{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:44:26Z","timestamp":1772207066263,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:00:00Z","timestamp":1634860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 108-2638-E-008-001-MY2.C"],"award-info":[{"award-number":["MOST 108-2638-E-008-001-MY2.C"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The industry-academia cooperation between Changsheng Construction Co., Ltd. and Earth-quake-Disaster, Risk Evaluation and Management Centre (E-DREaM)","award":["10810067"],"award-info":[{"award-number":["10810067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We aim to develop a comprehensive tunnel lining detection method and clustering technique for semi-automatic rebar identification in order to investigate the ten tunnels along the South-link Line Railway of Taiwan (SLRT). We used the Ground Penetrating Radar (GPR) instrument with a 1000 MHz antenna frequency, which was placed on a versatile antenna holder that is flexible to the tunnel\u2019s condition. We called it a Vehicle-mounted Ground Penetrating Radar (VMGPR) system. We detected the tunnel lining boundary according to the Fresnel Reflection Coefficient (FRC) in both A-scan and B-scan data, then estimated the thinning lining of the tunnels. By applying the Hilbert Transform (HT), we extracted the envelope to see the overview of the energy distribution in our data. Once we obtained the filtered radargram, we used it to estimate the Two-dimensional Forward Modeling (TDFM) simulation parameters. Specifically, we produced the TDFM model with different random noise (0\u201330%) for the rebar model. The rebar model and the field data were identified with the Hierarchical Agglomerative Clustering (HAC) in machine learning and evaluated using the Silhouette Index (SI). Taken together, these results suggest three boundaries of the tunnel lining i.e., the air\u2013second lining boundary, the second\u2013first lining boundary, and the first\u2013wall rock boundary. Among the tunnels that we scanned, the Fangye 1 tunnel is the only one in category B, with the highest percentage of the thinning lining, i.e., 13.39%, whereas the other tunnels are in category A, with a percentage of the thinning lining of 0\u20131.71%. Based on the clustered radargram, the TDFM model for rebar identification is consistent with the field data, where k = 2 is the best choice to represent our data set. It is interesting to observe in the clustered radargram that the TDFM model can mimic the field data. The most striking result is that the TDFM model with 30% random noise seems to describe our data well, where the rebar response is rough due to the high noise level on the radargram.<\/jats:p>","DOI":"10.3390\/rs13214250","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T22:07:11Z","timestamp":1635113231000},"page":"4250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3114-1397","authenticated-orcid":false,"given":"Jordi Mahardika","family":"Puntu","sequence":"first","affiliation":[{"name":"Department of Earth Sciences, National Central University, Taoyuan 320, Taiwan"}]},{"given":"Ping-Yu","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, National Central University, Taoyuan 320, Taiwan"},{"name":"Earthquake-Disaster, Risk Evaluation and Management Centre, National Central University, Taoyuan 320, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2362-4145","authenticated-orcid":false,"given":"Ding-Jiun","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, National Central University, Taoyuan 320, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3339-5921","authenticated-orcid":false,"given":"Haiyina Hasbia","family":"Amania","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, National Central University, Taoyuan 320, Taiwan"}]},{"given":"Yonatan Garkebo","family":"Doyoro","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, National Central University, Taoyuan 320, Taiwan"},{"name":"Earth System Science, Taiwan International Graduate Program (TIGP), Academia Sinica, Taipei 115, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"ref_1","unstructured":"Wang, Y.T., Chang, P., Lee, Y., and Lee, M. (2019, January 14\u201318). Safety inspection and reinforcement of South-link line railway tunnels. Proceedings of the 16th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering, Taipei, Taiwan."},{"key":"ref_2","unstructured":"Lee, C.H., and Wang, T.T. (2008, January 20\u201321). Rock tunnel maintenance in Taiwan. Proceedings of the 6th Asian Young Geotechnical Engineers Conference-2008, Bangalore, India."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.proeng.2016.06.107","article-title":"Using Remote Sensing Techniques to Identify the Landslide Hazard Prone Sections along the South Link Railway in Taiwan","volume":"143","author":"Hsu","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_4","unstructured":"TRA (Taiwan Railways Administration) (2021, June 29). Available online: https:\/\/www.railway.gov.tw\/en\/."},{"key":"ref_5","unstructured":"Chen, C.H. (2000). Geological Map of Taiwan, Central Geological Survey."},{"key":"ref_6","unstructured":"Chandra, S., and Agarwal, M.M. (2013). Railway Tunnelling. Railway Engineering, Oxford University Press. [2nd ed.]."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bickel, J.O., Kuesel, T.R., and King, E.H. (1996). Tunnel Engineering Handbook, Springer Science & Business Media. [2nd ed.].","DOI":"10.1007\/978-1-4613-0449-4"},{"key":"ref_8","unstructured":"Parkinson, G., and \u00c9kes, C. (2008, January 15\u201319). Ground penetrating radar evaluation of concrete tunnel linings. Proceedings of the 12th International Conference on Ground Penetrating Radar, Birmingham, UK."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jappgeo.2010.11.004","article-title":"Layer recognition and thickness evaluation of tunnel lining based on ground penetrating radar measurements","volume":"73","author":"Li","year":"2011","journal-title":"J. Appl. Geophys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.ndteint.2013.05.004","article-title":"GPR evaluation of the Damaoshan highway tunnel: A case study","volume":"59","author":"Xiang","year":"2013","journal-title":"NDT E Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.csndt.2016.10.001","article-title":"An innovative vehicle-mounted GPR technique for fast and efficient monitoring of tunnel lining structural conditions","volume":"6","author":"Zan","year":"2016","journal-title":"Case Stud. Nondestruct. Test. Eval."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1016\/j.conbuildmat.2017.09.100","article-title":"GPR applications in structural detailing of a major tunnel using different frequency antenna systems","volume":"158","author":"Alani","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"81","DOI":"10.3141\/2522-08","article-title":"Clustering-Based Threshold Model for Condition Assessment of Concrete Bridge Decks with Ground-Penetrating Radar","volume":"2522","author":"Dinh","year":"2015","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.autcon.2018.02.017","article-title":"An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks","volume":"89","author":"Dinh","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TGRS.2016.2592679","article-title":"Real-Time Hyperbola Recognition and Fitting in GPR Data","volume":"55","author":"Dou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liang, H., Xing, L., and Lin, J. (2020). Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review. Sensors, 20.","DOI":"10.3390\/s20102836"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.jappgeo.2018.02.026","article-title":"Neural network based inspection of voids and karst conduits in hydro\u2013electric power station tunnels using GPR","volume":"151","author":"Kilic","year":"2018","journal-title":"J. Appl. Geophys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107770","DOI":"10.1016\/j.measurement.2020.107770","article-title":"GPR B scan image analysis with deep learning methods","volume":"165","author":"Ozkaya","year":"2020","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jin, Y., and Duan, Y. (2020). Wavelet Scattering Network-Based Machine Learning for Ground Penetrating Radar Imaging: Application in Pipeline Identification. Remote Sens., 12.","DOI":"10.3390\/rs12213655"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cui, X., Quan, Z., Chen, X., Zhang, Z., Zhou, J., Liu, X., Chen, J., Cao, X., and Guo, L. (2021). GPR-Based Automatic Identification of Root Zones of Influence Using HDBSCAN. Remote Sens., 13.","DOI":"10.3390\/rs13061227"},{"key":"ref_21","unstructured":"Chang, P.Y., Lin, D.J., and Puntu, J.M. (2021). Antenna Holder Device for Ground Penetrating Radar, H01Q-001\/00(2006.01)."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nielsen, F. (2016). Introduction to HPC with MPI for Data Science, Springer.","DOI":"10.1007\/978-3-319-21903-5"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shyu, J.B.H. (2005). Neotectonic architecture of Taiwan and its implications for future large earthquakes. J. Geophys. Res., 110.","DOI":"10.1029\/2004JB003251"},{"key":"ref_24","unstructured":"Ho, C.S. (1988). An Introduction to the Geology of Taiwan, Explanatory Text of the Geologic Map of Taiwan."},{"key":"ref_25","unstructured":"Sandmeier, K.J. (2019). Reflexw Version 9.0: Windows XP\/7\/8\/10-Program for the Processing of Seismic, Acoustic or Electromagnetic, Reflection, Refraction and Transmission Data, Sandmeier Geophysical Research."},{"key":"ref_26","unstructured":"Jol, H.M. (2009). Ground Penetrating Radar Theory and Applications, Elsevier."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.sigpro.2016.05.016","article-title":"An overview of ground-penetrating radar signal processing techniques for road inspections","volume":"132","author":"Benedetto","year":"2017","journal-title":"Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1190\/1.1444540","article-title":"Geophysical surveys of burial sites: A case study of the Oaro urupa","volume":"64","author":"Nobes","year":"1999","journal-title":"Geophysics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/S0926-9851(99)00057-9","article-title":"Maximizing the information return from ground penetrating radar","volume":"43","author":"Olhoeft","year":"2000","journal-title":"J. Appl. Geophys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/BF02910382","article-title":"Removal of ringing noise in GPR data by signal processing","volume":"11","author":"Kim","year":"2007","journal-title":"Geosci. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"768","DOI":"10.18178\/ijmlc.2019.9.6.871","article-title":"The Development of Ground Penetrating Radar (GPR) Data Processing","volume":"9","author":"Maruddani","year":"2019","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_32","unstructured":"Utsi, E.C. (2017). Ground Penetrating Radar: Theory and Practice, Butterworth-Heinemann."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Daniels, D.J. (2004). Ground Penetrating Radar, The Institution of Electrical Engineer. [2nd ed.].","DOI":"10.1049\/PBRA015E"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"012088","DOI":"10.1088\/1757-899X\/271\/1\/012088","article-title":"Integrity inspection of main access tunnel using ground penetrating radar","volume":"271","author":"Ismail","year":"2017","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.earscirev.2004.01.004","article-title":"Ground-penetrating radar and its use in sedimentology: Principles, problems and progress","volume":"66","author":"Neal","year":"2004","journal-title":"Earth-Sci. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3319\/TAO.1993.4.2.171(T)","article-title":"Application of Ground Penetrating Radar to Locate Underground Pipes","volume":"4","author":"Tong","year":"1993","journal-title":"Terr. Atmos. Ocean. Sci."},{"key":"ref_37","unstructured":"De Souza, T. (2013). Concrete Scanning with GPR Guidebook, Sensors & Software Inc."},{"key":"ref_38","first-page":"1321","article-title":"Uses of dielectric constant reflection coefficients for determination of groundwater using ground-penetrating radar","volume":"6","author":"Vasudeo","year":"2009","journal-title":"World Appl. Sci. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, F., Chen, Z., Liu, H., Cui, J., Spencer, B.F., and Fang, G. (2018). Simultaneous Estimation of Rebar Diameter and Cover Thickness by a GPR-EMI Dual Sensor. Sensors, 18.","DOI":"10.3390\/s18092969"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Everitt, B.S., Landau, S., Leese, M., and Stahl, D. (2011). Cluster Analysis, John Wiley & Sons, Inc.. [5th ed.].","DOI":"10.1002\/9780470977811"},{"key":"ref_41","first-page":"1615","article-title":"An Efficient and Effective Generic Agglomerative Hierarchical Clustering Approach","volume":"19","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Batrakov, D., Golovin, D., Simachev, A., and Batrakova, A. (2010, January 6\u201310). Hilbert transform application to the impulse signal processing. Proceedings of the 2010 5th International Confernce on Ultrawideband and Ultrashort Impulse Signals, Sevastopol, Ukraine.","DOI":"10.1109\/UWBUSIS.2010.5609110"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Venkatachalam, A.S., Xia, T., Xie, Y., and Wang, G. (2014). Data analysis technique to leverage ground penetrating radar ballast inspection performance. IEEE Radar Conf.","DOI":"10.1109\/RADAR.2014.6875636"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"104203","DOI":"10.1016\/j.jappgeo.2020.104203","article-title":"Time-series clustering approaches for subsurface zonation and hydrofacies detection using a real time-lapse electrical resistivity dataset","volume":"184","author":"Delforge","year":"2020","journal-title":"J. Appl. Geophys."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jappgeo.2018.06.020","article-title":"Agglomerative hierarchical clustering of airborne electromagnetic data for multi-scale geological studies","volume":"157","author":"Dumont","year":"2018","journal-title":"J. Appl. Geophys."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.jappgeo.2017.07.006","article-title":"A clustering approach applied to time-lapse ERT interpretation\u2014Case study of Lascaux cave","volume":"144","author":"Xu","year":"2017","journal-title":"J. Appl. Geophys."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.enggeo.2012.06.002","article-title":"Monitoring landfill cover by electrical resistivity tomography on an experimental site","volume":"145\u2013146","author":"Genelle","year":"2012","journal-title":"Eng. Geol."},{"key":"ref_48","first-page":"2349","article-title":"Orange: Data Mining Toolbox in Python","volume":"14","author":"Demsa","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"113576","DOI":"10.1016\/j.icarus.2019.113576","article-title":"Ground-penetrating radar measurements of subsurface structures of lacustrine sediments in the Qaidam Basin (NW China): Possible implications for future in-situ radar experiments on Mars","volume":"338","author":"Meng","year":"2019","journal-title":"Icarus"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1007\/s11600-019-00352-9","article-title":"Finite-difference time domain (FDTD) modeling of ground penetrating radar pulse energy for locating burial sites","volume":"67","author":"Akinsunmade","year":"2019","journal-title":"Acta Geophys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1088\/1742-2132\/4\/3\/S04","article-title":"FDTD simulations for ground penetrating radar in urban applications","volume":"4","author":"Liu","year":"2007","journal-title":"J. Geophys. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4250\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:21:28Z","timestamp":1760167288000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,22]]},"references-count":51,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214250"],"URL":"https:\/\/doi.org\/10.3390\/rs13214250","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,22]]}}}