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Sensitivity of uncertainties in performance prediction of deteriorating concrete structures

机译：劣化混凝土结构性能预测中的不确定性敏感性

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摘要

Deterioration models for the condition and reliability prediction of civil infrastructure facilities involve numerous assumptions and simplifications. Furthermore, input parameters of these models are fraught with uncertainties. A Bayesian methodology has been developed by the authors, which uses information obtained through health monitoring to improve the quality of prediction. The sensitivity of prior and posterior predicted performance to different input parameters of the deterioration models, and the effect of instrument and measurement uncertainty, is investigated in this paper. The results quantify the influence of these uncertainties and highlight the efficacy of the updating methodology based on integrating monitoring data. It has been found that the probabilistic posterior performance predictions are significantly less sensitive to most of the input uncertainties. Furthermore, updating the performance distribution based on 'event' outcomes is likely to be more beneficial than monitoring and updating of the input parameters on an individual basis.
机译：用于民用基础设施的条件和可靠性预测的恶化模型涉及许多假设和简化。此外，这些模型的输入参数充满不确定性。作者开发了贝叶斯方法，该方法使用通过健康监视获得的信息来提高预测质量。研究了劣化模型的不同输入参数对事前和事后预测性能的敏感性，以及仪器和测量不确定度的影响。结果量化了这些不确定性的影响，并强调了基于集成监控数据的更新方法的有效性。已经发现，概率后验性能预测对大多数输入不确定性不那么敏感。此外，基于“事件”结果更新性能分布可能比单独监视和更新输入参数更有利。

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