Identification of the Damages and Abnormal Objects in Tibetan Stone Walls Based on GPR Data Analysis

References

• Allred, B. J., J. J. Daniels, C. C. Chen, L. Peters, and H. S. Youn. 2004. GPR detection of drainage pipes in farmlands. Proceedings of the Tenth International Conference on Ground Penetrating Radar, Jun 21–24, 2004, Delft Univ Technol, Delft, NETHERLANDS, 1 and 2:307–10.
   Google Scholar
• Bialkowski, K., and J. Karlovsek. 2014. Estimating propagating velocity through steel fibre reinforced concrete. IEEE Antennas and Propagation Society International Symposium (APSURSI) 61 (01):1566–67.
   Google Scholar
• Boniger, U., and J. Tronicke. 2012. Subsurface utility extraction and characterization: Combining GPR symmetry and polarization attributes. IEEE Transactions on Geoscience & Remote Sensing 50 (3):736–46. doi:10.1109/TGRS.2011.2163413.
   Web of Science ®Google Scholar
• Chang, C. W., C. H. Lin, and H. S. Lien. 2009. Measurement radius of reinforcing steel bar in concrete using digital image GPR. Construction and Building Materials 23 (2):1057–63. doi:10.1016/j.conbuildmat.2008.05.018.
   Web of Science ®Google Scholar
• Comite, D., A. Galli, S. E. Lauro, E. Mattei, and E. Pettinelli. 2015. Analysis of GPR early-time signal features for the evaluation of soil permittivity through numerical and experimental surveys. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 9 (01):1–10. doi:10.1109/JSTARS.2015.2466174.
   Google Scholar
• Deng, C. L., X. Liu, and L. Fu. 2016. Research on structural characteristics of Tibetan stone wall and mechanical properties of its bonding materials. Structural Engineer 32 (06):29–132. doi:10.15935/j.cnki.jggcs.2016.06.019.
   Google Scholar
• Dos Reis Jr, J. A., D. Lopes de Castro, T. E. Silva de Jesus, and F. Pinheiro. 2014. Characterization of collapsed paleocave systems using GPR attributes. Journal of Applied Geophysics 103 (21):43–56. doi:10.1016/j.jappgeo.2014.01.007.
   Google Scholar
• Gamba, P., and S. Lossani. 2000. Neural detection of pipe signatures in ground penetrating radar images. IEEE Transactions on Geoscience Remote Sensing 38 (02):790–97. doi:10.1109/36.842008.
   Google Scholar
• Hamrouche, R., G. Klysz, J. P. Balayssac, J. Rhazi, and G. Ballivy. 2012. Numerical simulations and laboratory tests to explore the potential of Ground-Penetrating Radar (GPR) in detecting unfilled joints in brick masonry structures. International Journal of Architectural Heritage 6 (6):648–64. doi:10.1080/15583058.2011.597484.
   Web of Science ®Google Scholar
• Huang, H., D. Yang, K. Chen, and B. Jia. 2020. Experiments on mechanical performance of Tibetan rubble stone walls retrofitted with BFRP grids. Journal of Southwest Jiaotong University 55 (03):643–49. doi:10.3969/j.issn.0258-2724.20180455.
   Google Scholar
• Ko, K. H., G. Jang, K. Park, and K. Kim. 2012. GPR-based landmine detection and identification using multiple features. International Journal of Antennas & Propagation 2012:1072–75. doi:10.1155/2012/826404.
   Web of Science ®Google Scholar
• Nishimoto, M., S. Ueno, and Y. Kimura. 2006. Feature extraction from GPR data for identification of landmine-like objects under rough ground surface. Journal of Electromagnetic Waves Applications 20 (12):1577–86. doi:10.1163/156939306779292318.
   Web of Science ®Google Scholar
• Opper, M., and O. Winther. 1989. Gaussian processes for classification: Mean-field algorithms. Neural Computation 12 (11):2655–84. doi:10.1162/089976600300014881.
   Google Scholar
• Pasoli, E., F. Melgani, and M. Donelli. 2009. A pattern recognition system for extracting buried object characteristics in GPR images. IEEE Transactions on Geoscience & Remote Sensing 47 (07):2206–17. doi:10.1109/TGRS.2009.2012701.
   Google Scholar
• Perez-Gracia, V., M. Solla, and S. Fontul. 2022. Analysis of the GPR signal for moisture detection: Application to heritage buildings. International Journal of Architectural Heritage 1–24. doi:10.1080/15583058.2022.2139652.
   Web of Science ®Google Scholar
• Simi, A., S. Bracciali, and G. Manacorda. 2008. Hough transform based automatic pipe detection for array GPR: Algorithm development and on-site tests. IEEE Radar Conference 2008 (04):1545–50.
   Google Scholar
• Teng, D. Y., and N. Yang. 2018. Research on the features of complete stress-strain curves of Tibetan-style stone masonry under compressive load. Engineering Mechanics 35 (11):172–80. doi:10.6052/j.issn.1000-4750.2017.08.0647.
   Google Scholar
• Yang, N., Y. H. Jiang, P. Chang, and A. J. Wu. 2022. Experimental study on the compressive behaviors of traditional Tibetan three-leaf walls. International Journal of Architectural Heritage 1–23. doi:10.1080/15583058.2022.2142697.
   Web of Science ®Google Scholar