Many methods of damage identification in bridge structures have focused on the use of numerical models, modal parameters or non-destructive damage tests as a means of condition assessment. These techniques can often be very effective but can also suffer from specific pitfalls such as, numerical model calibration issues for non-linear and inelastic behaviour, modal parameter sensitivity to environmental and operational conditions and bridge usage restrictions for non-destructive testing. This paper covers alternative approaches to damage identification of bridge structures using empirical parameters applied to measured vibration response data obtained from two field experiments of progressively damaged bridges subjected to ambient and vehicle-induced excitation, respectively. Numerous non-modal vibration-based damage features are detailed and selected for the assessment of either the ambient or vehicle-induced excitation data based on their inherent properties. The results of the application to two real bridges, one under ambient vibration and the other of forced vibration, demonstrate the robustness of the proposed damage features for damage identification using measurements of ambient and vehicle excitations. Moreover, this investigation has demonstrated that the novel empirical vibration parameters assessed are suitable for damage detection, localisation and quantification.

桥梁结构中的损伤识别的许多方法都集中在使用数值模型，模态参数或非破坏性损伤测试作为状态评估手段。这些技术通常可能非常有效，但也可能会遇到一些特定的陷阱，例如非线性和非弹性行为的数值模型校准问题，对环境和操作条件的模态参数敏感性以及无损检测的桥梁使用限制。本文涵盖了使用经验参数识别桥梁结构损伤的替代方法，这些经验参数分别应用于实测振动响应数据，该数据是分别从受到环境和车辆激励的渐进损伤桥梁的两次现场试验获得的。详细介绍了许多基于振动的非模态损伤特征，并根据其固有属性对环境或车辆诱发的激励数据进行了评估。应用于两座真实桥梁的结果表明，一座桥在环境振动下，另一座在强迫振动下，证明了所提出的损坏特征对于通过环境和车辆激励的测量进行识别的稳健性。此外，这项研究表明，评估的新型经验振动参数适用于损伤检测，定位和量化。通过环境和车辆激励的测量证明了提出的损坏特征用于识别损坏的鲁棒性。此外，这项研究表明，评估的新型经验振动参数适用于损伤检测，定位和量化。通过环境和车辆激励的测量证明了提出的损坏特征用于识别损坏的鲁棒性。此外，这项研究表明，评估的新型经验振动参数适用于损伤检测，定位和量化。