@prefix ocrer: <http://purl.org/net/OCRe/research.owl#> .
@prefix owl:   <http://www.w3.org/2002/07/owl#> .
@prefix scires: <http://vivoweb.org/ontology/scientific-research#> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix skos:  <http://www.w3.org/2004/02/skos/core#> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .
@prefix ocresd: <http://purl.org/net/OCRe/study_design.owl#> .
@prefix swo:   <http://www.ebi.ac.uk/efo/swo/> .
@prefix cito:  <http://purl.org/spar/cito/> .
@prefix geo:   <http://aims.fao.org/aos/geopolitical.owl#> .
@prefix ocresst: <http://purl.org/net/OCRe/statistics.owl#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix vivo:  <http://vivoweb.org/ontology/core#> .
@prefix event: <http://purl.org/NET/c4dm/event.owl#> .
@prefix vann:  <http://purl.org/vocab/vann/> .
@prefix foaf:  <http://xmlns.com/foaf/0.1/> .
@prefix c4o:   <http://purl.org/spar/c4o/> .
@prefix fabio: <http://purl.org/spar/fabio/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix thkoeln: <http://cris.nrw/hisinone#> .
@prefix vitro: <http://vitro.mannlib.cornell.edu/ns/vitro/0.7#> .
@prefix vitro-public: <http://vitro.mannlib.cornell.edu/ns/vitro/public#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ocresp: <http://purl.org/net/OCRe/study_protocol.owl#> .
@prefix bibo:  <http://purl.org/ontology/bibo/> .
@prefix obo:   <http://purl.obolibrary.org/obo/> .
@prefix ro:    <http://purl.obolibrary.org/obo/ro.owl#> .

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        rdfs:label                "Advances in engineering software" ;
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        rdfs:label                "Prediction of Dynamic Properties of Ultra-high Performance Concrete by an Artificial Intelligence Approach" , "RDF description of Prediction of Dynamic Properties of Ultra-high Performance Concrete by an Artificial Intelligence Approach - https://fis.th-koeln.de/vivo/individual/publ_19423" ;
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        bibo:abstract             "Significant advances in new generation of concrete led to construction of ultra-high performance concrete (UHPC). Since the demands of this engineering cementitious material have been increased, investigation on mechanical behaviour of this material is an interesting topic in today’s research programs. Over the years, strain-rate sensitive behavior of UHPC material has been studied by experimental tests. In the current research, an artificial intelligence (AI) system is implemented which can predict dynamic mechanical properties of UHPCs constructed by various mixtures. This system is able to predict elastic modulus, compressive strength and tensile strength, all under high rate of loading. The system is developed within the framework of case-based reasoning (CBR) methodology as a problem-solving AI method. CBR is a learning methodology which utilizes similar previous cases to solve particular new problems. In this paper, case-base of the implemented system is enriched by a literature study of numerous researches which tested various UHPCs. The proposed intelligent system has been applied to reduce human expert dependency and avoid time consuming experimental tests. The implemented system is evaluated by available results from experiments under high rate of loading." ;
        bibo:doi                  "10.1016/j.advengsoft.2018.10.002" ;
        bibo:issue                "127" ;
        bibo:pageEnd              "58" ;
        bibo:pageStart            "51" ;
        vitro:mostSpecificType    thkoeln:Journalartikel ;
        vivo:dateTimeValue        <http://cris.nrw/date2019> ;
        vivo:freetextKeyword      "Case-based reasoning" , "Dynamic mechanical properties" , "Ultra-high performance concrete" , "Artificial intelligence" ;
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        rdfs:label  "Institut für Allgemeinen Maschinenbau" ;
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                <https://fis.th-koeln.de/vivo/individual/publ_19423> .