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

Abstract

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 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.

Evaluating the Time-Dependent Behavior of Deeply Buried Tunnels in Soft Rock Environments and Relevant Measures Guaranteeing Their Long-Term Stability

“…The steel arch can be also monitored by FBG sensors. Regarding the secondary lining, its long-term monitoring can be effectuated by a vehicle-mounted ground penetrating radar (VMGPR) [73], and pressure sensors [74]. Table 14 provides relevant descriptions of the proposed sensors.…”

Groundwater monitoring and specific yield estimation using time-lapse electrical resistivity imaging and machine learning

“…HAC is a technique that uses bottom-up approaches which start with single data as a single cluster and merge them until one big cluster remains. The necessary procedure in order to accomplish the goal involves data preparation, similarity measures, and linkage criteria (Dumont et al, 2018;Delforge et al, 2021;Puntu et al, 2021). HAC exhibits several advantages over other algorithms.…”

“…Since the clustering method is an unsupervised method where the ground-truth labels are usually unavailable, an essential question that one should consider is how many clusters are suitable to describe the dataset. To evaluate the optimal number of clusters (k), we performed the Silhouette Index (SI), a method that measures how similar the data is to other data in the same cluster (cohesion and compactness) in comparison with the data of other clusters (separability) (Dudek, 2020;Delforge et al, 2021;Puntu et al, 2021). Mathematically, we write it down as:…”

Special Issue “Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems”

“…The fourth paper [4] evaluated the tunnel conditions through the two-dimensional forward modeling (TDFM) simulation of GPR measurements. In this study, the tunnel lining was detected via the Fresnel reflection coefficient in both A-scan and B-scan mode to estimate the thinning lining of the tunnels.…”