ETH Zurich, Future Cities Laboratory, Singapore-ETH Centre, 1 CREATE Way, CREATE Tower, Singapore 138602, Singapore.
Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland.
Sensors (Basel). 2018 May 24;18(6):1702. doi: 10.3390/s18061702.
The aim of structural identification is to provide accurate knowledge of the behaviour of existing structures. In most situations, finite-element models are updated using behaviour measurements and field observations. Error-domain model falsification (EDMF) is a multi-model approach that compares finite-element model predictions with sensor measurements while taking into account epistemic and stochastic uncertainties-including the systematic bias that is inherent in the assumptions behind structural models. Compared with alternative model-updating strategies such as residual minimization and traditional Bayesian methodologies, EDMF is easy-to-use for practising engineers and does not require precise knowledge of values for uncertainty correlations. However, wrong parameter identification and flawed extrapolation may result when undetected outliers occur in the dataset. Moreover, when datasets consist of a limited number of static measurements rather than continuous monitoring data, the existing signal-processing and statistics-based algorithms provide little support for outlier detection. This paper introduces a new model-population methodology for outlier detection that is based on the expected performance of the as-designed sensor network. Thus, suspicious measurements are identified even when few measurements, collected with a range of sensors, are available. The structural identification of a full-scale bridge in Exeter (UK) is used to demonstrate the applicability of the proposed methodology and to compare its performance with existing algorithms. The results show that outliers, capable of compromising EDMF accuracy, are detected. Moreover, a metric that separates the impact of powerful sensors from the effects of measurement outliers have been included in the framework. Finally, the impact of outlier occurrence on parameter identification and model extrapolation (for example, reserve capacity assessment) is evaluated.
结构识别的目的是提供对现有结构行为的准确了解。在大多数情况下，使用行为测量和现场观测来更新有限元模型。误差域模型伪造（EDMF）是一种多模型方法，它在考虑认知和随机不确定性（包括结构模型假设固有的系统偏差）的同时，将有限元模型预测与传感器测量进行比较。与替代的模型更新策略（如残差最小化和传统贝叶斯方法）相比，EDMF 易于实践工程师使用，并且不需要对不确定性相关性值有精确的了解。然而，当数据集中存在未检测到的异常值时，可能会导致错误的参数识别和有缺陷的外推。此外，当数据集由有限数量的静态测量而不是连续监测数据组成时，现有的基于信号处理和统计的算法几乎无法为异常值检测提供支持。本文介绍了一种新的基于设计传感器网络预期性能的模型群体异常值检测方法。因此，即使只有少数测量值（使用各种传感器收集），也可以识别可疑测量值。本文使用埃克塞特（英国）的一座全尺寸桥梁的结构识别来演示所提出方法的适用性，并将其性能与现有的算法进行比较。结果表明，能够影响 EDMF 准确性的异常值被检测到。此外，框架中还包含了一个从测量异常值的影响中分离出强大传感器影响的度量。最后，评估了异常值发生对参数识别和模型外推（例如，储备容量评估）的影响。
