@article{Zhou2026, 
author = {Boyang Zhou and Qian Yao and Jianghao Zhang and Chen Lin and Jian Wen},
title = {Exploration of technical models for assessing wind-induced damage risk to urban trees},
year = {2026},
journal = {Tree Health},
volume = {3},
number = {2},
pages = {49-59},
keywords = {urban trees, wind-induced damage risk, computational fluid dynamics, ground penetrating radar, biomechanical model, critical wind speed},
url = {https://www.sciopen.com/article/10.27035/j.cnki.issn2097-5279.20260206},
doi = {10.27035/j.cnki.issn2097-5279.20260206},
abstract = {Wind-induced damage to urban trees during extreme weather events poses severe challenges to ecological systems, socioeconomic stability, and public safety. This study integrates Computational Fluid Dynamics （CFD）, Ground Penetrating Radar （GPR）, Finite Element Analysis （FEA）, and a modified GALES model to propose a tree risk assessment framework combining non-destructive testing, wind field simulation, and mechanical stability assessment. By quantifying internal trunk structures and root system distributions using GPR non-destructive testing technology, combined with FEA, we proposed stability influencing factors to optimize the critical wind speed prediction model. Meanwhile, a rapid prediction model for urban micro-wind fields and wind directions was developed based on real-time meteorological data and campus geospatial information, enabling dynamic risk assessment of standing tree safety. The feasibility of this approach was validated using the Beijing Forestry University campus, a typical urban campus setting, as a case study. Results indicate that the parameterized individual tree model can accurately identify vulnerable parts of trees, thereby enhancing the accuracy of risk assessments. Tree morphological characteristics, soil parameters, and trunk defects combined with root distribution are significantly influenced by varying wind speeds and directions, which in turn affect trunk strength and root-soil anchorage capacity, ultimately impacting tree stability and safety. This research provides intuitive visualization of predictive outcomes, offering a data foundation and interdisciplinary theoretical basis for resilient urban tree management.}
}