Structural health monitoring data has been widely acknowledged as a significant source for evaluating the performance and health conditions of structures. However, a holistic framework that efficiently incorporates monitored data into structural identification and, in turn, provides a realistic life-cycle performance assessment of structures is yet to be established. There are different sources of uncertainty, such as structural parameters, computer model bias and measurement errors. Neglecting to account for these factors results in unreliable structural identifications, consequent financial losses, and a threat to the safety of structures and human lives. This paper proposes a new framework for structural performance assessment that integrates a comprehensive probabilistic finite element model updating approach, which deals with various structural identification uncertainties and structural reliability analysis. In this framework, Gaussian process surrogate models are replaced with a finite element model and its associate discrepancy function to provide a computationally efficient and all-round uncertainty quantification. Herein, the structural parameters that are most sensitive to measured structural dynamic characteristics are investigated and used to update the numerical model. Sequentially, the updated model is applied to compute the structural capacity with respect to loading demand to evaluate its as-is performance. The proposed framework’s feasibility is investigated and validated on a large lab-scale box girder bridge in two different health states, undamaged and damaged, with the latter state representing changes in structural parameters resulted from overloading actions. The results from the box girder bridge indicate a reduced structural performance evidenced by a significant drop in the structural reliability index and an increased probability of failure in the damaged state. The results also demonstrate that the proposed methodology contributes to more reliable judgment about structural safety, which in turn enables more informed maintenance decisions to be made.

中文翻译：

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使用基于高斯过程的有限元模型更新和可靠性分析的计算有效结构性能评估

结构健康监测数据已被广泛认为是评估结构性能和健康状况的重要来源。然而，一个能够有效地将监测数据纳入结构识别并进而提供结构生命周期性能评估的整体框架尚未建立。有不同的不确定性来源，例如结构参数、计算机模型偏差和测量误差。忽视这些因素会导致不可靠的结构识别、随之而来的经济损失以及对结构和人类生命安全的威胁。本文提出了一种新的结构性能评估框架，该框架集成了一种综合的概率有限元模型更新方法，它处理各种结构识别不确定性和结构可靠性分析。在这个框架中，高斯过程替代模型被有限元模型及其相关的差异函数所取代，以提供计算效率高的全方位不确定性量化。在此，对测量的结构动态特性最敏感的结构参数进行了研究并用于更新数值模型。随后，更新后的模型用于计算与负载需求相关的结构容量，以评估其原样性能。拟议框架的可行性在两个不同健康状态下的大型实验室规模箱梁桥上进行了调查和验证，未损坏和损坏，后一种状态表示结构参数的变化是由超载动作引起的。箱梁桥的结果表明结构性能降低，结构可靠性指数显着下降和损坏状态下失效概率增加。结果还表明，所提出的方法有助于对结构安全做出更可靠的判断，从而能够做出更明智的维护决策。