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Geomatics and Soft Computing Techniques for Road Infrastructure Monitoring

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Geomatics and Geospatial Technologies (ASITA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1507))

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Abstract

In the context of the monitoring and control of the Italian transport infrastructure heritage, both with regards to existing network and infrastructure works, an experiment has been developed: it combines geomatic and soft computing techniques in order to produce a system which aimed at both solving Early Warning problems and it is able to generate a forecasting system on the infrastructure’s behavior over time, mainly exploiting geomatics parameters.

The proposed integrated/early warning predictive system is based on the initial realization and integration of multiple models (geometric/structural) which represents the object of study (infrastructure) and the necessity for training and subsequently in the implementation of a neural network, that requires in input only the data, which can be acquired from the sensors, positioned on the infrastructure to produce different risk levels. Particular attention has been paid to the displacement measurement phase by GPS (Global Position System) signal.

The proposed integrated predictive system’s experiment was carried out in the viaduct “Annunziata” in Reggio Calabria (Southern Italy), already used as a case study in the context of other research activities conducted by the Geomatics laboratory of DICEAM (Civil, Energetic, Environmental and Material Engineering Department - University Mediterranea of Reggio Calabria).

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References

  1. Huang, Y., Ludwig, S.A., Deng, F.: Sensor optimization using a genetic algorithm for structural health monitoring in harsh environments. J. Civil Struct. Health Monit. 6(3), 509–519 (2016). https://doi.org/10.1007/s13349-016-0170-y

    Article  Google Scholar 

  2. Li, J., Zhang, X., Xing, J., Wang, P., Yang, Q., He, C.: Optimal sensor placement for long- span cable-stayed bridge using a novel particle swarm optimization algorithm. J. Civ. Struct. Health Monit. 5(5), 677–685 (2015)

    Article  Google Scholar 

  3. Yi, T.-H., Li, H.-N., Wang, C.-W.: Multiaxial sensor placement optimization in structural health monitoring using distributed wolf algorithm. Struct. Control Health Monit. 23(4), 719–734 (2016)

    Article  Google Scholar 

  4. Jin, C., Jang, S., Sun, X., Li, J., Christenson, R.: Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network. J. Civil Struct. Health Monit. 6(3), 545–560 (2016). https://doi.org/10.1007/s13349-016-0173-8

    Article  Google Scholar 

  5. Farrar, C.R., Worden, K.: Structural Health Monitoring. A Machine Learning Perspective. Wiley, Hoboken (2013)

    Google Scholar 

  6. Rao, A.R.M., Lakshmi, K.: Damage diagnostic technique combining POD with time- frequency analysis and dynamic quantum PSO. Meccanica 50(6), 1551–1578 (2015)

    Article  MathSciNet  Google Scholar 

  7. Diez, A., Khoa, N.L.D., Makki Alamdari, M., Wang, Y., Chen, F., Runcie, P.: A clustering approach for structural health monitoring on bridges. J. Civil Struct. Health Monit. 6(3), 429–445 (2016). https://doi.org/10.1007/s13349-016-0160-0

    Article  Google Scholar 

  8. Zhou, Q., Zhou, H., Zhou, Q., Yang, F., Luo, L., Li, T.: Structural damage detection based on posteriori probability support vector machine and Dempster-Shafer evidence theory. Appl. Soft Comput. 36, 368–374 (2015)

    Article  Google Scholar 

  9. Gonzalez, I., Karoumi, R.: BWIM aided damage detection in bridges using machine learning. J. Civil Struct. Health Monit. 5(5), 715–725 (2015). https://doi.org/10.1007/s13349-015-0137-4

    Article  Google Scholar 

  10. Das, S., Saha, P., Patro, S.K.: Vibration-based damage detection techniques used for health monitoring of structures: a review. J. Civil Struct. Health Monit. 6(3), 477–507 (2016). https://doi.org/10.1007/s13349-016-0168-5

    Article  Google Scholar 

  11. Neves, A., Simões, F., Pinto da Costa, A.: Vibrations of cracked beams: discrete mass and stiffness models. Comput. Struct. 168, 68–77 (2016)

    Article  Google Scholar 

  12. Abaqus FEA (2017) ABAQUS Inc. http://www.3ds.com/products-services/sim-ulia/products/abaqus/. Accessed May 2017

  13. Chen, S., Truong-Hong, L., Laefer, D., Mangina, E.: Automated Bridge Deck Evaluation through UAV Derived Point Cloud, September 2018

    Google Scholar 

  14. Cryderman, C., Mah, S.B., Shufletoski, A.: Evaluation of UAV photogrammetric accuracy for mapping and earthworks computations. Geomatica 68(4), 309–317 (2014)

    Article  Google Scholar 

  15. Eschmann, C., Wundsam, T.: Web-based georeferenced 3D inspection and monitoring of bridges with unmanned aircraft systems. J. Surv. Eng. 143(3), 04017003 (2017)

    Article  Google Scholar 

  16. Escobar-Wolf, R., Oommen, T., Brooks, C.N., Dobson, R.J., Ahlborn, T.M.: Unmanned Aerial Vehicle (UAV)-based assessment of concrete bridge deck delamination using thermal and visible camera sensors: a preliminary analysis. Res. Nondestruct. Eval. 29(4), 183–198 (2018)

    Article  Google Scholar 

  17. Gerke, M., Przybilla, H.-J.: Accuracy analysis of photogrammetric UAV image blocks: in- fluence of onboard RTK-GNSS and cross flight patterns. Photogramm. Fernerkund. Geoinformation 2016(1), 17–30 (2016)

    Article  Google Scholar 

  18. Gienko, G.A., Terry, J.P.: Three-dimensional modeling of coastal boulders using multi-view image measurements. Earth Surf. Process. Landf. 39, 853–864 (2014)

    Article  Google Scholar 

  19. Hackl, J., Adey, B.T., Woźniak, M., Schümperlin, O.: Use of Unmanned Aerial Vehicle photogrammetry to obtain topographical information to improve bridge risk assessment. J. Infrastruct. Syst. 24(1), 04017041 (2018)

    Article  Google Scholar 

  20. Ham, Y., Han, K.K., Lin, J.J., Golparvar-Fard, M.: Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works. Vis. Eng. 4(1), 1–8 (2016). https://doi.org/10.1186/s40327-015-0029-z

    Article  Google Scholar 

  21. Holst, J.M.F.G., et al.: Eurocode 8 Part 3: assessment and retrofitting of buildings. J. Constr. Steel Res. 54(2), 18–20 (2011)

    Google Scholar 

  22. Hug, C., Krzystek, P., Fuchs, W.: Advanced Lidar data processing with Lastools. In: Inter national Society for Photogrammtry and Remote Sensing (ISPRS), pp. 12–23, July 2012

    Google Scholar 

  23. Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. 32(3), 1–13 (2013)

    Article  Google Scholar 

  24. Khaloo, A., Lattanzi, D., Cunningham, K., Dell’Andrea, R., Riley, M.: Unmanned Aerial Vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling. Struct. Infrastruct. Eng. 14(1), 124–136 (2018)

    Article  Google Scholar 

  25. Lovelace, B.: Unmanned Aerial Vehicle Bridge Inspection Demonstration Project, p. 214, July 2015

    Google Scholar 

  26. Mader, D., Blaskow, R., Westfeld, P., Maas, H.G.: UAV-based acquisition of 3D point cloud a comparison of a low-cost laser scanner and SFM-tools. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci.-ISPRS Arch. 40(3W3), 335–341 (2015)

    Google Scholar 

  27. Marcus, W., Fonstad, M.: Earth Surf. Process. Landf. 33, 1491–1501 (2008)

    Google Scholar 

  28. Fotia A – Tecniche di Geomatica e soft computing per il monitoraggio del territorio e del costruito. Tesi di dottorato (2021)

    Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Civil, Energy, Environment and Materials Engineering (DICEAM), University Mediterranea of Reggio Calabria, Via Graziella, Feo di Vito, 89128, Reggio Calabria, Italy

    Antonino Fotia & Vincenzo Barrile

Authors
  1. Antonino Fotia
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  2. Vincenzo Barrile
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Corresponding authors

Correspondence to Antonino Fotia or Vincenzo Barrile .

Editor information

Editors and Affiliations

  1. DISAFA - University of Torino, Grugliasco, Italy

    Enrico Borgogno-Mondino

  2. University of Pisa, Pisa, Italy

    Paola Zamperlin

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Fotia, A., Barrile, V. (2022). Geomatics and Soft Computing Techniques for Road Infrastructure Monitoring. In: Borgogno-Mondino, E., Zamperlin, P. (eds) Geomatics and Geospatial Technologies. ASITA 2021. Communications in Computer and Information Science, vol 1507. Springer, Cham. https://doi.org/10.1007/978-3-030-94426-1_23

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  • DOI: https://doi.org/10.1007/978-3-030-94426-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94425-4

  • Online ISBN: 978-3-030-94426-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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