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.

翻译：本文提出了一种多任务学习框架，通过联合使用应变和加速度测量实现概率模型更新。该框架能够在量化不确定性的前提下，增强既有钢框架结构的损伤评估与响应预测能力。多任务学习可同时处理多个相似推理任务，通过任务间有用知识迁移实现更稳健的预测性能，即便在数据稀缺情况下亦然。所提出的模型更新流程将空间框架分解为多个平面框架，视为多项任务，基于层次贝叶斯模型进行联合分析，从而获得稳健的估计结果。该流程利用模态空间中的位移-应力关系，因其直接反映单元刚度，且与现有多数模型更新技术不同，无需关于质量的先验知识。通过在一层单跨双向抗弯钢框架的全尺寸振动试验中模拟柱脚损伤（通过松动锚栓实现），验证了所提框架的有效性。实验结果表明，位移-应力关系对局部损伤具有充分敏感性，且贝叶斯多任务学习方法可高效利用测量数据，从而降低模型参数估计中的不确定性。该框架有助于实现更稳健且信息更丰富的模型更新。