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A neural network approach to assessing building facade maintainability in the tropics

Author

Listed:
  • M. Y. L. Chew
  • Nayanthara De Silva
  • S. S. Tan

Abstract

A model was developed to assess the maintainability of facade using neural network techniques. Inputs were derived from comprehensive studies of 570 tall buildings (more than 12 stories) through detailed field evaluation and interviews with professionals in the whole building delivery process. Sensitivity analysis showed that the most significant factors associated with facade maintainability include the system selection, detailing, accessibility and material performance.

Suggested Citation

  • M. Y. L. Chew & Nayanthara De Silva & S. S. Tan, 2004. "A neural network approach to assessing building facade maintainability in the tropics," Construction Management and Economics, Taylor & Francis Journals, vol. 22(6), pages 581-594.
    • Handle: RePEc:taf:conmgt:v:22:y:2004:i:6:p:581-594
    DOI: 10.1080/01446190310001631019
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    Cited by:

    1. Abdullah N. Naseer & Khalid S. Al-Gahtani & Ayman A. Altuwaim & Naif M. Alsanabani & Abdulmohsen S. Almohsen, 2024. "Selecting Building Façade Materials by Integrating Stepwise Weight Assessment Ratio Analysis and Weighted Aggregated Sum Product Assessment into Value Engineering," Sustainability, MDPI, vol. 16(11), pages 1-32, May.
    2. Kyriakidis, A. & Michael, A. & Illampas, R. & Charmpis, D.C. & Ioannou, I., 2019. "Comparative evaluation of a novel environmentally responsive modular wall system based on integrated quantitative and qualitative criteria," Energy, Elsevier, vol. 188(C).

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