The decreasing cost and improved sensor and monitoring system technology (e.g. fiber optics and strain gauges) have led to more measurements in close proximity to each other. When using such spatially dense measurement data in Bayesian system identification strategies, the correlation in the model prediction error can become significant. The widely adopted assumption of uncorrelated Gaussian error may lead to inaccurate parameter estimation and overconfident predictions, which may lead to sub-optimal decisions. This paper addresses the challenges of performing Bayesian system identification for structures when large datasets are used, considering both spatial and temporal dependencies in the model uncertainty. We present an approach to efficiently evaluate the log-likelihood function, and we utilize nested sampling to compute the evidence for Bayesian model selection. The approach is first demonstrated on a synthetic case and then applied to a (measured) real-world steel bridge. The results show that the assumption of dependence in the model prediction uncertainties is decisively supported by the data. The proposed developments enable the use of large datasets and accounting for the dependency when performing Bayesian system identification, even when a relatively large number of uncertain parameters is inferred.

翻译：随着传感器与监测系统技术（如光纤和应变计）成本降低与性能提升，邻近区域内的测量数据日益密集。在贝叶斯系统识别策略中使用这种空间密集的测量数据时，模型预测误差的相关性可能变得显著。广泛采用的不相关高斯误差假设可能导致参数估计不准和过于自信的预测，进而引发次优决策。本文针对使用大规模数据集时的结构贝叶斯系统识别挑战，综合考虑了模型不确定性中的空间与时间依赖性。我们提出了一种高效计算对数似然函数的方法，并利用嵌套采样计算贝叶斯模型选择的证据。该方法首先通过合成案例进行验证，随后应用于一座（实测）实际钢桥。结果表明，数据明确支持模型预测不确定性中存在相关性的假设。所提出的进展使得在贝叶斯系统识别过程中能够使用大规模数据集并考虑相关性，即使在推断出较多不确定参数的情况下依然适用。