A methodology for weighting indicators of value assessment of historic building using AHP with experts' priorities. Issue 5 (3rd September 2022)

• Record Type: Journal Article
• Authors: Yang, Yulan; Yu, Beier; Tai, Huixin; Shen, Li; Liu, Fuying; Wang, Shaomei
• Is Part Of: Journal of Asian architecture and building engineering. Volume 21:Issue 5(2022)
• Page Start: 1814
• Page End: 1829
• Publication Date: 2022-09-03
• Subjects: Historic building -- value assessment -- weights -- AHP -- sequential clustering -- iterative clustering; Structural engineering -- East Asia -- Periodicals; Architectural design -- East Asia -- Periodicals; Architectural design; Structural engineering; Periodicals; Electronic journals; 720.95
• DOI: 10.1080/13467581.2021.1971529
• Languages: English
• ISSNs: 1346-7581

Abstract

ABSTRACT: The objective of this study is to provide a base for preservation of historic building, since weighting indicators is a foundation for value evaluation of historic building. A methodology of weighting indicators for the value assessment of historic building is proposed. The proposed methodology contains three methods. Method 1 assigns weights for indicators combining group analytical hierarchy process (AHP) with experts' average priorities. Method 2 assigns weights for indicators combining group AHP with experts' priorities by iterative clustering analysis. Method 3 assigns weights for indicators combining group AHP with experts' priorities by sequential clustering analysis. A frame containing 5 categories and 18 indicators is constructed for value assessment of historic building in Hangzhou city of China. The proposed three methods are used to weight these indicators. The main conclusions are drawn as: 1) the proposed methodology is effective to weight indicators by expert group decision; 2) method 2 and method 3 are capable of selection of individual experts with extremal preference and assigning less priorities for them, which assist in reduction of impact of them on weighting indicators; 3) method 3 optimizes method 2 by elimination of the risk of non-convergence caused by unconvincing iterative termination; and 4) method 3 improves method 2 by reducing the heavy workload of iterative clustering.