Autonomous bridge inspection based on a generated digital model

Railway owners manage geographically dispersed networks comprising major elements of ageing infrastructure that are very susceptible to natural hazards. Consequently, transport agencies must address maintenance issues to guarantee serviceability and safety. This includes increased inspections and investing into structural health monitoring (SHM) programs. Regular SHM of existing bridges are usually scheduled during their service life to evaluate their health and as part of proactive maintenance where future deterioration is anticipated. Typically, a routine inspection consists of field measurements and visual observations made by a bridge inspector. However, disruption to civil infrastructure services due to scheduled maintenance work, visual inspection, etc. is increasing. The main purpose of SHM is to collect information such as geometry, previous and ongoing concrete deterioration, steel rebar corrosion, water seepage, concrete cover delamination, deflections, or settlements etc. The way such data are documented is through field inspection notes, freehand sketches, and photographs. Oftentimes, the data is stored in different systems and data collection and visualization still relies on paper-based record keeping processes. In addition, the procedure is highly dependent on the inspector’s experience [1], and knowledge of the structural behavior, together with the material properties of the system being investigated. The method has its limitations in the sense that only accessible parts are investigated due to time shortage, safety issues, or the difficult terrain in which the structure is sometimes located. This is especially true for large structures, such as bridges, where investigating the whole area would be highly time-consuming and potentially unsafe [2]. Honfi et al. [3] noted that the inspection’s duration is highly dependent on the bridge span (less than 10m can amount to 0.5 days and bigger than 100 m can amount to 20 days). In addition, defects can only be detected when their presence is visible to the naked eye, so they may already affect the life of the structure. Graybeal et al. [4] noted that routine inspections have relatively poor accuracy, with the following factors affecting the reliability of these results: inspector fear of traffic, near visual acuity, color vision, accessibility, and complexity. Furthermore, knowledge transfer from one inspection period to another becomes difficult when different inspectors carry out the investigation. Therefore, there is a strong need to identify new inspection and monitoring techniques for infrastructure that, in addition to being contactless and productive, reduce disruption, and improve the efficiency and reliability of the acquired data. With the expansion of the low-cost consumer cameras, photogrammetry could play an important role in supporting SHM on existing bridges. Vaghefi et al. [5] carried out a study of 12 remote sensing technologies and their potential to detect a series of common problems on US bridges. They concluded that 3D optical technologies have potential for documenting surface-related defects. Faster bridge inspection and visualization was described when aiming to quantify the defects over bridge deck surfaces with a low cost and easily deployable technology. Other studies reported on the use of photogrammetry as alternative to traditional measurement applied in laboratory environment; a review is done by Baqersad et al. [6]. The researchers themselves have a well-proven experience in applying photogrammetry for determination of failure mechanisms in concrete structures, defect detection, and monitoring full-field deformations. In an effort, by Popescu et al. [7], to develop new monitoring and inspection methods with a preliminary study, photogrammetry and terrestrial laser scanning utilized to generate the 3D model of six railway bridges located in northern Sweden. Results have shown an acceptable performance of 3D model of existed infrastructure generated by photogrammetry. Therefore, the current project will contribute with optical alternatives to traditional SHM approaches that are low-cost, suitable for field application, and easily deployable. The results indicate that bridge inspection on generated digital model is more reliable, productive, and accessible than traditional surveys. For 3D model generation we used photogrammetry technique, which is more efficient and cost-effective compared to the laser scanning, but improvements in accuracy and automation during the image acquisition phase are still required. The approach of autonomous defect detection performed on two case studies. Two types of defects including cracks, and block opening in a hard-to-access area was successfully detected and measured by pixel-wise mapping to an orthophoto. The proposed method has considerable potential in automated infrastructure inspection but some problems due to background noise remain to be overcome. The existence of noisy patterns such as shadows, dirt, and snow or water spots on surfaces makes damage detection very challenging, especially for the fine cracks. Overall, while the automated inspection technique proposed herein performs well, it clearly still requires supervision by a human inspector