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Ontology-based approach for structural design considering low embodied energy and carbon

Hou, Shangjie, Li, Haijiang and Rezgui, Yacine 2015. Ontology-based approach for structural design considering low embodied energy and carbon. Energy and Buildings 102 , pp. 75-90. 10.1016/j.enbuild.2015.04.051

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Abstract

Buildings account a large share of energy consumption and greenhouse gas emission. Numerous methods have been applied to operate buildings more efficiently. However, the embodied energy and carbon are identified increasingly important in terms of sustainability throughout building life cycle. In traditional building structural design approach, the engineers pay little attention on environmental aspect which has been recognised as one of the most important factors to consider in modern integrated building design approach. The emergence of Semantic Web technologies provides more opportunities to assist structural engineers in improving building sustainability. The study presented in this paper investigates how ontology and Semantic Web rules can be used in a knowledge-based system, to represent information about structural design and sustainability, and to facilitate decision-making in design process by recommending appropriate solutions for different situations. A prototypical system named OntoSCS (Sustainable Concrete Structure Ontology), including a Web Ontology Language (OWL) ontology as knowledge base and SWRL rules providing reasoning mechanism, is developed to offer optimised structural design solutions and selections of material suppliers. Embodied energy and CO2e are used in the system as indicators to evaluate sustainability of structure. Both ontology reasoner and case studies are employed for system validation.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TD Environmental technology. Sanitary engineering
T Technology > TH Building construction
Uncontrolled Keywords: Sustainable structural design; Ontology; SWRL; Embodied energy and carbon; Decision support system
Publisher: Elsevier
ISSN: 0378-7788
Last Modified: 04 Jun 2017 13:37
URI: http://orca-mwe.cf.ac.uk/id/eprint/73163

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