EGU 2015 presentación_

Semi-automatic characterization of fractured rock massesusing 3D point clouds

discontinuity orientation, spacing and SMR geomechanical classification

A.Riquelmea1 R.Tomása2 A.Abellánb3 M.Cano a4 M.Jaboyedoffb5

a Civil Engineering Department, University of Alicante, Spain

b Center for Research on Terrestrial Environment, University of Lausanne, Switzerland

{[email protected], [email protected], [email protected],[email protected], [email protected]}

April 13, 2015

A. Riquelme et al; [email protected] EGU General Assembly 2015, Vienna (Austria)

Applied methodologyCase study

Conclusions

Index

1 Applied methodologyA brief descriptionDiscontinuity sets extractionSpacing analysisSMRTool

2 Case studySources of informationMean planes and spacing analysisSMR results

3 Conclusions



Conclusions

A brief descriptionDiscontinuity sets extractionSpacing analysisSMRTool

Index



3 Conclusions



Conclusions


Flowchart

Data acquisition

Fieldwork3D LS SfM

DS extractionSpacing analysis

DS extractionSpacing analysis

RMRb RMRb RMRb

SMRSMR SMR

Comparison of results



Conclusions


Discontinuity Set Extractor (DSE) software

Figure : Classified 3D laser scanning dataset (A new approach for semi-automatic rock massjoints recognition from 3D PC, Riquelme et al, Computer and Geosciences, 2014)



Conclusions


Normal spacing analysis from 3D point clouds

Figure : Normal spacing from 3D PC (Riquelme et al, Engineering Geology, under review)



Conclusions


SMRTool: a calculator for computing SMR

Figure : SMRTool software (Riquelme et al 2014, on-line)



Conclusions

Sources of informationMean planes and spacing analysisSMR results

Index



3 Conclusions



Conclusions


Three sources of information for SMR calculation

Figure : Data acquisition: fieldwork, 3D laser canning and digital photogrammetry



Conclusions


Classified 3D point cloud

Figure : SfM dataset: meshed classified PC.



Conclusions


DS extraction and spacing analysis

J1 (121/33); S1=0.247; RMRb=68J2 (308/75); S2=1.425; RMRb=75J3 (253/64); S3=1.000; RMRb=78J4 (157/73); S4=0.650; RMRb=76

J1 (110/37); S1=0.331; RMRb=68J2 (308/60); S2=0.374; RMRb=75J3 (239/69); S3=0.278; RMRb=73J4 (152/78); S4=0.483; RMRb=71J5 (268/62); S5=0.213; RMRb=73J6 (056/83); S6=0.303; RMRb=73

J1 (114/32); S1=0.337; RMRb=68J2 (303/66); S2=0.289; RMRb=75J3 (242/76); S3=0.194; RMRb=73J4 (137/73); S4=0.395; RMRb=71J5 (268/69); S5=0.240; RMRb=71J6 (056/87); S6=0.320; RMRb=73

Fieldwork SfM 3D laser scanning

Figure : Extracted discontinuity sets and their corresponding normal spacing



Conclusions


Slope Mass Rating calculation

SMRfieldwork

0 20 40 60 80 100

SM

R3D

LS o

r S

MR

SfM

0

10

20

30

40

50

60

70

80

90

100

SMR fie

ldwor

k=S

MR 3D

LS o

r SfM

+20

points (+

1 clas

s)

-20

points (-

1 clas

s)

V

IV

III

II

Class I

(a) Slope Mass Rating; Zaleski, Slope (124/49)

SfM

3D laser scanning

Figure : Comparison of SMR results depending on the used dataset



Conclusions

Index



3 Conclusions



Conclusions

ConclusionsThe 3D dataset was classified and its DS were extracted. The use of 3D PCallowed to identify more mean orientations.

Normal spacing was calculated automatically using the classified PC. Using 3Ddataset, spacing was minor or closer to those obtained through classicalapproach.

The results comparison of SMR evidenced some deviations, up to 1 class.


Semi-automatic characterization of fractured rock massesusing 3D point clouds

discontinuity orientation, spacing and SMR geomechanical classification

A.Riquelmea1 R.Tomása2 A.Abellánb3 M.Cano a4 M.Jaboyedoffb5

a Civil Engineering Department, University of Alicante, Spain

b Center for Research on Terrestrial Environment, University of Lausanne, Switzerland

{[email protected], [email protected], [email protected],[email protected], [email protected]}

April 13, 2015


Documents

EGU 2015 presentación_