Enhanced image processing and storage for defect evaluation in BIM of inspected steel bridges
Abstract Every structure undergoes a maintenance lifecycle, with increasing emphasis on structural health monitoring, defect diagnosis, and repair. As the demand for timely, effective, practical, and cost-efficient solutions grows, new techniques and processes are continually being developed. The tr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
|
| Series: | Discover Civil Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44290-025-00164-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823861649593860096 |
|---|---|
| author | Dogan Karluklu Cecilia Rinaldi Marianna Crognale Lucia Figuli Vincenzo Gattulli |
| author_facet | Dogan Karluklu Cecilia Rinaldi Marianna Crognale Lucia Figuli Vincenzo Gattulli |
| author_sort | Dogan Karluklu |
| collection | DOAJ |
| description | Abstract Every structure undergoes a maintenance lifecycle, with increasing emphasis on structural health monitoring, defect diagnosis, and repair. As the demand for timely, effective, practical, and cost-efficient solutions grows, new techniques and processes are continually being developed. The traditionally paper-based methods of bridge inspection, defect diagnosis, and repair are evolving into digital processes. The overall efficiency targeted by this digital transformation process can be increased by 35–50 percent when criteria such as time, cost, accuracy and applicability are evaluated. This paper presents an automated data collection and analysis procedure related to structural integrity evaluation. The study focuses on optimizing human-operated inspection procedures by incorporating advanced technologies, such as image processing techniques for defect identification in a digital environment, and integrating defect information into Building Information Modeling (BIM) systems. These focal points and processes, aiming to identify defect identification, and defect information management in BIM environment, are further explored and demonstrated through a case study involving a steel bridge. At the beginning of the study, approximately 160 defect images were collected, examined and some of them were selected for visualization purposes. The authors table a procedure that can be followed, starting with storing defect images and ending with defect visualization in the BIM environment, and present a discussion on the advantages and limits of the process by examining concepts such as automated data collection by drones, machine vision-based damage identification by color detection technique, BIM and Industry Foundation Classes standardisation applied to inspection data. |
| format | Article |
| id | doaj-art-7e1b16d6e2084ed5b0c48ed3faa01ef8 |
| institution | Kabale University |
| issn | 2948-1546 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Civil Engineering |
| spelling | doaj-art-7e1b16d6e2084ed5b0c48ed3faa01ef82025-02-09T12:53:57ZengSpringerDiscover Civil Engineering2948-15462025-02-012112110.1007/s44290-025-00164-5Enhanced image processing and storage for defect evaluation in BIM of inspected steel bridgesDogan Karluklu0Cecilia Rinaldi1Marianna Crognale2Lucia Figuli3Vincenzo Gattulli4Department of Structural and Geotechnical Engineering, Sapienza University of RomeDepartment of Structural and Geotechnical Engineering, Sapienza University of RomeDepartment of Structural and Geotechnical Engineering, Sapienza University of RomeUniversity of ZilinaDepartment of Structural and Geotechnical Engineering, Sapienza University of RomeAbstract Every structure undergoes a maintenance lifecycle, with increasing emphasis on structural health monitoring, defect diagnosis, and repair. As the demand for timely, effective, practical, and cost-efficient solutions grows, new techniques and processes are continually being developed. The traditionally paper-based methods of bridge inspection, defect diagnosis, and repair are evolving into digital processes. The overall efficiency targeted by this digital transformation process can be increased by 35–50 percent when criteria such as time, cost, accuracy and applicability are evaluated. This paper presents an automated data collection and analysis procedure related to structural integrity evaluation. The study focuses on optimizing human-operated inspection procedures by incorporating advanced technologies, such as image processing techniques for defect identification in a digital environment, and integrating defect information into Building Information Modeling (BIM) systems. These focal points and processes, aiming to identify defect identification, and defect information management in BIM environment, are further explored and demonstrated through a case study involving a steel bridge. At the beginning of the study, approximately 160 defect images were collected, examined and some of them were selected for visualization purposes. The authors table a procedure that can be followed, starting with storing defect images and ending with defect visualization in the BIM environment, and present a discussion on the advantages and limits of the process by examining concepts such as automated data collection by drones, machine vision-based damage identification by color detection technique, BIM and Industry Foundation Classes standardisation applied to inspection data.https://doi.org/10.1007/s44290-025-00164-5Bridge inspectionImage segmentationDefect detectionData collectionBuilding information modelling |
| spellingShingle | Dogan Karluklu Cecilia Rinaldi Marianna Crognale Lucia Figuli Vincenzo Gattulli Enhanced image processing and storage for defect evaluation in BIM of inspected steel bridges Discover Civil Engineering Bridge inspection Image segmentation Defect detection Data collection Building information modelling |
| title | Enhanced image processing and storage for defect evaluation in BIM of inspected steel bridges |
| title_full | Enhanced image processing and storage for defect evaluation in BIM of inspected steel bridges |
| title_fullStr | Enhanced image processing and storage for defect evaluation in BIM of inspected steel bridges |
| title_full_unstemmed | Enhanced image processing and storage for defect evaluation in BIM of inspected steel bridges |
| title_short | Enhanced image processing and storage for defect evaluation in BIM of inspected steel bridges |
| title_sort | enhanced image processing and storage for defect evaluation in bim of inspected steel bridges |
| topic | Bridge inspection Image segmentation Defect detection Data collection Building information modelling |
| url | https://doi.org/10.1007/s44290-025-00164-5 |
| work_keys_str_mv | AT dogankarluklu enhancedimageprocessingandstoragefordefectevaluationinbimofinspectedsteelbridges AT ceciliarinaldi enhancedimageprocessingandstoragefordefectevaluationinbimofinspectedsteelbridges AT mariannacrognale enhancedimageprocessingandstoragefordefectevaluationinbimofinspectedsteelbridges AT luciafiguli enhancedimageprocessingandstoragefordefectevaluationinbimofinspectedsteelbridges AT vincenzogattulli enhancedimageprocessingandstoragefordefectevaluationinbimofinspectedsteelbridges |