A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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 ...
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 ...
Autonomous bridge inspection based on a generated digital model
Mirzazade, Ali (author)
2022-01-01
Licentiate thesis / Luleå University of Technology, 1402-1757
Theses
Electronic Resource
English
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