Multicriteria Model for Determining the Best and Low-Cost Methods of Industrial Heritage Transformation and Utilization under Fuzzy Inputs

Abstract

The renovation and utilization of industrial heritage are important issues in the field of sustainable urban renewal. The renovation of industrial heritage is influenced by various factors such as the value of a heritage site, its location, the positioning of renovation, and the cost of renovation. Most existing studies focus on the concept of renovation and the establishment of heritage management techniques. However, a low cost in the context of urban sustainability has a greater impact on renovation. Therefore, this paper takes Beijing Xinhua 1949 Cultural and Creative Industrial Park as an example, incorporates the cost–benefit ratio into the plans for a low-cost construction, and proposes a method to evaluate the performance of holistic low-cost construction during the whole life cycle of industrial heritage renovation and utilization. This study uses the AHP method to create an evaluation index system and fuzzy TOPSIS(FTOPSIS) to rank the solutions so as to establish a comprehensive evaluation system to thus evaluate industrial heritage renovation projects that are difficult to fully quantify, with the aim to obtain performance evaluation conclusions. The results of this study suggest that the definition of a low-cost renovation should not be limited to a reduction in investment costs but should also pay equal attention to the cost–benefit ratio before and after renovation, and that functional and spatial sustainability is another feasible strategy for achieving the sustainable renovation of industrial heritage.

A Study on Industrial Heritage Renewal Strategy Based on Hybrid Bayesian Network

“…We then constructed each index layer and performed pairwise comparisons. The values within pairwise comparisons of criteria were shown in Table 8 [91]. to calculate the weight of each criterion.…”

“…The specific data analysis process will be presented in Section 3. The construction of Bayesian networks generally follows three main methods, as outlined in Table 9 [91]. In this study, we primarily the third method.…”