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Uncertainty quantification in model verification and validation as applied to large scale historic masonry monuments

机译:模型验证和确认中的不确定性量化,应用于大型历史性砖石建筑

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This publication focuses on the Verification and Validation (V&V) of numerical models for establishing confidence in model predictions, and demonstrates the complete process through a case study application completed on the Washington National Cathedral masonry vaults. The goal herein is to understand where modeling errors and uncertainty originate from, and obtain model predictions that are statistically consistent with their respective measurements. The approach presented in this manuscript is comprehensive, as it considers all major sources of errors and uncertainty that originate from numerical solutions of differential equations (numerical uncertainty), imprecise model input parameter values (parameter uncertainty), incomplete definitions of underlying physics due to assumptions and idealizations (bias error) and variability in measurements (experimental uncertainty). The experimental evidence necessary for reducing the uncertainty in model predictions is obtained through in situ vibration measurements conducted on the masonry vaults of Washington National Cathedral. By deploying the prescribed method, uncertainty in model predictions is reduced by approximately two thirds.
机译:该出版物侧重于建立对模型预测的信心的数值模型的验证和确认(V&V),并通过在华盛顿国家大教堂砌体金库上完成的案例研究应用程序演示了完整的过程。本文的目的是了解建模误差和不确定性的来源,并获得与各自测量值在统计上一致的模型预测。本手稿中介绍的方法是全面的,因为它考虑了所有主要的误差和不确定性来源,这些误差和不确定性源于微分方程的数值解(数值不确定性),不精确的模型输入参数值(参数不确定性),由于假设而对底层物理的不完整定义理想化(偏差误差)和测量的可变性(实验不确定性)。减少模型预测中的不确定性所必需的实验证据是通过对华盛顿国家大教堂的砖石拱顶进行的原位振动测量获得的。通过采用规定的方法,模型预测的不确定性减少了大约三分之二。

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