Abstract
With the accelerated growth of scale, complexity and diversity of construction projects, accurate demand analysis has become a crucial issue impacting effect and efficiency in collaborative design. However, comprehending deviations between clients and designers is ubiquitous because of the information asymmetry. Choosing a best practice as reference is considered to be a promising method to help solve this kind of problems, so the key problem is the best practices selection. This paper proposes a novel data mining approach based on Bayesian probability for best practice recommendation (BPR), integrating the satisfactions of former projects and similarity between former projects and the recent one. This approach is applicable because it reasons top ranking practices to help clarify requirement, so that design collaboration and clients' satisfaction should be improved with more probability. A green building retrofit example is presented to demonstrate the application of this approach.
Original language | English |
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Title of host publication | ICCREM 2014 |
Subtitle of host publication | Smart Construction and Management in the Context of New Technology - Proceedings of the 2014 International Conference on Construction and Real Estate Management |
Publisher | American Society of Civil Engineers (ASCE) |
Pages | 721-732 |
Number of pages | 12 |
ISBN (Electronic) | 9780784413777 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Event | 2014 International Conference on Construction and Real Estate Management: Smart Construction and Management in the Context of New Technology, ICCREM 2014 - Kunming, China Duration: 27 Sept 2014 → 28 Sept 2014 |
Conference
Conference | 2014 International Conference on Construction and Real Estate Management: Smart Construction and Management in the Context of New Technology, ICCREM 2014 |
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Country/Territory | China |
City | Kunming |
Period | 27/09/14 → 28/09/14 |
ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction