{"title":"IFC建筑能源性能模拟适用性检查工具","authors":"G. Lilis, G. Giannakis, K. Katsigarakis, D. Rovas","doi":"10.1201/9780429506215-8","DOIUrl":null,"url":null,"abstract":"Data quality of BIM models is a key determinant in the value that can be extracted out of \nthese data. Yet, despite this importance the discussion of data quality is often relegated to an afterthought. \nOne potential use of BIM model data is the generation of building energy performance simulation models. \nWithin this paper a checking procedure is presented, to ensure that user-supplied BIM models meet \nthreshold data quality criteria and are suitable for the generation of input data files for energy analysis. \nThe checking procedure comprises of three sets of checking operations: consistency, correctness and completeness \nchecks. Consistency checks ensure that the input data are schema compatible; data completeness \nchecks invoke the sequential execution of checking rules to verify the existence of required data; data correctness \nchecks perform more elaborate detection of geometric errors appearing in surfaces, space volumes \nand clashes between architectural elements, which affect the building energy performance simulation model \ngeneration process. The checking procedure has been implemented and tested in two case-study buildings. \nAlthough the BIM modelers had been provided with modeling guidelines, multiple inaccuracies and data \ninsufficiencies were still present, highlighting the importance of a posteriori process implementation that \nchecks the validity of the model in relation to the purpose of its use.","PeriodicalId":193683,"journal":{"name":"eWork and eBusiness in Architecture, Engineering and Construction","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A tool for IFC building energy performance simulation suitability checking\",\"authors\":\"G. Lilis, G. Giannakis, K. Katsigarakis, D. Rovas\",\"doi\":\"10.1201/9780429506215-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data quality of BIM models is a key determinant in the value that can be extracted out of \\nthese data. Yet, despite this importance the discussion of data quality is often relegated to an afterthought. \\nOne potential use of BIM model data is the generation of building energy performance simulation models. \\nWithin this paper a checking procedure is presented, to ensure that user-supplied BIM models meet \\nthreshold data quality criteria and are suitable for the generation of input data files for energy analysis. \\nThe checking procedure comprises of three sets of checking operations: consistency, correctness and completeness \\nchecks. Consistency checks ensure that the input data are schema compatible; data completeness \\nchecks invoke the sequential execution of checking rules to verify the existence of required data; data correctness \\nchecks perform more elaborate detection of geometric errors appearing in surfaces, space volumes \\nand clashes between architectural elements, which affect the building energy performance simulation model \\ngeneration process. The checking procedure has been implemented and tested in two case-study buildings. \\nAlthough the BIM modelers had been provided with modeling guidelines, multiple inaccuracies and data \\ninsufficiencies were still present, highlighting the importance of a posteriori process implementation that \\nchecks the validity of the model in relation to the purpose of its use.\",\"PeriodicalId\":193683,\"journal\":{\"name\":\"eWork and eBusiness in Architecture, Engineering and Construction\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"eWork and eBusiness in Architecture, Engineering and Construction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1201/9780429506215-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"eWork and eBusiness in Architecture, Engineering and Construction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1201/9780429506215-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A tool for IFC building energy performance simulation suitability checking
Data quality of BIM models is a key determinant in the value that can be extracted out of
these data. Yet, despite this importance the discussion of data quality is often relegated to an afterthought.
One potential use of BIM model data is the generation of building energy performance simulation models.
Within this paper a checking procedure is presented, to ensure that user-supplied BIM models meet
threshold data quality criteria and are suitable for the generation of input data files for energy analysis.
The checking procedure comprises of three sets of checking operations: consistency, correctness and completeness
checks. Consistency checks ensure that the input data are schema compatible; data completeness
checks invoke the sequential execution of checking rules to verify the existence of required data; data correctness
checks perform more elaborate detection of geometric errors appearing in surfaces, space volumes
and clashes between architectural elements, which affect the building energy performance simulation model
generation process. The checking procedure has been implemented and tested in two case-study buildings.
Although the BIM modelers had been provided with modeling guidelines, multiple inaccuracies and data
insufficiencies were still present, highlighting the importance of a posteriori process implementation that
checks the validity of the model in relation to the purpose of its use.