Prediction of Water Content in Subgrade Soil in Road Construction Using Hyperspectral Information Obtained through UAV
Source abstract
Abstract
In road construction, the compaction of the subgrade layer, which is one of the earthwork fields, is an essential procedure to support the pavement layer and traffic load. For the quality control of subgrades, water content must be measured. Currently, the measurement of water content is performed at specific locations in a large area of subgrades and has the disadvantage of taking a long time to derive. Because this is difficult to immediately confirm, inefficiencies arise in terms of construction schedule and quality control. Therefore, in this study, a CCM (Color-Coded Map) was proposed through hyperspectral remote sensing using drones. This method is a range-type water-content measurement method that can acquire data in a short time (about 20 min) and can be easily confirmed visually. For this, a predicted equation that can convert hyperspectral information into water content information is developed. Multivariate linear regression, a machine learning technique, was applied to the database (of actual measured water content and hyperspectral information). The predicted and measured water contents showed a coefficient of determination of 0.888, and it was confirmed that CCMs can also be presented in various ways depending on user settings.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Remote Sensing and LiDAR Applications · Remote-Sensing Image Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Corporate Affiliated Research Institute, UCI Tech, 313, Inha-ro, Michuhol-gu, Incheon 22012, Republic of Korea
Incheon Disaster Prevention Research Center, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
Department of Civil Engineering, Halla University, 28 Halladae-gil, Wonju-si 26404, Republic of Korea
Funding
Halla University
References (40)
Show source-provided references
1. Compaction quality evaluation of subgrade based on soil characteristics assessment using machine learning10.1016/j.trgeo.2021.100703 ↗
2. Subgrade failure division and influence factors analyze of expressway
3. An improved thermo-TDR technique for monitoring soil thermal properties, water content, bulk density, and porosity10.2136/vzj2019.03.0026 ↗
4. Integrated GPR and laboratory water content measures of sandy soils: From laboratory to field scale10.1016/j.conbuildmat.2017.11.082 ↗
5. Research and application of UAV-based hyperspectral remote sensing for smart city construction10.1016/j.cogr.2022.12.002 ↗
6. Use of aerial color photography for pavement evaluation studies
7. Clark, R.N. (1999). Spectroscopy of Rocks and Minerals and Principles of Spectroscopy, John Wiley and Sons.
8. Brecher, A., Noronha, V., and Herold, M. (2003, January 2). UAV2003: A roadmap for deploying unmanned aerial vehicle (UAVs) in transportation. Proceedings of the Specialist Workshop, Santa Barbara, CA, USA.
9. Herold, M., Robert, D., Smadi, O., and Noronha, V. (April, January 31). Road condition mapping with hyperspectral remote sensing. Proceedings of the 2004 AVIRIS Workshop, Pasadena, CA, USA.
10. Özdemir, O.B., Soydan, H., Yardımcı Çetin, Y., and Düzgün, H.Ş. (2020). Neural network based pavement condition assessment with hyperspectral images. Remote Sens., 12.10.3390/rs12233931 ↗
11. Ground object information extraction from hyperspectral remote sensing images using deep learning algorithm10.1016/j.micpro.2021.104394 ↗
12. Deterioration status of asbestos-cement roofing sheets assessed by analyzing hyperspectral data, Remote Sens
13. Direct estimation of aboveground forest prodectivity through hyperspectral remote sensing of canopy nitrogen10.1890/1051-0761(2002)012[1286:DEOAFP]2.0.CO;2 ↗
14. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies10.1016/j.rse.2008.10.018 ↗
15. Kovar, M., Brestic, M., Sytar, O., Barek, V., Hauptvogel, P., and Zivcak, M. (2019). Evaluation of hyperspectral reflectance parameters to assess the leaf water content in soybean. Water, 11.10.3390/w11030443 ↗
16. Zhang, F., and Zhou, G. (2019). Estimation of vegetation water content using hyperspectral vegetation indices: A comparison of crop water indicators in response to water stress treatments for summer maize. BMC Ecol., 19.10.1186/s12898-019-0233-0 ↗
17. Predicting water content using Gaussian model on soil spectra10.1016/j.rse.2003.11.009 ↗
18. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring10.7717/peerj.6926 ↗
19. Yuan, J., Wang, X., Yan, C.X., Wang, S.R., Ju, X.P., and Li, Y. (2019). Soil moisture retrieval model for remote sensing using reflected hyperspectral information. Remote Sens., 11.10.3390/rs11030366 ↗
20. Wu, T., Yu, J., Lu, J., Zou, X., and Zhang, W. (2020). Research on inversion model of cultivated soil moisture content based on hyperspectral imaging analysis. Agriculture, 10.10.3390/agriculture10070292 ↗
21. Lee, K., Park, J.J., and Hong, G. (2022). Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test. Sustainability, 14.10.3390/su141710999 ↗
22. Lee, K., Kim, K.S., Park, J., and Hong, G. (2022). Spectrum Index for Estimating Ground Water Content Using Hyperspectral Information. Sustainability, 14.10.3390/su142114318 ↗
23. (2023, December 01). Gray Scale Calibration. Available online: https://www.group8tech.com/gray-scale-calibration.
24. Spectral transmittance of a translucent sand sample with directional illumination10.1109/TGRS.2018.2810815 ↗
25. Lu, G., and Fei, B. (2014). Medical hyperspectral imaging: A review. J. Biomed. Opt., 19.10.1117/1.JBO.19.1.010901 ↗
26. Boresight calibration of GNSS/INS-assisted push-broom hyperspectral scanners on UAV platforms10.1109/JSTARS.2018.2813263 ↗
27. A novel hyperspectral anomaly detection algorithm for real-time applications with push-broom sensors10.1109/JSTARS.2019.2919911 ↗
28. Angel, Y., Turner, D., Parkes, S., Malbeteau, Y., Lucieer, A., and McCabe, M.F. (2020). Automated georectification and mosaicking of UAV-based hyperspectral imagery from push-broom sensors. Remote Sens., 12.10.3390/rs12010034 ↗
29. An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors10.1109/JSTARS.2021.3088945 ↗
30. Yi, L., Chen, J.M., Zhang, G., Xu, X., Ming, X., and Guo, W. (2021). Seamless Mosaicking of UAV-Based Push-Broom Hyperspectral Images for Environment Monitoring. Remote Sens., 13.10.3390/rs13224720 ↗
31. Hyperspectral push-broom microscope development and characterization10.1109/ACCESS.2019.2937729 ↗
32. Automated hyperspectral cueing for civilian search and rescue10.1109/JPROC.2009.2013561 ↗
33. (2015). Standard Test Method for Density and Unit Weight of Soil in Place by Sand-Cone Method. Standard No. ASTM D1556.
34. (2019). Standard Test Methods for Laboratory Determination of Water (Moisture) Content of Soil and Rock by Mass. Standard No. ASTM D2216.
35. (2017). Standard Test Method for Determination of Water Content of Soil and Rock by Microwave Oven Heating. Standard No. ASTM D4643.
36. (2018). Standard Test Method for Field Determination of Water (Moisture) Content of Soil by the Calcium Carbide Gas Pressure Tester. Standard No. ASTM D4944.
37. Comparison of VPN Protocols at Network Layer Focusing on Wire Guard Protocol
38. A review on linear regression comprehensive in machine learning10.38094/jastt1457 ↗
39. Sarkar, M.R., Rabbani, M.G., Khan, A.R., and Hossain, M.M. (2015, January 21–23). Electricity demand forecasting of Rajshahi City in Bangladesh using fuzzy linear regression model. Proceedings of the 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, Bangladesh.10.1109/ICEEICT.2015.7307424 ↗
40. Zhang, Z., Li, Y., Li, L., Li, Z., and Liu, S. (2019, January 12–17). Multiple linear regression for high efficiency video intra coding. Proceedings of the ICASSP 2019—2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8682358 ↗