A BIM-based Object-oriented Data Model to Support Sustainable Demolition Waste Management Decision Making at End-of-Life
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Sustainable demolition waste management is rarely practiced within the construction industry. This is mainly due to the fact that the decision-making process for sustainable demolition waste management is a very resource-demanding and time-consuming task in terms of data collection and data management. The decision-making process includes multiple analyses of possible demolition waste management alternatives from economic, environmental, and social perspectives. Such analyses require waste managers to capture and manage huge amounts of data scattered within fragmented data sources at the end-of-life of a building. The process of capturing and managing this information for the building end-of-life would be time-consuming and costly. Therefore, the waste managers are reluctant to pursue sustainable demolition waste management practices in order to prevent potential delays and incurred costs. This research identified information that is required to conduct sustainable demolition waste management analyses. The identified information was then classified based on information sources. An object-oriented data model (OODM) was proposed to allow the waste managers to more efficiently store and manage the information at the end-of-life phase. Furthermore, a sustainable demolition waste management prototype application was developed to demonstrate how the required information is captured from different sources of data, stored within OODM classes, and retrieved from the integrated database. Finally, the proposed OODM was verified in terms of its scope, flexibility, and implementability. The goal of the research is to offer a method for storing and managing end-of-life information in an efficient and effective manner to support sustainable demolition waste management decision making. To achieve the goal, this dissertation outlines the objectives of the research, the methodologies used in developing the object-oriented data model, conclusions, limitations, and potential future research work.
- Doctoral Dissertations