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Details

Autor(en) / Beteiligte
Titel
Automated management of green building material information using web crawling and ontology
Ist Teil von
  • Automation in construction, 2019-06, Vol.102, p.230-244
Ort / Verlag
Amsterdam: Elsevier B.V
Erscheinungsjahr
2019
Link zum Volltext
Quelle
Elsevier ScienceDirect Journals Complete
Beschreibungen/Notizen
  • Various green building certifications have been discussed as a part of efforts to realize sustainable development. In some countries, it is mandatory to acquire certifications for buildings above a certain scale. As a result, the demand for green building certifications has increased. Various studies have been conducted on the efficient performance of green building certification tasks. To improve the tasks for material information management, the following problems should be addressed: 1) Evaluation of material selection is difficult because of limitations on the amount and quality of the collected information; 2) Unnecessary duplication of work occurs because the important information created at each stage of a project is not delivered efficiently to the next step; 3) Information management for material information, which requires continuous updating, is not sufficient. Therefore, this study proposes an automated process of collecting and classifying Green Building Material Information (GBMI) using “web crawling” and “ontology” to improve the work efficiency of material information management. The proposed process is verified for interior finishing materials, which are a part of green building certification tasks. The proposed process can reduce the time required for the information management of building materials and eliminate human errors. •We develop a web-crawler and two ontologies that enable automated information collection and classification of Green Building Material Information(GBMI).•The scope of automated process is limited to internal finishing material.•We propose automated approach to manage GBMI.
Sprache
Englisch
Identifikatoren
ISSN: 0926-5805
eISSN: 1872-7891
DOI: 10.1016/j.autcon.2019.01.015
Titel-ID: cdi_proquest_journals_2210866863

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