Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/85020
Title: A study on analytic approaches to intelligent buildings assessment
Authors: Hong, Ju
Degree: Ph.D.
Issue Date: 2006
Abstract: This dissertation presents my PhD research into a study on generic analytical approaches to intelligent buildings assessment based on a novel prototype of lifecycle information management and knowledge utilization of intelligent buildings. This research aims to overcome the weakness existed in currently used intelligent buildings rating systems such as the Intelligent Building Index (IB Index) which has been developed by the Asian Institution of Intelligent Buildings (AIIB) since 2001, and to provide more accurate and effective toolkits for practitioners to appraise intelligent buildings, which have been established based on extensive literature review and questionnaire surveys. The analytical approaches being presented in this dissertation include an Analytic Network Process (ANP) approach, an Artificial Neural Network (ANN) approach, and a Knowledge-based Information Visualization (KIV) approach. The ANP approach is conducted to support decision making in the assessment of intelligent buildings under multicriteria. The ANN approach is introduced to facilitate the adoption of ANP approach in real-world appraisals. Both of them are integrated within a Tactical Intelligent Buildings Evaluation and Renovation (TIBER) model for intelligent buildings assessment. The KIV approach is originally developed to facilitate the adoption of currently used building rating systems such as the AIBI with a knowledge management toolkit. All these analytical approaches are integrated into the prototype of lifecycle information management and knowledge utilization of intelligent buildings, which is called the Data and Knowledge Management Platform of Intelligent Buildings (DAKIB platform). Experimental case studies are conducted to demonstrate the effectiveness of those analytical approaches.
Subjects: Hong Kong Polytechnic University -- Dissertations
Intelligent buildings -- Evaluation
Pages: xv, 296 leaves : ill. ; 30 cm
Appears in Collections:Thesis

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