Damages to various building structures often occur over their service life and can occasionally lead to severe structural failures, threatening the lives of its residents. In recent years, special attention has been paid to investigating various damages in buildings at the early stage to avoid failures and thereby minimize maintenance. Structural health monitoring can be used as a tool for damage quantification using vibration measurements. The application of various sensors for measuring accelerations, velocity and displacement in civil infrastructure monitoring has a long history in vibration-based approaches. These types of sensors reveal dynamic characteristics which are global in nature and ineffective in case of minor damage identification. In a practical application, the available damage detection approaches are not fully capable of quickly sensing and accurately identifying the realistic damage in structures. Research on damage identification from strain data is an interesting topic in recent days. Some work on the cross-correlation approach is now a centre of attraction and strictly confined to bridge or symmetric structures. The present paper uses strain data to validate the cross-correlation approach for detecting damage to building structures. The effectiveness of the methodology has been illustrated firstly on a simply supported beam, then on a 5-storey steel frame and a 6-storey scaled-down reinforced concrete shear building and lastly on a frame structure with moving load as a special case. The results show that this approach has the potential to identify damages in different kinds of civil infrastructure.

各种建筑结构的损坏经常发生在其使用寿命期间，有时会导致严重的结构失效，威胁居民的生命。近年来，人们特别注重对建筑物的各种损坏进行早期调查，以避免发生故障，从而最大限度地减少维护。结构健康监测可用作使用振动测量进行损伤量化的工具。在土木基础设施监测中，用于测量加速度、速度和位移的各种传感器的应用在基于振动的方法中有着悠久的历史。这些类型的传感器揭示了本质上是全局的动态特性，并且在轻微损坏识别的情况下无效。在实际应用中，现有的损伤检测方法并不完全能够快速感知和准确识别结构中的实际损伤。从应变数据中识别损伤的研究是近年来的一个有趣的话题。关于互相关方法的一些工作现在成为了关注的焦点，并且严格限于桥或对称结构。本文使用应变数据来验证用于检测建筑结构损坏的互相关方法。首先在简支梁上，然后在 5 层钢框架和 6 层缩小的钢筋混凝土剪力建筑上，最后在带有移动荷载的框架结构上作为特例，说明了该方法的有效性。结果表明，这种方法有可能识别不同类型民用基础设施的损坏情况。