Time-Series 3D Modeling of Tunnel Damage Through Fusion of Image and Point Cloud Data
Պահպանված է:
| Հրատարակված է: | Remote Sensing vol. 17, no. 18 (2025), p. 3173-3211 |
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| Հիմնական հեղինակ: | |
| Այլ հեղինակներ: | , , |
| Հրապարակվել է: |
MDPI AG
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| Խորագրեր: | |
| Առցանց հասանելիություն: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Ցուցիչներ: |
Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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|---|---|---|---|
| 001 | 3254636638 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17183173 |2 doi | |
| 035 | |a 3254636638 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Lee, Chulhee |u Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang 10223, Republic of Korea; lch@kict.re.kr (C.L.); dgkim2004@kict.re.kr (D.K.); | |
| 245 | 1 | |a Time-Series 3D Modeling of Tunnel Damage Through Fusion of Image and Point Cloud Data | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Precise maintenance is vital for ensuring the safety of tunnel structures; however, traditional visual inspections are subjective and hazardous. Digital technologies such as LiDAR and imaging offer promising alternatives, but each has complementary limitations in geometric precision and visual representation. This study addresses these limitations by developing a three-dimensional modeling framework that integrates image and point cloud data and evaluates its effectiveness. Terrestrial LiDAR and UAV images were acquired three times over a freeze–thaw cycle at an aging, abandoned tunnel. Based on the data obtained, three types of 3D models were constructed: TLS-based, image-based, and fusion-based. A comparative evaluation results showed that the TLS-based model had excellent geometric accuracy but low resolution due to low point density. The image-based model had high density and excellent resolution but low geometric accuracy. In contrast, the fusion-based model achieved the lowest root mean squared error (RMSE), the highest geometric accuracy, and the highest resolution. Time-series analysis further demonstrated that only the fusion-based model could identify the complex damage progression mechanism in which leakage and icicle formation (visual changes) increased the damaged area by 55.8% (as measured by geometric changes). This also enabled quantitative distinction between active damage (leakage, structural damage) and stable-state damage (spalling, efflorescence, cracks). In conclusion, this study empirically demonstrates the necessity of data fusion for comprehensive tunnel condition diagnosis. It provides a benchmark for evaluating 3D modeling techniques in real-world environments and lays the foundation for digital twin development in data-driven preventive maintenance. | |
| 653 | |a Accuracy | ||
| 653 | |a Defects | ||
| 653 | |a Concrete | ||
| 653 | |a Lidar | ||
| 653 | |a Data processing | ||
| 653 | |a Damage detection | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Measurement techniques | ||
| 653 | |a Data integration | ||
| 653 | |a Registration | ||
| 653 | |a Density | ||
| 653 | |a Leakage | ||
| 653 | |a Efflorescence | ||
| 653 | |a Visualization | ||
| 653 | |a Preventive maintenance | ||
| 653 | |a Geometric accuracy | ||
| 653 | |a Inspections | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Digital transformation | ||
| 653 | |a Lasers | ||
| 653 | |a Freeze-thawing | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Sensors | ||
| 653 | |a Time series | ||
| 653 | |a Three dimensional models | ||
| 653 | |a Digital twins | ||
| 653 | |a Freeze thaw cycles | ||
| 653 | |a Image acquisition | ||
| 653 | |a Spalling | ||
| 700 | 1 | |a Kim Donggyou |u Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang 10223, Republic of Korea; lch@kict.re.kr (C.L.); dgkim2004@kict.re.kr (D.K.); | |
| 700 | 1 | |a Kim Dongku |u Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang 10223, Republic of Korea; lch@kict.re.kr (C.L.); dgkim2004@kict.re.kr (D.K.); | |
| 700 | 1 | |a Kang Joonoh |u Department of Urban Engineering, Incheon National University, Incheon 22012, Republic of Korea | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 18 (2025), p. 3173-3211 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254636638/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254636638/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254636638/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |