JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (6): 252-259.doi: 10.12302/j.issn.1000-2006.202207035

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Research on the disease monitoring and structural protection of garden rockery heritage based on an finite element analysis

ZHANG Qingping(), WANG Cencen, ZHANG Xiaodong   

  1. College of Landscape Architecture,Nanjing Forestry University,Nanjing 210037,China
  • Received:2022-07-26 Revised:2022-09-08 Online:2024-11-30 Published:2024-12-10

Abstract:

【Objective】This study provides a new perspective on the research of structural protection for Chinese garden rockeries, utilizing the finite element method as a research tool. It aims to determine the primary damage locations within rockeries. This research further advances the methods and technologies for quantitative analysis and scientific preservation of rockeries, enhancing the scientific rigor and precision of rockery heritage protection methods in Chinese classical gardens.【Method】Based on a three-dimensional laser scanning technology, this study proposes two modeling methods for complex structures such as rockeries. Starting with finite elements in the rockery heritage protection research and considering the entire rockery as the research object, two types of mechanical models of rockeries are developed for numerical simulation.【result】An numerical simulation analysis is conducted on the four famous stones and the Heyuan Huangshi rockery, resulting in the main stress cloud map, shear stress cloud map, and strain cloud map for these elements. This analysis clarified the maximum values and their precise locations, further identifying the stress vulnerabilities and critical monitoring areas within the rockery heritage structure.【Conclusion】This method provides significant value for the preventive protection of rockeries, a complex gardening element. The monitoring and protection system should not exclude rockeries without apparent structural damage.

Key words: conservation of garden heritage, rockery heritage, Ansys, structural protection, disease monitoring

CLC Number: