A fiber Bragg grating (FBG) based miniature sensor for fast detection of soil moisture profiles in highway slopes and subgrades

Dingfeng Cao 1, Hongyuan Fang 2, 3, 4, Fuming Wang 1, 2, 3, 4, Honghu Zhu 5, Mengya Sun

1 School of Civil Engineering, Sun Yat-sen University, Guangzhou 510006, China; 

2 College of Water Conservancy & Environmental Engineering, Zhengzhou University, Zhengzhou, 450001, China; 

3 National local joint engineering laboratory of major infrastructure testing and rehabilitation technology, Zhengzhou, 450001, China

4 Collaborative Innovation Center of Water Conservancy and Transportation Infrastructure Safety, Henan Province, Zhengzhou, 450001, China

5 School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China;

Sensors, 2019, in press.

Abstract:  A fiber Bragg grating (FBG)-based aluminum oxide tube packed sensor (ATPS) was developed for the fast detection of the soil moisture profile in highway slopes and subgrades. The novel ATPS consists of an aluminum oxide tube with a diameter of 5 mm, an optical fiber containing a quasi-distributed FBG sensors, a “U”-shaped resistance wire, and a flange. There are four 0.9-mm diameter holes in the ATPS. Laboratory experiments were carried out to calibrate the relationship between the thermal response of ATPS and the soil moisture content. Two laboratory rainfall validation model tests were performed to validate the ATPS for capturing the soil moisture profile in highway slopes and subgrades. During the validations, the accuracy of the ATPS was quantified, and water infiltration through grassy and grassless ground surfaces were investigated. The calibrations indicate that the ATPS can detect and record real-time changes in the highway slope and subgrade moisture after rainfall, and reveal the most dangerous zones that occur at the connection between different construction materials. The average measurement accuracy of soil moisture monitoring was 0.015 m3/m3. Please note that the connection is where cracks form easily and the soil hydraulic conductivity increases significantly. The test results also indicate that grassy cover (lawn) significantly prevents water infiltration during the first few minutes of rainfall (twelve minutes in this study), after which, however, the infiltration rate drops sharply. The influence of lawn on water infiltration depends on the soil structure, hydraulic conductivity, and rainfall time. In summary, due to its small size and fast detection, the ATPS is a portable probe that can be used for moisture monitoring in highway slopes and subgrades.

Keywords: highway slope and subgrade; fiber Bragg grating (FBG); aluminum oxide tube packed sensor (ATPS); temperature sensing; soil moisture

References
1. Adeyemi, O.; Grove, I.; Peets S.; Domun, Y.; Norton.T., Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling. Sensors 2018, 18, (10) ,3408.
2. Pichorim, S. F.; Gomes, N. J.; Batchelor, J. C., Two solutions of soil moisture sensing with RFID for landslide monitoring. Sensors 2018, 18, (2), 452. 
3. Liu, Z., Influence of rainfall characteristics on the infiltration moisture field of highway subgrades. Road Mater. Pavement 2015, 16, (3), 635-652.
4. Chen, J. S.; Lin, K. Y.; Young, S. Y., Effects of crack width and permeability on moisture-induced damage of pavements. J. Mater. Civil Eng. 2004, 16, (3), 276-282.
5. Sharma, L. K.; Umrao, R. K.; Singh, R.; Ahmad, M.; Singh, T.N., Stability investigation of hill cut soil slopes along National highway 222 at Malshej Ghat, Maharashtra. J. Geol. Soc. India 2017, 89, (2), 165-174.
6. Wu, J. H.; Shi, B.; Cao, D. F.; Jiang, H. T.; Wang, X. F.; Gu, K., Model test of soil deformation response to draining-recharging conditions based on DFOS. Eng. Geol. 2017, 226, 107-121. 
7. Su, S. L.; Singh, D. N.; Baghini, M. S., A critical review of soil moisture measurement. Measurement 2014, 54, 92-105.
8. Dobriyal, P.; Qureshi, A.; Badola, R.; Hussain, S. A., A review of the methods available for estimating soil moisture and its implications for water resource management. J. Hydrol. 2012, 458, 110-117. 
9. Cao, D. F.; Shi, B.; Wei, G. Q.; Chen, S. E.; Zhu, H. H., An improved distributed sensing method for monitoring soil moisture profile using heated carbon fibers. Measurement 2018, 123, 175-184.
10. Striegl, A. M.; Loheide II, S. P., Heated distributed temperature sensing for field scale soil moisture monitoring. Groundwater 2012, 50, (3), 340-347.
11. Kong, Q.; Chen, H.; Mo, Y. L.; Song, G., Real-time monitoring of water content in sandy soil using shear mode piezoceramic transducers and active sensing—A feasibility study. Sensors 2017, 17, (10), 2395.
12. Limberger, H.; Giaccari, P.; Kronenberg, P. Influence of humidity and temperature on polyimide-coated fiber Bragg gratings. In Bragg Gratings, Photosensitivity, and Poling in Glass Waveguides; BFB2; OSA Publishing: Stresa, Italy, 2001.
13. Yeo, T. L.; Sun, T.; Grattan, K. T. V., Fibre-optic sensor technologies for humidity and moisture measurement. Sens. Actuators A. 2008, 144, (2), 280-295. 
14. Yang, T.; He, X.; Ran, Z.; Xie, Z.; Rao, Y.; Qiao, X.; He, P., Highly Integrated All-Fiber FP/FBG Sensor for Accurate Measurement of Strain under High Temperature. Materials 2018, 11, (10), 1867.
15. Zhao, X.; Gou, J.; Song, G.; Ou, J., Strain monitoring in glass fiber reinforced composites embedded with carbon nanopaper sheet using Fiber Bragg Grating (FBG) sensors. Composites Part B 2009, 40, (2), 134-140.
16. Ren, L.; Jia, Z. G.; Li, H. N.; Song, G., Design and experimental study on FBG hoop-strain sensor in pipeline monitoring. Opt. Fiber Technol. 2014, 20, (1), 15-23.
17. Xu, D.; Borana, L.; Yin, J. H., Measurement of small strain behavior of a local soil by fiber Bragg grating-based local displacement transducers. Acta Geotechnica 2014, 9, (6), 935-943.
18. Xu, D., A new measurement approach for small deformations of soil specimens using fiber bragg grating sensors. Sensors 2017, 17, (5), 1016.
19. Hou, Q.; Ren, L.; Jiao, W.; Zou, P.; Song, G., An improved negative pressure wave method for natural gas pipeline leak location using FBG based strain sensor and wavelet transform. Math. Prob. Eng. 2013, 278794.
20. Hou, Q.; Jiao, W.; Ren, L.; Cao, H.; Song, G., Experimental study of leakage detection of natural gas pipeline using FBG based strain sensor and least square support vector machine. J. Loss Prev. Process Ind. 2014, 32, 144-151.
21. Ho, S. C. M.; Ren, L.; Li, H. N.; Song, G., A fiber Bragg grating sensor for detection of liquid water in concrete structures. Smart Mater. Struct. 2013, 22, (5), 055012.
22. Zhu, H. H.; Shi, B.; Zhang, C. C., FBG-based monitoring of geohazards: current status and trends. Sensors 2017, 17, (3), 452. 
23. Alwis, L.; Sun, T.; Grattan, K. T. V., Optical fibre-based sensor technology for humidity and moisture measurement: review of recent progress. Measurement 2013, 46, (10), 4052-4074. 
24. Kong, X., Ho, S. C. M.; Song, G.; Cai, C. S., Scour monitoring system using fiber Bragg grating sensors and water-swellable polymers. J. Bridge Eng. 2017, 22, (7), 04017029.
25. Huang, X. F.; Sheng, D. R.; Cen, K. F.; Zhou, H., Low-cost relative humidity sensor based on thermoplastic polyimide-coated fiber Bragg grating. Sensors and Actuators B: Chemical 2007, 127, (2), 518-524. 
26. Tiefenthaler, K.; Lukosz, W., Grating couplers as integrated optical humidity and gas sensors. Thin Solid Films, 1985, 126, (3-4), 205-211. 
27. Leone, M.; Principe, S.; Consales, M.; Parente, R.; Laudati, A.; Caliro, S.; Cutolo, A.; Cusano, A., Fiber Optic Thermo-Hygrometers for Soil Moisture Monitoring. Sensors, 2017, 17(6), 1451.
28. Cao, D. F.; Shi, B.; Zhu, H. H.; Inyang, H. I.; Wei, G. Q.; Duan, C. Z., A soil moisture estimation method using actively heated fiber Bragg grating sensors. Eng. Geol. 2018, 242, 142-149. 
29. Li, W.; Ho, S. C. M.; Song, G., Corrosion detection of steel reinforced concrete using combined carbon fiber and fiber Bragg grating active thermal probe. Smart Mater. Struct. 2016, 25, (4), 045017.
30. Sayde, C.; Gregory, C.; Gil‐Rodriguez, M.; Tufillaro, N.; Tyler, S.; van de Giesen, N.; Selker, J. S., Feasibility of soil moisture monitoring with heated fiber optics. Water Resour. Res. 2010, 46, (6), 2840-2849. 
31. Florides, G.; and Kalogirou, S., First in situ determination of the thermal performance of a U-pipe borehole heat exchanger, in Cyprus. Appl. Therm. Eng. 2008, 28(2–3), 157-163. 
32. Carslaw, H. S.; and Jaeger, J. C., 1959. Conduction of heat in solids. Oxford: Clarendon Press. 1959, 2nd ed.
33. Cao, D.; Shi, B.; Zhu, H.; Wei, G.; Chen, S. E.; Yan, J., A distributed measurement method for in-situ soil moisture content by using carbon-fiber heated cable. J. Rock Mech. Geotech. Eng. 2015, 7, (6), 700-707.
34. Lhendup, T.; Aye, L.; Fuller, R. J., In-situ measurement of borehole thermal properties in Melbourne. Appl. Therm. Eng. 2014, 73,(1), 287-295.
35. Dong, J.; Agliata, R.; Steele-Dunne, S.; Hoes, O.; Bogaard, T.; Greco, R.; van de Giesen, N., The impacts of heating strategy on soil moisture estimation using actively heated fiber optics. Sensors 2017, 17, (9), 2102.
36. Gamage, V. D. N.; Biswas, A.; Strachan, I. B.; Adamchuk, V. I., Soil Water Measurement Using Actively Heated Fiber Optics at Field Scale. Sensors 2018, 18, (4), 1116.
37. Sourbeer, J. J.; Loheide, S. P. Obstacles to long‐term soil moisture monitoring with heated distributed temperature sensing. Hydrol. Processes 2016, 30, (7), 1017-1035.
38. Ray, R. L.; Jacobs, J. M., Relationships among remotely sensed soil moisture, precipitation and landslide events. Nat. Hazards 2007, 43(2), 211-222.
39. Zaibon, S.; Anderson, S. H.; Thompson, A. L.; Kitchen, N. R.; Gantzer, C. J.; Haruna, S. I., Soil water infiltration affected by topsoil thickness in row crop and switchgrass production systems. Geoderma 2017, 286, 46-53.
40. Chang, S.; Wu, B.; Yan, N.; Zhu, J.; Wen, Q.; Xu, F. A Refined Crop Drought Monitoring Method Based on the Chinese GF-1 Wide Field View Data. Sensors, 2018, 18, (4), 1297.
41. Almedeij, J.; Esen, I. I., Modified Green-Ampt infiltration model for steady rainfall. J. Hydrol. Eng. 2013, 19, (9), 04014011.
42. Suribabu, C. R.; and Bhaskar, J., Evaluation of urban growth effects on surface runoff using SCS-CN method and Green-Ampt infiltration model. Earth Sci. Inform. 2015, 8, (3), 609-626.
43. Mao, L.; Li, Y.; Hao, W.; Zhou, X.; Xu, C.; Lei, T., A new method to estimate soil water infiltration based on a modified Green–Ampt model. Soil Tillage Res. 2016, 161, 31-37.
44. Yin, Z.; Lei, T.; Yan, Q.; Chen, Z.; Dong, Y., A near-infrared reflectance sensor for soil surface moisture measurement. Comput. Electron. Agric. 2003, 99, 101-107.
45. Dente, L.; Su, Z.; Wen, J., Validation of SMOS soil moisture products over the Maqu and Twente regions. Sensors 2012, 12, (8), 9965-9986.
46. Gao, Z.; Zhu, Y.; Liu, C.; Qian, H.; Cao, W.; Ni, J., Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers. Sensors 2018, 18, (5), 1648.