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Cited by 1253D Surveying and Cultural HeritageInfrastructure Maintenance and MonitoringStructural Health Monitoring Techniques

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Preserving our heritage: A photogrammetry-based digital twin framework for monitoring deteriorations of historic structures

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Kong, Xiangxiong, Hucks, Ronny Garrett·2023·Automation in Construction
DOI 10.1016/j.autcon.2023.104928
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Research works printed but not linked: 57

  1. Belal, 2019, Heritage in post-war period challenges and solutions, IFAC-PapersOnLine, 52, 252, 10.1016/j.ifacol.2019.12.491 · DOI
  2. Farhan, 2022, Revitalizing the historical center of Al-Najaf city in Iraq: learning from the British conservation experiences, J. Cult. Heritage Manag. Sustain. Develop., 12, 513, 10.1108/JCHMSD-01-2020-0002 · DOI
  3. Hammer, 2018, Remote assessments of the archaeological heritage situation in Afghanistan, J. Cult. Herit., 33, 125, 10.1016/j.culher.2017.12.008 · DOI
  4. Coleman, 2022, Official response to the Russian war crimes in Ukraine, Curator: The Museum J., 65, 15, 10.1111/cura.12483 · DOI
  5. Vatan, 2012, Condition survey of historic buildings by visual inspection-case study: Murat pasha mosque, Int. J. Electron. Mech. Mechatron. Eng., 2, 147
  6. De Stefano, 2016, Structural health monitoring of historical heritage in Italy: some relevant experiences, J. Civ. Struct. Heal. Monit., 6, 83, 10.1007/s13349-016-0154-y · DOI
  7. O’Shea, 2020, Design of a BIM integrated structural health monitoring system for a historic offshore lighthouse, Buildings, 10, 131, 10.3390/buildings10070131 · DOI
  8. Gattulli, 2016, Dynamic testing and health monitoring of historic and modern civil structures in Italy, Struct. Monit. Mainten., 3, 71
  9. Ubertini, 2016, Vibration-based structural health monitoring of a historic bell-tower using output-only measurements and multivariate statistical analysis, Struct. Health Monit., 15, 438, 10.1177/1475921716643948 · DOI
  10. Masciotta, 2017, The importance of structural monitoring as a diagnosis and control tool in the restoration process of heritage structures: a case study in Portugal, J. Cult. Herit., 27, 36, 10.1016/j.culher.2017.04.003 · DOI
  11. Blanco, 2018, An integrated structural health monitoring system for determining local/global responses of historic masonry buildings, Struct. Control. Health Monit., 25, 10.1002/stc.2196 · DOI
  12. Zou, 2019, CNN-based statistics and location estimation of missing components in routine inspection of historic buildings, J. Cult. Herit., 38, 221, 10.1016/j.culher.2019.02.002 · DOI
  13. Wang, 2020, Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning, Comput.-Aided Civil and Infrastruct. Eng., 35, 277, 10.1111/mice.12488 · DOI
  14. Mansuri, 2022, Artificial intelligence-based automatic visual inspection system for built heritage, Smart Sustain. Built Environ., 11, 622, 10.1108/SASBE-09-2020-0139 · DOI
  15. Pathak, 2021, An object detection approach for detecting damages in heritage sites using 3-D point clouds and 2-D visual data, J. Cult. Herit., 48, 74, 10.1016/j.culher.2021.01.002 · DOI
  16. Hatır, 2021, The deep learning method applied to the detection and mapping of stone deterioration in open-air sanctuaries of the Hittite period in Anatolia, J. Cult. Herit., 51, 37, 10.1016/j.culher.2021.07.004 · DOI
  17. Mishra, 2021, Machine learning techniques for structural health monitoring of heritage buildings: a state-of-the-art review and case studies, J. Cult. Herit., 47, 227, 10.1016/j.culher.2020.09.005 · DOI
  18. Wang, 2018, Automatic internal crack detection from a sequence of infrared images with a triple-threshold canny edge detector, Meas. Sci. Technol., 29, 10.1088/1361-6501/aa9857 · DOI
  19. Reagan, 2018, Feasibility of using digital image correlation for unmanned aerial vehicle structural health monitoring of bridges, Struct. Health Monit., 17, 1056, 10.1177/1475921717735326 · DOI
  20. Kong, 2019, Non-contact fatigue crack detection in civil infrastructure through image overlapping and crack breathing sensing, Autom. Constr., 99, 125, 10.1016/j.autcon.2018.12.011 · DOI
  21. Kong, 2018, Image registration-based bolt loosening detection of steel joints, Sensors, 18, 1000, 10.3390/s18041000 · DOI
  22. Galantucci, 2019, Advanced damage detection techniques in historical buildings using digital photogrammetry and 3D surface anlysis, J. Cult. Herit., 36, 51, 10.1016/j.culher.2018.09.014 · DOI
  23. Sangirardi, 2022, Detecting damage evolution of masonry structures through computer-vision-based monitoring methods, Buildings, 12, 831, 10.3390/buildings12060831 · DOI
  24. Albertz, 2002, Albrecht Meydenbauer-Pioneer of photogrammetric documentation of the cultural heritage, Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci., 34, 19
  25. Colomina, 2014, Unmanned aerial systems for photogrammetry and remote sensing: a review, ISPRS J. Photogramm. Remote Sens., 92, 79, 10.1016/j.isprsjprs.2014.02.013 · DOI
  26. Pepe, 2018, Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors, Eur. J. Remote Sens., 51, 412, 10.1080/22797254.2018.1444945 · DOI
  27. Omar, 2018, Towards an automated photogrammetry-based approach for monitoring and controlling construction site activities, Comput. Ind., 98, 172, 10.1016/j.compind.2018.03.012 · DOI
  28. Braun, 2019, Combining inverse photogrammetry and BIM for automated labeling of construction site images for machine learning, Autom. Constr., 106, 10.1016/j.autcon.2019.102879 · DOI
  29. Galantucci, 2018, A proposal for a new standard quantification of damages of cultural heritages, based on 3D scanning, SCIRES-IT-SCIentific RESearch and Inform. Technol., 8, 121
  30. Galantucci, 2019, Advanced damage detection techniques in historical buildings using digital photogrammetry and 3D surface anlysis, J. Cult. Herit., 36, 51, 10.1016/j.culher.2018.09.014 · DOI
  31. Biscarini, 2020, UAV photogrammetry, infrared thermography and GPR for enhancing structural and material degradation evaluation of the Roman masonry bridge of Ponte Lucano in Italy, NDT & E Int., 115, 10.1016/j.ndteint.2020.102287 · DOI
  32. Forlin, 2018, Assessing earthquake effects on archaeological sites using photogrammetry and 3D model analysis, Digit. Appl. Archaeol. Cult. Heritage, 9
  33. Ulvi, 2022, Using UAV photogrammetric technique for monitoring, change detection, and analysis of archeological excavation sites, J. Comput. Cult. Heritage (JOCCH), 15, 1, 10.1145/3522742 · DOI
  34. Galantucci, 2019, Advanced damage detection techniques in historical buildings using digital photogrammetry and 3D surface anlysis, J. Cult. Herit., 36, 51, 10.1016/j.culher.2018.09.014 · DOI
  35. Zhao, 2021, Structural health monitoring and inspection of dams based on UAV photogrammetry with image 3D reconstruction, Autom. Constr., 130, 10.1016/j.autcon.2021.103832 · DOI
  36. Jafari, 2017, Deformation tracking in 3D point clouds via statistical sampling of direct cloud-to-cloud distances, J. Nondestruct. Eval., 36, 1, 10.1007/s10921-017-0444-2 · DOI
  37. Kong, 2021, Identifying geomorphological changes of coastal cliffs through point cloud registration from UAV images, Remote Sens., 13, 3152, 10.3390/rs13163152 · DOI
  38. Thelen, 2022, A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies, Struct. Multidiscip. Optim., 65, 1, 10.1007/s00158-022-03425-4 · DOI
  39. Grieves, 2014, Digital twin: manufacturing excellence through virtual factory replication, White Paper, 1, 1
  40. Jiang, 2021, Digital twin and its implementations in the civil engineering sector, Autom. Constr., 130, 10.1016/j.autcon.2021.103838 · DOI
  41. Mohammadi, 2021, Quality evaluation of digital twins generated based on UAV photogrammetry and TLS: bridge case study, Remote Sens., 13, 3499, 10.3390/rs13173499 · DOI
  42. Marra, 2021, Combining integrated informative system and historical digital twin for maintenance and preservation of artistic assets, Sensors, 21, 5956, 10.3390/s21175956 · DOI
  43. Falcone, 2021, From architectural survey to continuous monitoring: graph-based data management for cultural heritage conservation with digital twins, Int. Arch. Photogramm., Remote Sens. Spatial Inform. Sci., 43, 47, 10.5194/isprs-archives-XLIII-B4-2021-47-2021 · DOI
  44. Pregnolato, 2022, Towards civil engineering 4.0: concept, workflow and application of digital twins for existing infrastructure, Autom. Constr., 141, 10.1016/j.autcon.2022.104421 · DOI · Paper page
  45. Ullman, 1979, The interpretation of structure from motion, Proceed. Royal Soc. of London. Series B. Biol. Sci., 203, 405
  46. Andrew, 2001, Multiple view geometry in computer vision, Kybernetes, 30, 1333, 10.1108/k.2001.30.9_10.1333.2 · DOI
  47. Kingsland, 2020, Comparative analysis of digital photogrammetry software for cultural heritage, Digit. Appl. Archaeol. Cult. Heritage, 18
  48. Aicardi, 2018, Recent trends in cultural heritage 3D survey: the photogrammetric computer vision approach, J. Cult. Herit., 32, 257, 10.1016/j.culher.2017.11.006 · DOI
  49. Chen, 1992, Object modelling by registration of multiple range images, Image Vis. Comput., 10, 145, 10.1016/0262-8856(92)90066-C · DOI
  50. Huttenlocher, 1993, Comparing images using the Hausdorff distance, IEEE Trans. Pattern Anal. Mach. Intell., 15, 850, 10.1109/34.232073 · DOI · Paper page
  51. Lague, 2013, Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (NZ), ISPRS J. Photogramm. Remote Sens., 82, 10, 10.1016/j.isprsjprs.2013.04.009 · DOI
  52. Graves, 2022, Full-scale highway bridge deformation tracking via photogrammetry and remote sensing, Remote Sens., 14, 2767, 10.3390/rs14122767 · DOI
  53. Smith, 2016, Structure from motion photogrammetry in physical geography, Prog. Phys. Geogr., 40, 247, 10.1177/0309133315615805 · DOI
  54. Han, 2015, Localization of acoustic emission sources in structural health monitoring of masonry bridge, Struct. Control. Health Monit., 22, 314, 10.1002/stc.1675 · DOI
  55. Grillanda, 2021, SHM of a severely cracked masonry arch bridge in India: experimental campaign and adaptive NURBS limit analysis numerical investigation, Constr. Build. Mater., 280, 10.1016/j.conbuildmat.2021.122490 · DOI
  56. Cheng, 2018, Registration of laser scanning point clouds: a review, Sensors, 18, 1641, 10.3390/s18051641 · DOI
  57. Kaiser, 2022, Automatic co-registration of photogrammetric point clouds with digital building models, Autom. Constr., 134, 10.1016/j.autcon.2021.104098 · DOI

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Metadata

Authors
Kong, X., & Hucks, R. G.
Affiliations
Coastal Carolina University (US)
Corresponding author
Kong, Xiangxiong
ORCID
Kong, Xiangxiong
Venue
Automation in Construction
Publisher
Elsevier BV
Published
2023-08
Volume
152
Pages
104928
ISSN
0926-5805 (ISSN-L)
DOI
10.1016/j.autcon.2023.104928
OpenAlex
W4380872890
Cited by
125
References
3
Citations by year
2026: 48 · 2025: 38 · 2024: 32 · 2023: 7
FWCI
28.49
Type
journal article
Language
en
Open access
Licence
Locations
Automation in Construction (acceptedVersion)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Keywords
digital twin, historic buildings, structural deterioration, bridge health monitoring, photogrammetry, point cloud processing, stone arch bridges, cultural heritage preservation, structural damage detection, structural settlement

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CoChat. (2026). Citation check: Preserving our heritage: A photogrammetry-based digital twin framework for monitoring deteriorations of historic structures. https://app.cochat.ai/papers/10.1016/j.autcon.2023.104928
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Kong, X., & Hucks, R. G. (2023). Preserving our heritage: A photogrammetry-based digital twin framework for monitoring deteriorations of historic structures. Automation in Construction. https://doi.org/10.1016/j.autcon.2023.104928