Ontology type: schema:ScholarlyArticle
2010-12
AUTHORSKhalifa S. Al-Jabri, Saleh M. Al-Alawi
ABSTRACTThis paper describes an artificial neural networking (ANN) model developed to predict the behaviour of semi-rigid composite joints at elevated temperature. Three different semi-rigid composite joints were selected, two flexible end-plates and one flush end-plate. Seventeen different parameters were selected as input parameters representing the geometrical and mechanical properties of the joints as well as the joint’s temperature and the applied loading, and used to model the rotational capacity of the joints with increasing temperatures. Data from experimental fire tests were used for training and testing the ANN model. Results from nine experimental fire tests were evaluated with a total of 280 experimental cases. The results showed that the R2 value for the training and testing sets were 0.998 and 0.97, respectively. This indicates that results from the ANN model compared well with the experimental results demonstrating the capability of the ANN simulation techniques in predicting the behaviour of semi-rigid composite joints in fire. The described model can be modified to study other important parameters that can have considerable effect on the behaviour of joints at elevated temperatures such as temperature gradient, axial restraints, etc. More... »
PAGES337-347
http://scigraph.springernature.com/pub.10.1007/bf03215842
DOIhttp://dx.doi.org/10.1007/bf03215842
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1049911052
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/2008/0912",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Materials Engineering",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Engineering",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Sultan Qaboos University",
"id": "https://www.grid.ac/institutes/grid.412846.d",
"name": [
"Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, PO Box 33, PC 123, Al-Khod, Oman"
],
"type": "Organization"
},
"familyName": "Al-Jabri",
"givenName": "Khalifa S.",
"id": "sg:person.016131156661.68",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016131156661.68"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Sultan Qaboos University",
"id": "https://www.grid.ac/institutes/grid.412846.d",
"name": [
"Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, PO Box 33, PC 123, Al-Khod, Oman"
],
"type": "Organization"
},
"familyName": "Al-Alawi",
"givenName": "Saleh M.",
"id": "sg:person.012343467607.36",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012343467607.36"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/s0379-7112(01)00065-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008407805"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.engstruct.2005.12.009",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008791322"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jcsr.2006.01.003",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009092589"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.firesaf.2007.01.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010341121"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jcsr.2007.01.013",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014127514"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jcsr.2007.12.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014694647"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0954-1810(94)00011-s",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028083880"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jcsr.2004.09.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028411426"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0306-2619(00)00005-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039845496"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jcsr.2006.01.008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042587061"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/175247.175256",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044714778"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jcsr.2003.09.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048624011"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jcsr.2005.09.011",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051256763"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1061/(asce)0733-9445(1999)125:10(1188)",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1057599442"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.2307/2684922",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1070058214"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/hicss.1991.184056",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1086285334"
],
"type": "CreativeWork"
}
],
"datePublished": "2010-12",
"datePublishedReg": "2010-12-01",
"description": "This paper describes an artificial neural networking (ANN) model developed to predict the behaviour of semi-rigid composite joints at elevated temperature. Three different semi-rigid composite joints were selected, two flexible end-plates and one flush end-plate. Seventeen different parameters were selected as input parameters representing the geometrical and mechanical properties of the joints as well as the joint\u2019s temperature and the applied loading, and used to model the rotational capacity of the joints with increasing temperatures. Data from experimental fire tests were used for training and testing the ANN model. Results from nine experimental fire tests were evaluated with a total of 280 experimental cases. The results showed that the R2 value for the training and testing sets were 0.998 and 0.97, respectively. This indicates that results from the ANN model compared well with the experimental results demonstrating the capability of the ANN simulation techniques in predicting the behaviour of semi-rigid composite joints in fire. The described model can be modified to study other important parameters that can have considerable effect on the behaviour of joints at elevated temperatures such as temperature gradient, axial restraints, etc.",
"genre": "research_article",
"id": "sg:pub.10.1007/bf03215842",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1136691",
"issn": [
"1598-2351",
"2093-6311"
],
"name": "International Journal of Steel Structures",
"type": "Periodical"
},
{
"issueNumber": "4",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "10"
}
],
"name": "An advanced ANN model for predicting the rotational behaviour of semi-rigid composite joints in fire using the back-propagation paradigm",
"pagination": "337-347",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"edc29fa9a58a8a88a3def19a540b2490085acd654f73aa02d40e5f0eb6b4450b"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/bf03215842"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1049911052"
]
}
],
"sameAs": [
"https://doi.org/10.1007/bf03215842",
"https://app.dimensions.ai/details/publication/pub.1049911052"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T23:29",
"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/v90/0000000001_0000000264/records_8693_00000534.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007%2FBF03215842"
}
]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/bf03215842'
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/bf03215842'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf03215842'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf03215842'
This table displays all metadata directly associated to this object as RDF triples.
117 TRIPLES
21 PREDICATES
43 URIs
19 LITERALS
7 BLANK NODES