The application of web of data technologies in building materials information modelling for construction waste analytics

Bilal, Muhammad; Oyedele, Lukumon O.; Munir, Kamran; Ajayi, Saheed O.; Akinade, Olugbenga O.; Owolabi, Hakeem A.; Alaka, Hafiz A.

Abstract

© 2017 Elsevier B.V. Predicting and designing out construction waste in real time is complex during building waste analysis (BWA) since it involves a large number of analyses for investigating multiple waste-efficient design strategies. These analyses require highly specific data of materials that are scattered across different data sources. A repository that facilitates applications in gaining seamless access to relatively large and distributed data sources of building materials is currently unavailable for conducting the BWA. Such a repository is the first step to developing a simulation tool for the BWA. Existing product data exchange ontologies and classification systems lack adequate modelling of building materials for the BWA. In this paper, we propose a highly resilient and data-agnostic building materials database. We use ontologies at the core of our approach to capture highly accurate and semantically conflicting data of building materials using the Resource Description Framework (RDF) and Web Ontology Language (OWL). Owing to the inherent capabilities of RDF, the architecture provides syntactical homogeneity while accessing the diverse and distributed data of building materials during the BWA. We use software packages such as Protégé and Oracle RDF Graph database for implementing the proposed architecture. Our research provides technical details and insights for researchers and software engineers who are seeking to develop the semantic repositories of similar kind of simulation applications that can be used for building waste performance analysis.

• Journal Article Type: Article
• Acceptance Date: Dec 22, 2016
• Publication Date: Apr 1, 2017
• Deposit Date: Mar 2, 2017
• Publicly Available Date: Mar 10, 2017
• Journal: Sustainable Materials and Technologies
• Electronic ISSN: 2214-9937
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Pages: 28-37
• DOI: https://doi.org/10.1016/j.susmat.2016.12.004
• Keywords: building materials database, RDF/OWL, ontologies, building waste analysis, construction waste minimisation, NoSQL systems, big data analytics
• Public URL: https://uwe-repository.worktribe.com/output/900334
• Publisher URL: http://dx.doi.org/10.1016/j.susmat.2016.12.004
• Contract Date: Mar 2, 2017