[논문]암석에 대한 라이다 반사강도의 영향 인자 분석

김문주; 이수득; 전석원

doi:10.7474/tus.2020.30.4.417

암석에 대한 라이다 반사강도의 영향 인자 분석
Analysis of Parameters Affecting LiDAR Intensity on Rock 원문보기

터널과 지하공간: 한국암반공학회지 = Tunnel and underground space, v.30 no.4, 2020년, pp.417 - 431

김문주 (서울대학교 에너지시스템공학부) , 이수득 (서울대학교 에너지시스템공학부) , 전석원 (서울대학교 에너지시스템공학부)

초록
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이 연구에서는 라이다(LiDAR) 반사강도를 이용하여 암반 풍화도 및 변질도를 산정하는 작업의 기초연구를 진행하였다. 실내 시험을 통하여 라이다 반사강도에 직접적으로 영향을 미치는 인자와 그 영향 정도를 정량적으로 고찰하고자 하였다. 영향 인자로는 주사거리, 입사각, 표면거칠기, 표면색상, 암석물성, 광물조성, 포화도를 선정하였다. 실험에서는 FARO 라이다 장비와 12가지 종류의 시험편을 사용하였다. 실험 결과 반사강도는 표면색상, 입사각, 주사거리, 암석물성, 포화도 혹은 표면습윤상태, 표면거칠기 순으로 영향을 크게 미치는 것으로 나타났다.

Abstract ▼ AI-Helper

In this study, a fundamental investigation was made on how to use LiDAR technology to determine the degree of weathering and alteration of rock mass. The purpose of the study was to identify the affecting parameters to LiDAR intensity and to quantitatively assess the relations among them through laboratory-scale experiment. A few potential affecting parameters were selected including scanning distance, incidence angle, surface roughness, surface color, mineral composition, and water saturation. In the experiment, FARO LiDAR unit was used for twelve different types of specimen. It was observed that the intensity was affected by, in the order of importance, surface color, incidence angle, scanning distance, property of rock, water condition, and surface roughness.

주제어

표/그림 (17)

그림 Fig. 1. Coal mine high wall mesh: (a) colorized by close range photogrammetry, and (b) colorized by relative reflectance of LiDAR (Fowler et al., 2011)
표 Table 1. Size, mechanical and physical properties of the specimens used in this study
그림 Fig. 2. Dried and saturated specimens used in the experiment (d-: dried, w-: saturated)
그림 Fig. 3. Definition of incidence angle (where dip angle + incidence angle = 90°)
그림 Fig. 4. 3D printed gypsum specimens with different JRCs of 4, 8, 12, 16, and 20 (in white), sandpaper with 220, 100, 40 grit size (in black) and Diastone specimens polished with the sandpaper (in yellow)
그림 Fig. 5. Color palette table composed of 252 RGB combinations (except for four cases of RGB combination out of 256 selected combinations)
그림 Fig. 6. Experimental set-up used in this study
표 Table 2. Specifications of FARO Focus S350
그림 Fig. 7. Relationship between scanning distance and intensity
그림 Fig. 8. Relationship between incidence angle and intensity
그림 Fig. 9. Relationship between saturation level or surface water condition and LiDAR intensity at the scanning distance of 5 m and incidence angle of 60°
그림 Fig. 10. Relationship between porosity and intensity: (a) intensity of dries and saturated specimens at 5 m of scanning distance and 90° of incidence angle, and (b) intensity difference between dried and saturated condition
그림 Fig. 11. Relationship between specific gravity and intensity
그림 Fig. 12. Relationship between color information (after RGB color value converted to grayscale level) and intensity.
표 Table 3. Weight-averaged LiDAR intensity from mineral composition of the granite used in this study
그림 Fig. 13. Relationship between grayscale level and intensity
표 Table 4. Contribution of affecting parameters to LiDAR intensity

AI 본문요약
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문제 정의

Both technologies are usually effective to characterize rock mass discontinuities, but they are limitedly used to characterize alteration and weathering of rock mass, of which the information is essential for rock mass classification. In this study, a fundamental investigation was made on how to use LiDAR technology to determine the degree of weathering and alteration of rock mass.
The objective of this study is to ascertain what factors directly affect LiDAR intensity and the degree to which they affect LiDAR intensity. Referring past studies on LiDAR intensity (Yoo et al.
In such way, the LiDAR point cloud of the corrected intensity can be ultimately used in the process of determining the rock mass classification for the target surface of rock mass. This study provides primitive results of characterizing LiDAR intensity for an engineering purpose, especially rock engineering. Accumulated knowledge and case studies will help improve the applicability of LiDAR intensity.

제안 방법

, 2017). Considering that the scanning resolution of LiDAR is high enough, it was tried to test both JRC scale and micro-scale of surface roughness. Five specimens with different JRCs, three sandpapers with grit size of 40, 100, and 220, and three Diastone specimens polished with the sandpaper were placed on the same specimen plate as shown in Figure 4, and the intensity was measured for different distances and incidence angles.
For the same sets of the specimen, experiment was repeated at 5, 10, 15, and 20 m of distance between the specimen and the LiDAR to determine the relationship between the scanning distance and the intensity. To determine the relationship between the incidence angle and the intensity, the specimen plate was tilted from 15° to 90° at 15° interval for each distance.
In addition, in this study, a laboratory color test was performed to minimize the effect of sunshine and atmospheric conditions by identifying the characteristics of the RGB values that affect the intensity. The test was performed on a printed color palette table by setting a total of 252 RGB values as shown in Figure 5.
But, additional factors such as surface hardness and roughness need to be considered for the application in rock engineering. In this study, a series of experiment were conducted to quantitatively obtain the relationship between LiDAR intensity and not only distance, incidence angle, color in RGB, water condition, but also roughness, mechanical/physical properties of rocks, i.e. uniaxial compressive strength (UCS), p- and s-wave velocity, and porosity, and mineral composition.
In this study, a series of laboratory test were performed to investigate the characteristics of LiDAR intensity. Twelve rock and rock-like specimens were selected to represent different rock types, mechanical and physical properties.
Previous studies have shown that LiDAR intensity is affected by mineral composition of rock, strength, and the degree of weathering. In this study, the relationships between LiDAR intensity and various affecting parameters were analyzed including scanning distance, incidence angle, surface roughness, water condition, mechanical and physical properties, mineral composition, and color information. For the specimens used in this study, the parameters that influenced the LiDAR intensity were, in the order of importance, color information in RGB (29.
(2013) found the reflectivity of a laser scanner could be used for measuring the degree of weathering of old architecture. The study presented a correlation between the Schmidt hammer rebound values and the reflectivity on the surface that was detected by means of terrestrial scanner laser. Accordingly, the result demonstrated that such an investigation could be a significant alternative as an innovative and non-destructive technique.
Two sets of the 12 specimens were prepared to investigate the relationship between water saturation or surface water condition and LiDAR intensity. The first group (w-group) was tested after 24 hours of water saturation, while the second group (d-group) was tested after 24 hours of drying.

대상 데이터

A total of 12 rock or rock-like test specimens were prepared with different properties and colors: five types of sandstone (Bandera, Berea, Buff Berea, Boise, and Briarhill sandstone), two granites, two shales with different anisotropic feature, limestone, cement mortar, and Diastone (a kind of hard gypsum). All of them were shaped as a Brazilian tension disc.
Twelve rock and rock-like specimens were selected to represent different rock types, mechanical and physical properties. Five different sandstones (Bandera, Berea, Boise, Briarhill, and Buff Berea), two granites, two shales, a limestone, cement mortar, and Diastone were used. For the LiDAR unit, FARO Focus S350 was used of which the full range of intensity was between 0 and 2027.
Five different sandstones (Bandera, Berea, Boise, Briarhill, and Buff Berea), two granites, two shales, a limestone, cement mortar, and Diastone were used. For the LiDAR unit, FARO Focus S350 was used of which the full range of intensity was between 0 and 2027.
In this study, a series of laboratory test were performed to investigate the characteristics of LiDAR intensity. Twelve rock and rock-like specimens were selected to represent different rock types, mechanical and physical properties. Five different sandstones (Bandera, Berea, Boise, Briarhill, and Buff Berea), two granites, two shales, a limestone, cement mortar, and Diastone were used.
(2009) suggested a quantitative relationship between water content and LiDAR intensity. Two types of rock were tested in their study: marl and limestone. In general, as the water content increased by 1%, the intensity decreased by 10% except for a few outliers.

성능/효과

In this study, the relationships between LiDAR intensity and various affecting parameters were analyzed including scanning distance, incidence angle, surface roughness, water condition, mechanical and physical properties, mineral composition, and color information. For the specimens used in this study, the parameters that influenced the LiDAR intensity were, in the order of importance, color information in RGB (29.4%), incidence angle (23.6%), scanning distance (16.5%), mechanical/physical properties (13.4%), and water condition (12.5%). Surface roughness affected the intensity but it was negligible with less than 5% of contribution.
Surface roughness affected the intensity but it was negligible with less than 5% of contribution. The LiDAR intensity of the specimens measured from 700 to 1700, which took roughly 50% of the full range of the intensity by FARO S350. It implies that the intensity value can be effectively used to identify the affecting parameters which are involved in rock mass characterization.

참고문헌 (14)

Bellian, J.A., Kerans, C. and Jennette, D.C., 2005. Digital outcrop models: applications of terrestrial scanning LIDAR technology in stratigraphic modeling. Journal of Sedimentary Research, 75(2), 166-176.

상세보기
Burton, D., Dunlap, D.B., Wood, L.J. and Flaig, P.P., 2011. LIDAR intensity as a remote sensor of rock properties. Journal of Sedimentary Research, 81(5), 339-347.

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Coren, F. and Sterzai, P., 2006. Radiometric correction in laser scanning. International Journal of Remote Sensing, 27(15), 3097-3104.

상세보기
Ercoli, L., Megna, B., Nocilla, A. and Zimbardo, M., 2013. Measure of a limestone weathering degree using laser scanner. International Journal of Architectural Heritage, 7(5), 591-607.

상세보기
Fowler, A., France, J.I. and Truong, M., 2011. Applications of advanced laser scanning technology in geology. Riegl USA. http://www. rieglusa. com/pdf/applications-ofadvanced-laser-scanning-technology-in-geology-ananda-fowler-final. pdf. (May 20, 2020)
Franceschi, M., Teza, G., Preto, N., Pesci, A., Galgaro, A. and Girardi, S., 2009. Discrimination between marls and limestones using intensity data from terrestrial laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 522-528.

상세보기
Kaasalainen, S., Jaakkola, A., Kaasalainen, M., Krooks, A. and Kukko, A., 2011. Analysis of incidence angle and distance effects on terrestrial laser scanner intensity: Search for correction methods. Remote Sensing, 3(10), 2207-2221.

상세보기
Klise, K.A., Weissmann, G.S., McKenna, S.A., Nichols, E.M., Frechette, J.D., Wawrzyniec, T.F. and Tidwell, V.C., 2009. Exploring solute transport and streamline connectivity using LIDAR-based outcrop images and geostatistical representations of heterogeneity. Water Resources Research, 45(5), W05413.
Lee, S.D. and Jeon, S.W., 2017. A Study on the Roughness Measurement for Joints in Rock Mass Using LIDAR. Tunnel and Underground Space, 27(1), 58-68. (in Korean)

원문보기 상세보기
Pesci, A., Teza, G. and Ventura, G., 2008. Remote sensing of volcanic terrains by terrestrial laser scanner: preliminary reflectance and RGB implications for studying Vesuvius crater (Italy). Annals of Geophysics, 51(4), 633-653.
Shin, J.I., 2007. Signal characteristics analysis and normalization of LIDAR intensity data. Master's Thesis, Inha University. (in Korean)
Tan, K. and Cheng, X., 2015. Intensity data correction based on incidence angle and distance for terrestrial laser scanner. Journal of Applied Remote Sensing, 9(1), 094094.

상세보기
Wicaksana, Y., 2020. Prediction of rock cutting performance and abrasiveness considering dynamic properties at intermediate strain rate. Ph.D. Dissertation, Seoul National University.
Yoo, W.K., Kim, J. and Kim, T.H., 2015. The relationship between weathering degree and reflectance of laser scanner considering RGB value, Journal of the Korea Academia Industrial Cooperation Society, 16(10), 7182-7188. (in Korean)

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