A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jordi Mahardika Puntu, Ping-Yu Chang, Ding-Jiun Lin, Haiyina Hasbia Amania, Yonatan Garkebo Doyoro
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ground penetrating radar railway tunnel rebar detection tunnel lining hierarchical agglomerative clustering silhouette index Science Q
Acceso en línea:	https://doaj.org/article/a36f3b12c6e94d35bdab4da3ae34e990
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a36f3b12c6e94d35bdab4da3ae34e990
record_format	dspace
spelling	oai:doaj.org-article:a36f3b12c6e94d35bdab4da3ae34e9902021-11-11T18:51:28ZA Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements10.3390/rs132142502072-4292https://doaj.org/article/a36f3b12c6e94d35bdab4da3ae34e9902021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4250https://doaj.org/toc/2072-4292We 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’s 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 (0–30%) 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–second lining boundary, the second–first lining boundary, and the first–wall 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 0–1.71%. Based on the clustered radargram, the TDFM model for rebar identification is consistent with the field data, where <i>k</i> = 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.Jordi Mahardika PuntuPing-Yu ChangDing-Jiun LinHaiyina Hasbia AmaniaYonatan Garkebo DoyoroMDPI AGarticleground penetrating radarrailway tunnelrebar detectiontunnel lininghierarchical agglomerative clusteringsilhouette indexScienceQENRemote Sensing, Vol 13, Iss 4250, p 4250 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ground penetrating radar railway tunnel rebar detection tunnel lining hierarchical agglomerative clustering silhouette index Science Q
spellingShingle	ground penetrating radar railway tunnel rebar detection tunnel lining hierarchical agglomerative clustering silhouette index Science Q Jordi Mahardika Puntu Ping-Yu Chang Ding-Jiun Lin Haiyina Hasbia Amania Yonatan Garkebo Doyoro A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements
description	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’s 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 (0–30%) 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–second lining boundary, the second–first lining boundary, and the first–wall 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 0–1.71%. Based on the clustered radargram, the TDFM model for rebar identification is consistent with the field data, where <i>k</i> = 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.
format	article
author	Jordi Mahardika Puntu Ping-Yu Chang Ding-Jiun Lin Haiyina Hasbia Amania Yonatan Garkebo Doyoro
author_facet	Jordi Mahardika Puntu Ping-Yu Chang Ding-Jiun Lin Haiyina Hasbia Amania Yonatan Garkebo Doyoro
author_sort	Jordi Mahardika Puntu
title	A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements
title_short	A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements
title_full	A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements
title_fullStr	A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements
title_full_unstemmed	A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements
title_sort	comprehensive evaluation for the tunnel conditions with ground penetrating radar measurements
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a36f3b12c6e94d35bdab4da3ae34e990
work_keys_str_mv	AT jordimahardikapuntu acomprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements AT pingyuchang acomprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements AT dingjiunlin acomprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements AT haiyinahasbiaamania acomprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements AT yonatangarkebodoyoro acomprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements AT jordimahardikapuntu comprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements AT pingyuchang comprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements AT dingjiunlin comprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements AT haiyinahasbiaamania comprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements AT yonatangarkebodoyoro comprehensiveevaluationforthetunnelconditionswithgroundpenetratingradarmeasurements
_version_	1718431716545134592

A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements

Ejemplares similares