Structural health monitoring (SHM) activities are essential for achieving a realistic characterisation of bridge structural performance levels throughout the service life. These activities can help detect structural damage before the potential occurrence of component- or system-level structural failures. In addition to their application at discrete times, SHM systems can also be installed to provide long-term accurate and reliable data continuously throughout the entire service life of a bridge. Owing to their superior accuracy and long-term durability compared to traditional strain gages, fiber optic sensors are ideal in extracting accurate real-time strain and temperature data of bridge components. This paper presents a statistical damage detection and localisation approach to evaluate the performance of prestressed concrete bridge girders using fiber Bragg grating sensors. The presented approach employs Artificial Neural Networks to establish a relationship between the strain profiles recorded at different sensor locations across the investigated girder. The approach is capable of detecting and localising the presence of damage at the sensor location without requiring detailed loading information; accordingly, it can be suitable for long-term monitoring activities under normal traffic loads. Experimental laboratory data obtained from the structural testing of a large-scale prestressed concrete bridge girder is used to illustrate the approach.

结构健康监测（SHM）活动对于在整个使用寿命中实现桥梁结构性能水平的真实表征至关重要。这些活动可以帮助在潜在的组件级或系统级结构故障发生之前检测结构损坏。除了在离散时间上应用外，还可以安装SHM系统以在桥梁的整个使用寿命中连续提供长期准确而可靠的数据。与传统的应变计相比，由于其优越的精度和长期的耐用性，光纤传感器是提取桥梁部件实时准确的应变和温度数据的理想选择。本文提出了一种统计损伤检测和定位方法，以使用光纤布拉格光栅传感器评估预应力混凝土桥梁的性能。提出的方法采用人工神经网络来建立在研究的大梁上不同传感器位置记录的应变曲线之间的关系。该方法能够检测并定位传感器位置是否存在损坏，而无需详细的负载信息。因此，它适合在正常交通负载下进行长期监控活动。从大型预应力混凝土桥梁梁的结构测试获得的实验实验室数据用于说明该方法。提出的方法采用人工神经网络来建立在研究的大梁上不同传感器位置记录的应变曲线之间的关系。这种方法能够检测和定位传感器位置是否存在损坏，而无需详细的负载信息；因此，它适合在正常交通负载下进行长期监控活动。从大型预应力混凝土桥梁梁的结构测试获得的实验实验室数据用于说明该方法。提出的方法采用人工神经网络来建立在研究的大梁上不同传感器位置记录的应变曲线之间的关系。该方法能够检测并定位传感器位置是否存在损坏，而无需详细的负载信息。因此，它适合在正常交通负载下进行长期监控活动。从大型预应力混凝土桥梁梁的结构测试获得的实验实验室数据用于说明该方法。它适用于在正常流量负载下的长期监视活动。从大型预应力混凝土桥梁梁的结构测试获得的实验实验室数据用于说明该方法。它适用于在正常流量负载下的长期监视活动。从大型预应力混凝土桥梁梁的结构测试获得的实验实验室数据用于说明该方法。