Analysis of the GPR signal for moisture detection: application to heritage buildings

References

• Agliata, R., T. A. Bogaard, R. Greco, L. Mollo, E. C. Slob, and S. C. Steele-Dunne. 2018. Non-invasive estimation of moisture content in tuff bricks by GPR. Construction and Building Materials 160:698–706. doi:10.1016/j.conbuildmat.2017.11.103.
   Web of Science ®Google Scholar
• Alzeyadi, A., and T. Yu. 2021. Determination of critical contour area in SAR images of concrete for subsurface moisture sensing. In: Proc. SPIE 11591, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, California, United States, 22-27 March 2021: 1159114.
   Google Scholar
• Annan, A. P. 1999. Ground penetrating radar: Workshop notes. Ontario, Canada: Sensors and Software Inc.
   Google Scholar
• Annan, A. P. 2003. Ground penetrating radar. Principles, procedures & applications. Canada: Sensors & Software Inc. Mississauga.
   Google Scholar
• Annila, P. J., and J. Lahdensivu. 2020. Reliability of the detection of moisture and mould damage in visual inspections. E3S Web of Conferences, Yogyakarta, Indonesia, September 7-8, 2020, Vol. 172, p. 23004.
   Google Scholar
• Asadi, P., M. Gindy, and M. Álvarez. 2019. A machine learning based approach for automatic rebar detection and quantification of deterioration in concrete bridge deck ground penetrating radar B-scan images. KSCE Journal of Civil Engineering 23 (6):2618–27. doi:10.1007/s12205-019-2012-z.
   Web of Science ®Google Scholar
• Barone, P. M., and C. Ferrara. 2018. Non-Invasive moisture detection for the preservation of cultural heritage. Heritage 1 (1):163–70. doi:10.3390/heritage1010011.
   Google Scholar
• Barroca, N., L. M. Borges, F. J. Velez, F. Monteiro, M. Górski, and J. Castro-Gomes. 2013. Wireless sensor networks for temperature and moisture monitoring within concrete structures. Construction and Building Materials 40:1156–66. doi:10.1016/j.conbuildmat.2012.11.087.
   Web of Science ®Google Scholar
• Bertolino, M., and T. Tanzi. 2020. Towards 3D simulation for disaster intervention robot behaviour assessment. Advances in Radio Science 18 (C.):23–32. doi:10.5194/ars-18-23-2020.
   Google Scholar
• Binley, A., P. Winship, R. Middleton, M. Pokar, and J. West. 2001. High‐resolution characterization of vadose zone dynamics using cross‐borehole radar. Water Resources Research 37 (11):2639–52. doi:10.1029/2000WR000089.
   Web of Science ®Google Scholar
• Biscarini, C., I. Catapano, N. Cavalagli, G. Ludeno, F. A. Pepe, and F. Ubertini. 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 International 115:102287. doi:10.1016/j.ndteint.2020.102287.
   Web of Science ®Google Scholar
• Bradford, J. H., J. T. Harper, and J. Brown. 2009. Complex dielectric permittivity measurements from ground‐penetrating radar data to estimate snow liquid water content in the pendular regime. Water Resources Research 45 (8).
   Google Scholar
• Dannowski, G., and U. Yaramanci. 1999. Estimation of water content and porosity using combined radar and geoelectrical measurements. European Journal of Environmental and Engineering Geophysics 4 (1):71–85.
   Google Scholar
• Deshpande, J. 2020. Geographical information system & building information modelling. Dubai, UAE: ADCC Infocad IT Services.
   Google Scholar
• Fontul, S., P. Couto, and M. J. F. Silva. 2021. BIM applications to pavements and railways. In Integration of numerical parameters. Chapter book in: Sustainability and automation in smart constructions ed. by Hugo Rodrigues, Florindo Gaspar, Paulo Fernandes, Artur Mateus. Switzerland: Springer, 69–74.
   Google Scholar
• Fontul, S., A. Paixão, M. Solla, and L. Pajewski. 2018a. Railway track condition assessment at network level by frequency domain analysis of GPR data. Remote Sensing 10 (4):559. doi:10.3390/rs10040559.
   Web of Science ®Google Scholar
• Fontul, S., M. Solla, H. Cruz, J. Machado, and L. Pajewski. 2018b. Ground penetrating radar investigations in the Noble Hall of São Carlos Theatre in Lisbon, Portugal. Surveys in Geophysics 39 (6):1125. doi:10.1007/s10712-018-9477-z.
   Web of Science ®Google Scholar
• Francisco, C., L. Gonçalves, and F. Gaspar Et.al. 2021. Data acquisition in cultural heritage buildings using non-destructive techniques, and its gathering with BIM – The case study of the gothic Monastery of Batalha in Portugal. In Chapter book in: Sustainability and automation in smart constructions, edited by Hugo Rodrigues, Florindo Gaspar, Paulo Fernandes, Artur Mateus, 59–68. Switzerland: Springer.
   Google Scholar
• García-Fernández, M., Y. Álvarez-López, and F. Las Heras. 2019. Autonomous airborne 3D SAR imaging system for subsurface sensing: UWB-GPR on board a UAV for landmine and IED detection. Remote Sensing 11 (20):2357. doi:10.3390/rs11202357.
   Web of Science ®Google Scholar
• Garrido, I., M. Solla, S. Lagüela, and N. Fernández. 2020. IRT and GPR techniques for moisture detection and characterization in buildings. Sensors 20 (22):6421. doi:10.3390/s20226421.
   PubMed Web of Science ®Google Scholar
• Gibb, S., H. M. La, T. Le, L. Nguyen, R. Schmid, and H. Pham. 2018. Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection. Journal of Field Robotics 35 (6):988–1004.
   Web of Science ®Google Scholar
• Howlader, M. O. F., T. P. Sattar, and S. Dudley. 2016. Development of a wall climbing robotic ground penetrating radar system for inspection of vertical concrete structures. International Journal of Mechanical and Mechatronics Engineering 10 (8):1382–88.
   Google Scholar
• Hugenschmidt, J., A. Kalogeropoulos, F. Soldovieri, and G. Prisco. 2010. Processing strategies for high-resolution GPR concrete inspections. NDT & E International 43 (4):334–42. doi:10.1016/j.ndteint.2010.02.002.
   Web of Science ®Google Scholar
• Hugenschmidt, J., and R. Loser. 2008. Detection of chlorides and moisture in concrete structures with ground penetrating radar. Materials and Structures 41 (4):785–92. doi:10.1617/s11527-007-9282-5.
   Web of Science ®Google Scholar
• Jol, H. M. 2009. Ground penetrating radar. Theory and applications. Amsterdam, The Netherlands: Elsevier.
   Google Scholar
• Kaplanvural, İ., K. Özkap, and E. Pekşen. 2021. Influence of water content investigation on GPR wave attenuation for early age concrete in natural air-drying condition. Construction and Building Materials 297:123783. doi:10.1016/j.conbuildmat.2021.123783.
   Web of Science ®Google Scholar
• Klotzsche, A., L. Lärm, J. Vanderborght, G. Cai, S. Morandage, M. Zörner, H. Vereecken, and J. Van der Kruk. 2019. Monitoring soil water content using time‐lapse horizontal borehole GPR data at the field‐plot scale. Vadose Zone Journal 18 (1):190044. doi:10.2136/vzj2019.05.0044.
   Web of Science ®Google Scholar
• Kovacic, I., and M. Honic. 2019. Scanning and data capturing for BIM-supported resources assessment: A case study. Journal of Information Technology in Construction Special issue CIB World Building Congress 2019: Information technology of smart city development. 26:624–638. doi:10.36680/j.itcon.2021.032.
   Web of Science ®Google Scholar
• Lagüela, S., L. Díaz-Vilariño, D. Roca, and A. Filgueira. 2016. Thermographic 3D modeling of existing constructions , In Chapter book in: Non-destructive techniques for the evaluation of structures and infrastructures, edited by Belén Riveiro, Mercedes Solla, 233–252. The Netherlands: CRC Press, Taylor & Francis Group.
   Google Scholar
• Lai, W. W., X. Dérobert, and P. Annan. 2018. A review of ground penetrating radar application in civil engineering: A 30-year journey from locating and testing to imaging and diagnosis. NDT & E International 96:58–78. doi:10.1016/j.ndteint.2017.04.002.
   Web of Science ®Google Scholar
• Liu, B., Y. Ren, H. Liu, H. Xu, Z. Wang, A.G. Cohn and P. Jiang. 2020. GPRInvNet: Deep learning-based ground penetrating radar data inversion for tunnel lining in IEEE Transactions on Geoscience and Remote Sensing, Cornell University. 59 (10): 8305–8325. doi:10.1109/TGRS.2020.3046454.
   Google Scholar
• Loeffler, O., and M. Bano. 2004. Ground penetrating radar measurements in a controlled vadose zone: Influence of the water content. Vadose Zone Journal 3 (4):1082–92. doi:10.2136/vzj2004.1082.
   Web of Science ®Google Scholar
• Lourenço, P., E. Luso, and M. Almeida. 2006. Defects and moisture problems in buildings from historical city centres: A case study in Portugal. Building and Environment 41 (2):223–34. doi:10.1016/j.buildenv.2005.01.001.
   Web of Science ®Google Scholar
• Martínez-Garrido, M. I., R. Fort, M. Gómez-Heras, J. Valles-Iriso, and M. J. Varas-Muriel. 2018. A comprehensive study for moisture control in cultural heritage using non-destructive techniques. Journal of Applied Geophysics 155:36–52. doi:10.1016/j.jappgeo.2018.03.008.
   Web of Science ®Google Scholar
• Novo, A., H. Lorenzo, F. I. Rial, and M. Solla. 2010. Three-dimensional ground-penetrating radar strategies over an indoor archaeological site: Convent of Santo Domingo (Lugo, Spain). Archaeological Prospection 17 (4):213–22. doi:10.1002/arp.386.
   Web of Science ®Google Scholar
• Núñez-Nieto, X., M. Solla, A. Novo, and H. Lorenzo. 2014. Three-dimensional ground-penetrating radar methodologies for the characterization and volumetric reconstruction of underground tunnelling. Construction and Building Materials 71:551–60. doi:10.1016/j.conbuildmat.2014.08.083.
   Web of Science ®Google Scholar
• Pedret-Rodés, J., V. Perez-Gracia, and A. Martínez-Reguero. 2015. Evaluation of the GPR frequency spectra in asphalt pavement assessment. Construction and Building Materials 96:181–88. doi:10.1016/j.conbuildmat.2015.08.017.
   Web of Science ®Google Scholar
• Pérez-Gracia, V. 2001. Radar de subsuelo. Evaluación para aplicaciones en arqueología y en patrimonio histórico-artístico. Ph.D Thesis, Universitat Politècnica de Catalunya. Barcelona, Spain.
   Google Scholar
• Pérez-Gracia, V., F. G. García, and I. R. Abad. 2008. GPR evaluation of the damage found in the reinforced concrete base of a block of flats: A case study. NDT & E International 41 (5):341–53. doi:10.1016/j.ndteint.2008.01.001.
   Web of Science ®Google Scholar
• Pérez-Gracia, V., S. Santos-Assuncao, O. Caselles, J. Clapés, and J. A. Canas. 2014. Study of wood beams in buildings with ground penetrating radar. In: Proceedings of the 15th International Conference on Ground Penetrating Radar, 30 June - 04 july, 2014, Brussels, Belgium.
   Google Scholar
• Pochanin, G., L. Varianytsia-Roshchupkina, V. Ruban, I. Pochanina, P. Falorni, G. Borgioli, L. Capineri and T. Bechtel. 2017. Design and simulation of a “single transmitter-four receiver” impulse GPR for detection of buried landmines. In: Proceedings of the 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), 28-30 June 2017, Edinburgh, UK.
   Google Scholar
• Qiao, X., F. Yang, and X. Xu. 2014. The prediction method of soil moisture content based on multiple regression and rbf neural network. In: Proceedings of the 15th International Conference on Ground Penetrating Radar, 30 June - 04 July, 2014, Brussels, Belgium.
   Google Scholar
• Reynolds, J. M. 2011. An introduction to applied and environmental geophysics. 2nd ed. Oxford, UK: Wiley-Blackwell, John Wiley & Sons, Ltd.
   Google Scholar
• Sánchez-Aparicio, L. J., S. Del Pozo, P. Rodríguez-Gonzálvez, J. Herrero-Pascual, A. Muñoz-Nieto, D. González-Aguilera, and D. Hernández-López. 2016. Practical use of multispectral techniques for the detection of pathologies in constructions . In Chapter book in: Non-destructive techniques for the evaluation of structures and infrastructures, edited by Belén Riveiro, Mercedes Solla, 253–273. The Netherlands: CRC Press, Taylor & Francis Group.
   Google Scholar
• Šarlah, N., T. Podobnikar, D. Mongus, T. Ambrožič, and B. Mušič. 2019. Kinematic GPR-TPS model for infrastructure asset identification with High 3D georeference accuracy developed in a real urban test field. Remote Sensing 11 (12):1457. doi:10.3390/rs11121457.
   Web of Science ®Google Scholar
• Serrat, C., A. Banaszek, A. Cellmer, and V. Gibert. 2019. Use of UAVs for technical inspection of buildings within the BRAIN massive inspection platform. IOP Conference Series: Materials Science and Engineering 471:022008. doi:10.1088/1757-899X/471/2/022008.
   Google Scholar
• Sihvola, A., E. Nyfors, and M. Tiuri. 1985. Mixing formulae and experimental results for the dielectric constant of snow. Journal of Glaciology 31 (108):163–70. doi:10.1017/S0022143000006419.
   Web of Science ®Google Scholar
• Šipoš, D., and D. Gleich. 2020. A lightweight and low-power UAV-borne ground penetrating radar design for landmine detection. Sensors 20 (8):2234. doi:10.3390/s20082234.
   PubMed Web of Science ®Google Scholar
• Sofi, A., J. J. Regita, B. Rane, and H. H. Lau. 2022. Structural health monitoring using wireless smart sensor network–An overview. Mechanical Systems and Signal Processing 163:108113. doi:10.1016/j.ymssp.2021.108113.
   Web of Science ®Google Scholar
• Solla, M., and N. Fernández. 2022. GPR analysis to detect subsidence: A case study on a loaded reinforced concrete pavement. International Journal of Pavement Engineering 1–15. doi:10.1080/10298436.2022.2027420.
   Web of Science ®Google Scholar
• Solla, M., L. M. S. Gonçalves, G. Gonçalves, C. Francisco, I. Puente, P. Providência, F. Gaspar and H. Rodrigues. 2020. A building information modeling approach to integrate geomatic data for the documentation and preservation of cultural heritage. Remote Sensing. 12(24):4028. doi:10.3390/rs12244028.
   Web of Science ®Google Scholar
• Solla, M., S. Lagüela, N. Fernández, and I. Garrido. 2019. Assessing rebar corrosion through the combination of nondestructive GPR and IRT methodologies. Remote Sensing 11 (14):1705. doi:10.3390/rs11141705.
   Web of Science ®Google Scholar
• Topp, G. C., J. L. Davis, and A. P. Annan. 1980. Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resources Research 16 (3):574–82. doi:10.1029/WR016i003p00574.
   Web of Science ®Google Scholar
• Topp, G. C., G. W, T. P. F. Parkin, M. R. Carter, and E. G. Gregorich. 2008. Soil water content. Soil Sampling and Methods of Analysis 939–62.
   Google Scholar
• Travassos, X. L., S. L. Avila, and N. Ida. 2021. Artificial neural networks and machine learning techniques applied to ground penetrating radar: A review. Applied Computing and Informatics 17 (2):296–308. doi:10.1016/j.aci.2018.10.001.
   Google Scholar
• Veiga, M. R. 2012. Conservation of historic renders and plasters: from laboratory to site. In RILEM bookseries, ed. V. J, J. Hughes, and C. Groot, Historic Mortars, Vol. 7, 207–25. Dordrecht:Springer. doi:10.1007/978-94-007-4635-0_16.
   Google Scholar
• Vergnano, A., D. Franco, and A. Godio. 2022. Drone-borne ground-penetrating radar for snow cover mapping. Remote Sensing 14 (7):1763. doi:10.3390/rs14071763.
   Web of Science ®Google Scholar
• Wang, J. R., and T. J. Schmugge. 1980. An empirical model for the complex dielectric permittivity of soils as a function of water content. IEEE Transactions on Geoscience and Remote Sensing 4 (4):288–95. doi:10.1109/TGRS.1980.350304.
   Google Scholar
• Wu, K., G. A. Rodriguez, M. Zajc, E. Jacquemin, M. Clément, A. De Coster and S. Lambot 2019. A new drone-borne GPR for soil moisture mapping. Remote Sensing of Environment 235:111456. doi:10.1016/j.rse.2019.111456.
   Web of Science ®Google Scholar
• Xu, X., Y. Lei, and F. Yang. 2018. Railway subgrade defect automatic recognition method based on improved faster R-CNN. Scientific Programming 4832972.
   Google Scholar
• Yangí, L., G. Yang, Z. Liu, Y.Chang, B. Jiang, Y. Awad, and J. Xiao. 2018. Wall-climbing robot for visual and GPR inspection. In: Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), Wuhan, China.
   Google Scholar
• YongShuai, Y., Y. Yajing, and Z. Guizhang. 2019. Estimation of sand water content using GPR combined time-frequency analysis in the Ordos Basin, China. Open Physics 17 (1):999–1007. doi:10.1515/phys-2019-0106.
   Web of Science ®Google Scholar
• Zhang, Y., A. Venkatachalam, and T. Xia. 2015. Ground-penetrating radar railroad ballast inspection with an unsupervised algorithm to boost the region of interest detection efficiency. Journal of Applied Remote Sensing 9 (1):1–19. doi:10.1117/1.JRS.9.095058.
   Google Scholar