Ontology type: schema:ScholarlyArticle
2015-03-20
AUTHORSD. Jahed Armaghani, E. Tonnizam Mohamad, M. Hajihassani, S. V. Alavi Nezhad Khalil Abad, A. Marto, M. R. Moghaddam
ABSTRACTMines, quarries and construction sites face environmental impacts, such as flyrock, due to blasting operations. Flyrock may cause damage to structures and injury to human. Therefore, flyrock prediction is required to determine safe blasting zone. In this regard, 232 blasting operations were investigated in five granite quarries, Malaysia. Blasting parameters comprising maximum charge per delay and powder factor were prepared to predict flyrock using empirical and intelligent methods. An empirical graph was proposed to predict flyrock distance for different powder factor values. In addition, using the same datasets, two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict flyrock. Considering some model performance indices including coefficient of determination (R2), value account for and root mean squared error and also using simple ranking procedure, the best flyrock prediction models were selected. It was found that the ANFIS model can predict flyrock with higher performance capacity compared to ANN predictive model. R2 values of testing datasets are 0.925 and 0.964 for ANN and ANFIS techniques, respectively, suggesting the superiority of the ANFIS technique in predicting flyrock. More... »
PAGES109-121
http://scigraph.springernature.com/pub.10.1007/s00366-015-0402-5
DOIhttp://dx.doi.org/10.1007/s00366-015-0402-5
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1022416262
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"
}
],
"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": "D.",
"id": "sg:person.012214152011.74",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012214152011.74"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM, 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": "E.",
"id": "sg:person.010532067637.20",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010532067637.20"
],
"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": "M.",
"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": "Alavi Nezhad Khalil Abad",
"givenName": "S. V.",
"id": "sg:person.012466071103.38",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012466071103.38"
],
"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": "A.",
"id": "sg:person.01200512774.84",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01200512774.84"
],
"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": "M. R.",
"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/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/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/s006030050049",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044493601",
"https://doi.org/10.1007/s006030050049"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-011-0631-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039536792",
"https://doi.org/10.1007/s00521-011-0631-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-015-0400-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003281017",
"https://doi.org/10.1007/s00366-015-0400-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-012-0917-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044702409",
"https://doi.org/10.1007/s00521-012-0917-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/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/s10064-014-0588-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002537991",
"https://doi.org/10.1007/s10064-014-0588-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12517-012-0703-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032640964",
"https://doi.org/10.1007/s12517-012-0703-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10706-004-7068-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029378913",
"https://doi.org/10.1007/s10706-004-7068-x"
],
"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/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/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/s10064-014-0657-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016555821",
"https://doi.org/10.1007/s10064-014-0657-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-011-0210-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029681557",
"https://doi.org/10.1007/s00366-011-0210-5"
],
"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/s00366-009-0157-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044529681",
"https://doi.org/10.1007/s00366-009-0157-y"
],
"type": "CreativeWork"
}
],
"datePublished": "2015-03-20",
"datePublishedReg": "2015-03-20",
"description": "Mines, quarries and construction sites face environmental impacts, such as flyrock, due to blasting operations. Flyrock may cause damage to structures and injury to human. Therefore, flyrock prediction is required to determine safe blasting zone. In this regard, 232 blasting operations were investigated in five granite quarries, Malaysia. Blasting parameters comprising maximum charge per delay and powder factor were prepared to predict flyrock using empirical and intelligent methods. An empirical graph was proposed to predict flyrock distance for different powder factor values. In addition, using the same datasets, two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict flyrock. Considering some model performance indices including coefficient of determination (R2), value account for and root mean squared error and also using simple ranking procedure, the best flyrock prediction models were selected. It was found that the ANFIS model can predict flyrock with higher performance capacity compared to ANN predictive model. R2 values of testing datasets are 0.925 and 0.964 for ANN and ANFIS techniques, respectively, suggesting the superiority of the ANFIS technique in predicting flyrock.",
"genre": "article",
"id": "sg:pub.10.1007/s00366-015-0402-5",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1041785",
"issn": [
"0177-0667",
"1435-5663"
],
"name": "Engineering with Computers",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "32"
}
],
"keywords": [
"artificial neural network",
"adaptive neuro-fuzzy inference system",
"ANN predictive model",
"ANFIS technique",
"neuro-fuzzy inference system",
"prediction of flyrock",
"intelligent systems",
"intelligent method",
"higher performance capacity",
"neural network",
"inference system",
"model performance indices",
"testing dataset",
"same dataset",
"root mean squared error",
"flyrock prediction",
"mean squared error",
"ANFIS model",
"dataset",
"prediction model",
"flyrock distance",
"squared error",
"empirical graphs",
"computational methods",
"flyrock",
"predictive model",
"construction sites",
"operation",
"network",
"value account",
"performance index",
"graph",
"system",
"technique",
"blasting zone",
"model",
"powder factor",
"superiority",
"blasting operations",
"prediction",
"method",
"maximum charge",
"error",
"delay",
"coefficient of determination",
"environmental impacts",
"granite quarry",
"factor values",
"evaluation",
"distance",
"R2 values",
"performance capacity",
"mine",
"humans",
"account",
"quarry",
"parameters",
"coefficient",
"structure",
"values",
"procedure",
"zone",
"charge",
"regard",
"addition",
"capacity",
"impact",
"damage",
"determination",
"Malaysia",
"index",
"factors",
"sites",
"injury"
],
"name": "Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods",
"pagination": "109-121",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1022416262"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00366-015-0402-5"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00366-015-0402-5",
"https://app.dimensions.ai/details/publication/pub.1022416262"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-10T10:09",
"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_670.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s00366-015-0402-5"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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/s00366-015-0402-5'
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/s00366-015-0402-5'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00366-015-0402-5'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00366-015-0402-5'
This table displays all metadata directly associated to this object as RDF triples.
241 TRIPLES
22 PREDICATES
116 URIs
91 LITERALS
6 BLANK NODES