DFOS Technology in Geoengineering Monitoring in the Past 35 Years: A Bibliometric Analysis
Abstract
:1. Introduction
2. A Brief History of DFOS
3. Database Selection and Data Mining Process
3.1. Step One: Determine the Research Topics
3.2. Step Two: Retrieve Information on Other Topics in Geoengineering Monitoring
3.3. Step Three: Combine Retrieval Forms
4. Results
4.1. Overview
4.2. Research Areas and Technology Applications
4.3. Technology Categories
4.4. Countries and International Cooperation
4.5. Research Institutions and Cooperations between Them
4.6. The Impacts of Authors
4.7. The Impacts of Publication Sources
4.8. Keywords and Research Trends
5. Discussion
6. Conclusions
- (1)
- Over the past 35 years, DFOS technology has played an important role in modern infrastructure monitoring and disaster prevention. Engineering has become the subject category with the largest number of articles published in this field. Due to its high-accuracy sensors, cost-effective instruments, and capability of taking multi-parameter measurements, FBG has an advantage out of the different technology categories.
- (2)
- The United States, China, and the United Kingdom emerged as major contributors, with China surpassing the United States in total publications in 2016. However, challenges persist for Chinese publications in achieving a comparable level of citations or impact.
- (3)
- Institutions such as the Nanjing University and Dalian University of Technology led in terms of publication count, but the United States Department of Energy and the University of California system showcased a superior impact with fewer publications. The journal analysis highlighted the productivity of conference journals, emphasizing the need for a balanced approach considering both impact and quantity.
- (4)
- The development trend of distributed monitoring in geotechnical engineering shows a dynamic trajectory. Initially focused on using fiber optics for structural health monitoring, recent research indicates a more diverse environment with a shift towards interdisciplinary collaborations. Scholars in the field are increasingly integrating emerging technologies, like machine learning and distributed acoustic sensing. This suggests a future characterized by advanced monitoring technologies, a reliance on data-driven approaches, diverse application scenarios, and the development of sensors that combine durability and stability.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sensing Type | Sensing Technology | Sensing Parameters | Sensing Accuracy | Maximum Sensing Distance (km) | Spatial Resolution (m) | Sampling Resolution (m) | Advantages | Limitations |
---|---|---|---|---|---|---|---|---|
Quasi-distributed | FBG [42] | Strain | ±1 με | - | - | - | Cost-effective, high reliability, corrosion resistance, high sensitivity, easy to implement multiplexing | Possibility of missed detection, grating extinction phenomenon at high temperature |
temperature | ±0.1 °C | |||||||
UWFBG [13] | Strain | ±1 με | - | - | - | High resolution, high sensitivity, multipoint-capable, broadband | High equipment cost, complex fabrication, limited dynamic range | |
temperature | ±0.1 °C | |||||||
Fully distributed | BOTDR [42] | Strain | ±10 με | 80 | 1.0 | 0.05 | Long-range sensing, distributed sensing capability, non-destructive testing | Costly equipment, environmental sensitivity, limited resolution for very long distances |
temperature | ±1.0 °C | |||||||
BOFDA [43] | Strain | ±2 με | 80 | 0.2–2.5 * | 0.05 | Long-range sensing, high accuracy, high spatial resolution | Costly equipment, limited dynamic range, complex system setup | |
temperature | ±0.1 °C | |||||||
BOTDA [44] | Strain | ±20 με | 25 | 0.05–1 * | 0.01 | High sensitivity, high spatial resolution, short testing time | High equipment cost, limited resolution for extremely long distances | |
Temperature | ±1.0 °C | |||||||
ROTDR [42] | Temperature | ±0.3 °C | 16 | 0.5–3 * | 0.05 | Simple setup, cost-effective, long-range sensing capability | Signal degradation, limited dynamic range | |
OFDR [45] | Strain | ±1 με | 0.07 | 0.00065–0.01 * | 0.0003 | High spatial resolution, high sensitivity, simultaneous multi-parameter sensing | High equipment cost, short measurement distance, long data acquisition time | |
Temperature | ±0.1 °C | |||||||
OTDR [46] | Signal loss | - | 260 | - | 0.04–40 ** | Long-range measurement, high fault detection accuracy | High equipment cost, sensitivity to spurious reflections | |
Φ-OTDR [47] | Vibration | - | 40 | 1–10 * | 0.01 | Long-range capability, high sensitivity, enhanced detection of small disturbances | High equipment cost, complex data processing, environmental sensitivity |
Typical Scenarios | Sensing Parameters | Sensing Technology | Applications Sites | References |
---|---|---|---|---|
Landslides | Strain, Temperature, Soil moisture content, Displacement | FBG, UWFBG, BOTDR, BOTDA | Three Gorges Reservoir, China; Izumo landslide, Japan; Basilicata, Italy | [10,13,48,49,50] |
Debris flows | Displacement, Stress, Vibration | FBG | Weijiagou, China; Nautou county, China | [51,52,53,54] |
Ground subsidence and land fissures | Strain, Displacement | BOTDA, BOTDR | Wuxi, China; Ebro Valley, Spain | [55,56] |
Tunnels | Displacement, Strain | BOTDA, OFDR, OTDR | Mass Rapid Transit (MRT) tunnel, Singapore; Ebersviller tunnel, French; Suzhou Metro Line 3, China; Heinenoordtunnel, Netherlands | [43,57,58,59] |
Pipelines | Strain | FBG | Colombian pipeline, United States; Three Gorges Reservoir, China | [60,61] |
Railways | Temperature, Strain, Displacement | FBG, ROTDR | Qinghai-Tibet Railway, China; Stagecoach Supertram tramway, United Kingdom; Santo Stefano Magra railway, Italy | [62,63,64,65] |
Technology | A | TC | H-Index | TC/A | 1st Year |
---|---|---|---|---|---|
FBG | 859 | 5712 | 39 | 12.31 | 1997 [82] |
OTDR | 111 | 926 | 16 | 11.02 | 1994 [83] |
BOTDR | 109 | 1963 | 21 | 19.44 | 2002 [84] |
BOTDA | 105 | 1280 | 20 | 15.06 | 2008 [85] |
Institution | Country | TA | TC | TC/TA |
---|---|---|---|---|
Nanjing University | China | 169 | 2910 | 17.22 |
Dalian University of Technology | China | 107 | 2579 | 24.1 |
United States Department of Energy | United States | 104 | 3165 | 30.43 |
Chinese Academy of Sciences | China | 97 | 1103 | 11.37 |
Harbin Institute of Technology | China | 86 | 1193 | 13.87 |
Swiss Federal Institutes of Technology Domain | Switzerland | 85 | 2014 | 23.69 |
Hong Kong Polytech University | China | 68 | 1976 | 29.06 |
University of California System | United States | 68 | 1980 | 29.12 |
Centre National de la Recherche Scientifique | France | 66 | 1132 | 17.15 |
University of Cambridge | United Kingdom | 60 | 1990 | 33.17 |
Helmholtz Association | Germany | 58 | 1398 | 24.1 |
Wuhan University of Technology | China | 52 | 275 | 5.29 |
China University of Mining Technology | China | 51 | 436 | 8.55 |
Lawrence Berkeley National Laboratory | United States | 49 | 1629 | 33.24 |
Southeast University China | China | 44 | 672 | 15.27 |
Helmholtz Center Potsdam GFZ German Research Center for Geosciences | Germany | 42 | 838 | 19.95 |
Chang’an University | China | 19 | 366 | 19.26 |
Naval Research Laboratory | United States | 19 | 332 | 17.47 |
University of Birmingham | United Kingdom | 19 | 454 | 23.89 |
University of Trento | Italy | 19 | 339 | 17.84 |
Author | H-Index | TA | TC | TC/TA | Country | Affiliation |
---|---|---|---|---|---|---|
Zhu H.H. | 28 | 84 | 2011 | 23.94 | China | Nanjing University |
Shi B. | 26 | 120 | 2119 | 17.66 | China | Nanjing University |
Yin J.H. | 19 | 37 | 1140 | 30.81 | China | Hong Kong Polytechnic University |
Glisic B. | 18 | 57 | 1062 | 18 | United States | Princeton University |
Wei G.Q. | 16 | 37 | 708 | 19.14 | China | Suzhou Nanzee Sensing Technol Co., Ltd. |
Zhang C.C. | 16 | 36 | 692 | 19.22 | China | Nanjing University |
Ansari F. | 15 | 29 | 795 | 27.41 | United States | University of Illinois at Chicago |
Soga K. | 15 | 27 | 1262 | 46.74 | United States | University of California, Berkeley |
Inaudi D. | 14 | 60 | 915 | 15.25 | Switzerland | Smartec SA |
Zhang L. | 14 | 28 | 585 | 20.89 | China | China University of Geosciences |
Pei H.F. | 14 | 21 | 681 | 32.43 | China | Dalian University of Technology |
Xu D.S. | 13 | 16 | 489 | 30.56 | China | Wuhan University of Technology |
Zhang D. | 12 | 30 | 479 | 15.97 | China | Nanjing University |
Benmokrane B. | 12 | 17 | 743 | 43.71 | Canada | University of Sherbrooke |
Hong C.Y. | 11 | 21 | 717 | 34.14 | China | Shenzhen University |
Chai J. | 10 | 26 | 280 | 10.77 | China | Xi’an University of Science and Technology |
Grattan K.T.V. | 10 | 20 | 352 | 17.6 | United Kingdom | University of London |
Schenato L. | 10 | 19 | 319 | 16.79 | Italy | National Research Council–Research Institute for Geo–Hydrological Protection |
Reinsch T. | 10 | 17 | 338 | 19.88 | Germany | German Research Centre for Geosciences |
Minardo A. | 10 | 15 | 284 | 18.93 | Italy | Università della Campania Luigi Vanvitelli |
Publications | Type | TA | TC/TA | H-Index | IF |
---|---|---|---|---|---|
Proceedings of SPIE | C | 585 | 3.48 | 18 | -- |
Measurement | J | 94 | 21.27 | 27 | 5.2 |
Measurement Science and Technology | J | 43 | 26.21 | 19 | 2.7 |
Optics Express | J | 43 | 30.16 | 16 | 3.2 |
Geophysics | J | 42 | 10.6 | 13 | 3.0 |
Sensors and Actuators A-Physical | J | 40 | 32.6 | 18 | 4.1 |
IEEE Sensors Journal | J | 39 | 13.62 | 14 | 4.3 |
Applied Optics | J | 38 | 17.37 | 13 | 1.7 |
Optical Fiber Technology | J | 37 | 11.59 | 12 | 2.6 |
Smart Materials and Structures | J | 33 | 29.12 | 19 | 3.7 |
Optical Engineering | J | 31 | 6.32 | 8 | 1.1 |
Engineering Geology | J | 30 | 23.33 | 15 | 7.6 |
Structural Health Monitoring-An International Journal | J | 30 | 26.6 | 17 | 5.7 |
Journal of Sensors | J | 29 | 11.31 | 10 | 1.4 |
Tunnelling and Underground Space Technology | J | 29 | 29.66 | 18 | 6.7 |
Engineering Structures | J | 27 | 77.81 | 17 | 5.6 |
Scientific Reports | J | 26 | 32.5 | 13 | 3.8 |
Structural Control & Health Monitoring | J | 26 | 27.35 | 14 | 3.8 |
Geophysical Research Letters | J | 25 | 44.6 | 16 | 4.6 |
Journal of Geophysical Research Solid Earth | J | 22 | 21.45 | 10 | 3.9 |
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Wang, J.; Garg, A.; Satyam, N.; Zhussupbekov, A.; Sushkova, S. DFOS Technology in Geoengineering Monitoring in the Past 35 Years: A Bibliometric Analysis. Sensors 2024, 24, 5051. https://doi.org/10.3390/s24155051
Wang J, Garg A, Satyam N, Zhussupbekov A, Sushkova S. DFOS Technology in Geoengineering Monitoring in the Past 35 Years: A Bibliometric Analysis. Sensors. 2024; 24(15):5051. https://doi.org/10.3390/s24155051
Chicago/Turabian StyleWang, Jia, Ankit Garg, Neelima Satyam, Askar Zhussupbekov, and Svetlana Sushkova. 2024. "DFOS Technology in Geoengineering Monitoring in the Past 35 Years: A Bibliometric Analysis" Sensors 24, no. 15: 5051. https://doi.org/10.3390/s24155051
APA StyleWang, J., Garg, A., Satyam, N., Zhussupbekov, A., & Sushkova, S. (2024). DFOS Technology in Geoengineering Monitoring in the Past 35 Years: A Bibliometric Analysis. Sensors, 24(15), 5051. https://doi.org/10.3390/s24155051