During the life-cycle of constructions various influences induce material defects that could affect the behavior of the structural system. Therefore, anomalies that affect heavily stressed constructions, such as bridges, need to be inspected and evaluated regarding their impact on the structural capacity. By using new technologies in the field of damage detection, e.g. laser scanners or unmanned aircraft systems (UAS), this process can be facilitated. However, the classification and assessment of detected anomalies must still be performed in a manual way by human experts due to the lack of machine-processable evaluation methods. In this paper an approach is proposed towards a machine-based damage evaluation, applying semantic web technologies on a new developed method for damage detection on constructions. Thereby, anomalies are detected based on a large amount of high-resolution images from which georeferenced point clouds are calculated by using photogrammetric methods. Using the geometric relations among the image positions and reconstructed points, image features such as anomalies are localized on a 3 dimensional surface. Based on these image features, a web ontology as semantic representation of the recorded damages is generated and linked with an ontology that contains information about the affected construction and its environment. By using predefined rules based on expert knowledge, the detected anomalies are classified and assessed automatically. The inferred information is then used to generate damage representations in a structural analysis model. Furthermore, the geometrical data, which are represented in a model created according to Building Information Modeling (BIM) standards, the semantic data as well as the structural data are linked by utilizing the Multimodel approach.

在建筑的生命周期中，各种影响都会诱发可能影响结构系统性能的材料缺陷。因此，需要检查和评估影响桥梁等承受较大压力的结构的异常对结构能力的影响。通过在损坏检测领域中使用新技术，例如激光扫描仪或无人机系统（UAS），可以简化此过程。但是，由于缺乏机器可处理的评估方法，因此仍必须由人类专家以手动方式对检测到的异常进行分类和评估。在本文中，提出了一种基于机器的损坏评估的方法，该方法将语义Web技术应用于一种新开发的用于建筑物损坏的检测方法。从而，基于大量高分辨率图像检测异常，然后使用摄影测量方法从中计算出地理参考点云。利用图像位置和重构点之间的几何关系，将诸如异常之类的图像特征定位在3维表面上。基于这些图像特征，将生成网络本体作为记录的损害的语义表示，并将其与包含有关受影响的建筑物及其环境的信息的本体链接。通过使用基于专家知识的预定义规则，可以对检测到的异常进行自动分类和评估。然后，将推断出的信息用于在结构分析模型中生成损伤表示。此外，几何数据