Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/45575
Title: Artificial neural network approach for grading of maintainability in wet areas of high-rise buildings
Authors: Chew, M.Y.L. 
De Silva, N. 
Tan, S.S.
Issue Date: 2004
Source: Chew, M.Y.L.,De Silva, N.,Tan, S.S. (2004). Artificial neural network approach for grading of maintainability in wet areas of high-rise buildings. Architectural Science Review 47 (1) : 27-42. ScholarBank@NUS Repository.
Abstract: A grading system using artificial neural networks to enhance decision-making of wet area design was developed. The model was derived from condition survey of 450 tall buildings and in-depth assessment of a further 120 tall buildings and interviews with the relevant building professionals. The system allows comparison of various alternative designs, materials, construction and maintenance practices, so as to achieve optimum solutions of technical attributes that lead to minimum life cycle maintenance cost.
Source Title: Architectural Science Review
URI: http://scholarbank.nus.edu.sg/handle/10635/45575
ISSN: 00038628
Appears in Collections:Staff Publications

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