用迁移学习让一座桥的模型快速适配另一座，实现桥梁网络实时损伤评估。
Model-Based Transfer Learning for Real-Time Damage Assessment of Bridge Networks
- 基于神经网络代理模型，跨桥梁共享损伤识别知识。
- 在真实数据上验证，对损伤位置、程度和范围均高度敏感。
- 适合大规模桥梁网络监测，提升智能运维效率。
永久监测系统的广泛应用带来了大量数据，为结构评估提供了新机遇，但也带来了可扩展性挑战，尤其是在大型桥梁网络中。管理多座结构需要高效追踪和比较长期行为。为此，相似结构间的知识迁移至关重要。本研究提出一种基于模型的迁移学习方法，采用神经网络代理模型，使在一座桥梁上训练的模型能够适应具有相似特征的另一座桥梁。这些模型捕捉了共享的损伤机制，支持可扩展且通用的监测框架。该方法使用两座桥梁的真实数据进行了验证。转移后的模型被集成到贝叶斯推理框架中，基于监测数据的模态特征进行连续损伤评估。结果表明，该方法对损伤位置、严重程度和范围具有高敏感性。该方法提升了实时监测能力，实现了跨结构知识迁移，推动了网络级智能监测策略与韧性提升。
原文摘要 · Abstract (English)
The growing use of permanent monitoring systems has increased data availability, offering new opportunities for structural assessment but also posing scalability challenges, especially across large bridge networks. Managing multiple structures requires tracking and comparing long-term behaviour efficiently. To address this, knowledge transfer between similar structures becomes essential. This study proposes a model-based transfer learning approach using neural network surrogate models, enabling a model trained on one bridge to be adapted to another with similar characteristics. These models capture shared damage mechanisms, supporting a scalable and generalizable monitoring framework. The method was validated using real data from two bridges. The transferred model was integrated into a Bayesian inference framework for continuous damage assessment based on modal features from monitoring data. Results showed high sensitivity to damage location, severity, and extent. This approach enhances real-time monitoring and enables cross-structure knowledge transfer, promoting smart monitoring strategies and improved resilience at the network level.
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