Dominant causal factors of failure performance of cross-jointed segmental lining

力矩(物理) 刚度 参数统计 结构工程 非线性系统 灵敏度(控制系统) 接头(建筑物) 震级(天文学) 排名(信息检索) 结构体系 衰减 数学 计算机科学 统计 工程类 物理 光学 经典力学 量子力学 天文 电子工程 机器学习
作者
Xiaoyang Chen,Xinping Dong,Jinjin Zhang,Yingchun Cai,Guolong Ping
出处
期刊:Communications in Nonlinear Science and Numerical Simulation [Elsevier BV]
卷期号:130: 107731-107731 被引量:1
标识
DOI:10.1016/j.cnsns.2023.107731
摘要

To understand the failure behavior of segmental linings with inclined bolts, this study proposes a group of interrelated contribution-measuring scales (CMS) to evaluate the contributions and relative importance of causal factors. In the field of failure behavior research, parametric analysis is typically used to weight the sensitivity of a system to hypothetical changes in specific parameters; however, the true contribution of influencing factors to the system cannot be assessed via this sensitivity analysis. Identifying and ranking the contributions of causal factors to system effects is important for understanding as to why an incident occurs. To this end, this study first analyzes the member force breakdown structure of a crossed-jointed segmental lining, followed by the evaluation principle, evaluation indices and criteria, and analytical calculation method of the CMS. Finally, the CMS is used to analyze the failure behavior of a cross-jointed segmental lining. The results indicate that: (1) Based on the moment search method and moment separation algorithm, the CMS can be calculated analytically, and the maximum error between the analytical method and numerical solution does not exceed 3 %. (2) For the cross-jointed lining, the magnitude of the joint effect on the lining moment is related to the attenuation of the rotational stiffness of the joint and the system nonlinear characteristics are governed by the dominant joints. (3) Using the CMS, the structure and its variation in the mechanical response of the segmental lining system in the failure history can be measured and represented effectively and the governing variables can be identified quantitatively to simplify the understanding of the system failure behavior.

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