Abstract
The selection of restoration methods for ancient architectures is of great significance for the protection of human cultural heritage. This paper proposes a novel restoration methods selection approach for wood components of Chinese ancient architectures, in which a multicriteria group decision-making (MCGDM) method with decision-making information is in the form of single-valued neutrosophic sets (SNNSs). Firstly, it establishes an index system by comprehensively considering subjective and objective criteria. In addition, the best-worst method (BWM) and the entropy weight method are combined to produce index weights. Furthermore, the TODIM method is utilized by the single-valued neutrosophic sets to prioritize restoration methods. Finally, a specific case of wood component restoration is conducted to demonstrate the practicability of the proposed model. The robustness and effectiveness of the proposed method is verified by sensitivity analysis and comparison analysis.
Highlights
We introduce some concepts and definitions, which will be useful in developing the wood components restoration methods selection model
We transform the linguistic terms into singlevalued neutrosophic numbers (SVNNs) according to Table 2
A selection model of restoration methods for wood components of Chinese ancient architectures has been developed in this paper, which is helpful to assisting experts to solve the problem of selecting appropriate restoration methods
Summary
The purpose of this study is to design a restoration methods selection model based on TODIM with single-valued neutrosophic sets for wood components of Chinese ancient architectures. Is model is designed to help experts select the most appropriate restoration method. E contributions of this research are summarized as follows: (1) developing an index system for the restoration of wood components of Chinese ancient architectures; (2) using single-valued neutrosophic weighted average operator to aggregate experts’ evaluation results, which make them more comprehensive and reliable; (3) applying the BWM method and entropy weight method to determine the index weights in case of unknown criteria and index weights; (4) introducing the TODIM method to obtain the ranking orders of alternatives; and (5) demonstrating the process of the proposed model by presenting an empirical study of a particular case
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