Suitability evaluation of underground space based on finite interval cloud model and genetic algorithm combination weighting. (February 2021)

• Record Type: Journal Article
• Authors: Tan, Fei; Wang, Jun; Jiao, Yu-Yong; Ma, Bangchuang; He, Lingling
• Is Part Of: Tunnelling and underground space technology. Volume 108(2021)
• Publication Date: 2021-02
• Subjects: Underground space -- Suitability evaluation -- Finite interval cloud model -- Genetic algorithm -- Improved CRITIC method; Tunneling -- Periodicals; Underground construction -- Periodicals; Tunnels -- Periodicals; Underground areas -- Periodicals; 624.193
• DOI: 10.1016/j.tust.2020.103743
• Languages: English
• ISSNs: 0886-7798

Abstract

Highlights: The finite interval cloud model is introduced into the suitability evaluation. A genetic algorithm is adopted to solve the combination weights. The fuzziness and randomness of geological indicators are addressed. The discreteness, correlation, and contrast intensity of geological data are considered. Abstract: The development and utilization of urban underground space is an inevitable solution for urban sustainable development. Therefore, it is particularly important to perform suitability evaluation of underground space in various regions of a city. To improve the rationality of the evaluation results, this study proposes an evaluation method based on the finite interval cloud model and genetic algorithm combination weighting. The study considers the starting area of the Wuhan Changjiang New Town as the evaluation area. An evaluation index system is established for 12 indexes of five geological factors. The cloud characteristic parameters of each evaluation factor belonging to different classification levels are obtained through the finite interval cloud model. The forward finite interval cloud generator is used to generate cloud drop graphs of the indexes. Further, the certainty degrees of the evaluation factors belonging to different levels are obtained, which provide the mapping of uncertainty and randomness between the semantic variables and the index values. Subjective and objective weights of indexes are calculated by an improved analytic hierarchy process (AHP) and the criteria importance through intercriteria correlation (CRITIC) method improved with the entropy method. The combination weights are solved by the application of the genetic algorithm, which can comprehensively consider the attributes of discreteness, correlation, and contrast intensity of geological data. By following the above-mentioned procedures, the comprehensive certainty degree of the evaluation sample is obtained according to the combination weights and certainty degrees of the evaluation indexes. According to the principle of the maximum membership degree, the suitability grades of the underground space are determined. This evaluation method comprehensively considers the fuzziness of the suitability grade boundary of the underground space and the advantages of the subjective and objective weighting methods. Thus, the method ensures rationality of the evaluation results to the greatest extent, thereby providing a certain guiding significance for the development of underground space.