如何识别在役隧道衬砌上的土压力：来自解决非唯一性的贝叶斯反演的见解
How to identify earth pressures on in-service tunnel linings: Insights from Bayesian inversion to address non-uniqueness
AI总结：
针对在役隧道衬砌土压力反演的非唯一性问题，本研究提出一种贝叶斯方法量化不确定性，发现分布压力的均匀分量是主因，并验证了增加变形数据或观测内部法向力等缓解策略的有效性。
摘要：
识别在役交通隧道衬砌上的土压力对于其健康监测和性能预测至关重要，特别是在表现出性能不佳的结构中。由于压力计相关的高昂成本，基于易于观测的结构响应（如变形）的压力反演更受青睐。一个重大挑战在于反演结果的非唯一性，即不同的压力可能产生相似的结构响应。现有方法往往忽略了对这一关键问题的详细讨论。为填补这一空白，本研究引入了一种贝叶斯方法。所提出的统计框架有效地量化了由非唯一性引起的不确定性。进一步分析表明，分布压力中的均匀分量是非唯一性的主要来源。本文提供了缓解策略的见解，包括增加变形数据量或在隧道衬砌内部加入对法向力的观测——后者被证明更为有效。在数值案例研究中的实际应用证明了该方法的有效性。此外，我们的调查建议将变形测量精度保持在[-1, 1] mm范围内，以确保满意的结果。最后，讨论了该方法的不足之处及未来的潜在扩展。
英文摘要：
Identifying earth pressures on in-service transportation tunnel linings is essential for their health monitoring and performance prediction, particularly in structures that exhibit poor performance. Due to the high costs associated with pressure gauges, pressure inversion based on easily observed structural responses, such as deformations, is preferred. A significant challenge lies in the non-uniqueness of inversion results, where various pressures can yield similar structural responses. Existing approaches often overlook detailed discussions on this critical issue. In addressing this gap, this study introduces a Bayesian approach. The proposed statistical framework effectively quantifies the uncertainty induced by non-uniqueness. Further analysis identifies the uniform component in distributed pressures as the primary source of non-uniqueness. Insights into mitigation strategies are provided, including increasing the quantity of deformation data or incorporating an observation of internal normal force within the tunnel lining -- the latter proving to be notably more effective. A practical application in a numerical case study demonstrates the effectiveness of this approach. In addition, our investigation recommends maintaining deformation measurement accuracy within the range of [-1, 1] mm to ensure satisfactory outcomes. Finally, deficiencies and potential future extensions of this approach are discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
