Alliance, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia
214 Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia
215 rdf:type schema:Organization
216 grid-institutes:grid.411463.5 schema:alternateName Young Researchers and Elit Club, South Tehran Branch, Islamic Azad University, Tehran, Iran
217 schema:name Young Researchers and Elit Club, South Tehran Branch, Islamic Azad University, Tehran, Iran
218 rdf:type schema:Organization
 




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


...