## 6 Case Studies
### A Case Study of Bridge Inspection  
To validate the effectiveness of the proposed ontology-based IR method, a single-span prestressed concrete hollow slab bridge with a length of 20 m and width of 7.5 m in Guangdong, China, was selected as a case study bridge. Figure 9 shows a schematic diagram of the main elements numbering of the bridge. There are 44 main elements to be inspected in this case, including each bearing, slab and wet joint, etc. Accessory elements such as lighting and marker sign are not included. According to the implemented process of the proposed IR method, the Blontology of this bridge was created and visualized in the Protege environment. Figure 10 shows partial elements and their spatial relationships. In this case, when two elements (e.g., \"1&wetJoint\" and \"2&slab\") can touch each other or they (e.g., \"1-1-3&bearing\" and \"1-1-4&bearing\") are adjacent, the spatial relationships among them can be established in the Blontology. This is because the inspector usually tends to inspect the target element that is close to the inspected element. Afterwards, the Blontology was manually uploaded to the Fuseki server for data conversion, and the transformed data were stored in the Jena TDB database. Bridge inspection could be implemented using the developed prototype system when the Blontology construction and data conversion were completed. Figure 11 shows the screenshot of the bridge inspection prototype system. An inspector was inspecting the bridge element called \"1&slab\" and typing in a defect \"crack\". Meanwhile, some elements (\"1&wetJoint\" and \"1-0-1&bearing\", for instance) are recommended as candidate elements to be inspected using the proposed prediction algorithm. After recording the rest of the defect-relevant information of the element, the inspector can choose an element from the candidate elements and go on inspecting this element without using the keyword-based IR method. Figure 9: Schematic diagram of elements numbering scheme of the selected bridge. To test the accuracy of the presented algorithm for predicting the target elements to be inspected, 4 inspectors are required to inspect all the 44 elements of this bridge according to their own route independently. Every time an element is inspected, the system provides the predicted elements. The prediction results can be accepted as true when the predicted elements include the target element to be inspected. The accuracy \(Acc_{0}\) is by:  
\[Acc_{0}=\frac{CT_{0}}{TT}\times 100\% \tag{1}\]  
Figure 11: The screenshot of the bridge inspection prototype system. Figure 10: The Blontology visualization in Protégé (partial view). where \(CT_{0}\) is the times of cases with a correct prediction, \(TT\) is the total times of predictions. In particular, if the first candidate element of the predicted elements is the target element, the accuracy \(Acc_{1}\) is computed by:  
\[Acc_{1}=\frac{CT_{1}}{TT}\times 100\% \tag{2}\]  
where \(CT_{1}\) is the times of cases in which the first candidate element is the target element. The context-aware computing framework (CACF) [21] is selected as the method for comparison because (1) the CACF shares the same goal that the proposed algorithm does, i.e., predicting the target element to be inspected, and (2) the CACF is used to accomplish the goal based on the logic that the elements beside an inspector are identified as the target elements, which is also the same as the logic of the proposed algorithm. The CACF firstly tracks the pose of the inspector and constructs a geometric model of the spatial region in the inspector's vision. Then, the CACF coordinates the geometry of the bridge elements with the constructed viewing region to compute the list of components within the inspector's spatial context. Lastly, the contextual elements are prioritized to determine the target elements. Figure 12 shows a comparison of the prediction results between our proposed algorithm and the CACF based on a global positioning system (GPS) and magnetic compass.