A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Artificial Neural Network Approach for Grading of Maintainability in Wet Areas of High-Rise Buildings
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.
Artificial Neural Network Approach for Grading of Maintainability in Wet Areas of High-Rise Buildings
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.
Artificial Neural Network Approach for Grading of Maintainability in Wet Areas of High-Rise Buildings
Chew, M. Y.L. (author) / de Silva, Nayanthara (author) / Tan, S. S. (author)
Architectural Science Review ; 47 ; 27-42
2004-03-01
16 pages
Article (Journal)
Electronic Resource
Unknown
Artificial Neural Network Approach for Grading of Maintainability in Wet Areas of High-Rise Building
| British Library Online Contents | 2004
Artificial Neural Network Approach for Grading of Maintainability in Wet Areas of High-Rise Building
| Online Contents | 2004
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| British Library Online Contents | 2003
Maintainability problems of wet areas in high-rise residential buildings
| Online Contents | 2003