This paper proposes a multitask learning framework for probabilistic model updating by jointly using strain and acceleration measurements. This framework can enhance the structural damage assessment and response prediction of existing steel frame structures with quantified uncertainty. Multitask learning may be used to address multiple similar inference tasks simultaneously to achieve a more robust prediction performance by transferring useful knowledge from one task to another, even in situations of data scarcity. In the proposed model-updating procedure, a spatial frame is decomposed into multiple planar frames that are viewed as multiple tasks and jointly analyzed based on the hierarchical Bayesian model, leading to robust estimation results. The procedure uses a displacement–stress relationship in the modal space because it directly reflects the elemental stiffness and requires no prior knowledge concerning the mass, unlike most existing model-updating techniques. Validation of the proposed framework by using a full-scale vibration test on a one-story, one-bay by one-bay moment resisting steel frame, wherein structural damage to the column bases is simulated by loosening the anchor bolts, is presented. The experimental results suggest that the displacement–stress relationship has sufficient sensitivity toward localized damage, and the Bayesian multitask learning approach may result in the efficient use of measurements such that the uncertainty involved in model parameter estimation is reduced. The proposed framework facilitates more robust and informative model updating.

中文翻译：

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使用应变和加速度测量更新钢框架结构的概率模型：多任务学习框架

本文提出了一种联合使用应变和加速度测量来更新概率模型的多任务学习框架。该框架可以增强现有钢框架结构的结构损伤评估和响应预测，并具有量化的不确定性。多任务学习可用于同时解决多个类似的推理任务，即使在数据稀缺的情况下，也可以通过将有用的知识从一项任务转移到另一项任务来实现更稳健的预测性能。在所提出的模型更新过程中，空间框架被分解为多个平面框架，这些平面框架被视为多个任务并基于分层贝叶斯模型联合分析，从而产生稳健的估计结果。该过程在模态空间中使用位移-应力关系，因为它直接反映单元刚度，并且不需要有关质量的先验知识，这与大多数现有的模型更新技术不同。通过在一层、一湾一湾抗力矩钢框架上进行全尺寸振动测试，对所提出的框架进行了验证，其中通过松开地脚螺栓来模拟柱底座的结构损坏。实验结果表明，位移-应力关系对局部损伤具有足够的敏感性，贝叶斯多任务学习方法可能会导致测量的有效利用，从而减少模型参数估计中涉及的不确定性。所提出的框架有助于更稳健和信息丰富的模型更新。