BIG DATA and Data Mining. Developments of Models ...

Sichuan University College of Hydraulic and Hydroelectric Engineering

BIG DATA and Data Mining. Developments of Models in GeoEngineering Luis Ribeiro e Sousa Sichuan University, Chengdu, China

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Chengdu, July 14, 2015

Contents 1. BIG Introduction 2. Underground Hydroelectric Schemes, Portugal o o

Venda Nova II Bemposta II

3. Models for Geomechanical Characterization of Rock Mass at DUSEL o o o

Geotechnical investigations at Sanford Laboratory Development of Geomechanical Models Use of Bayesian Networks (BN)

4. Models for the prediction of rockburst indexes o o o

Rockburst laboratory tests Rockburst maximum stresses Rockburst indexes

5. BIG Conclusions 2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering


1. BIG Introduction

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Use of Data Mining techniques • The uncertainties in underground structures are related with geotechnical conditions and construction. • The determination of geotechnical parameters in RMs for underground works is submitted to high uncertainties. • A rigorous determination of the geomechanical parameters is the key for an efficient design and rigorous of the supports and for the excavation process. • The methodologies are based in laboratory and in situ tests and in the application of empirical methodologies (RMR, Q e GSI). • Prediction of rockburst indexes based on laboratory tests. • Multiple Model System Identification (Clustering Multiple Models) Schem of the Schwandbach bridge used to illustrate the proposed methodology for iterative sensor placement

2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering

Genetic algorithms

Symbolic techniques: rules and decision trees

Neural networks

Clustering

Bayesian networks Tools for Data Mining

Fuzzy logic

Machine Learning Tools and DM Techniques 2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering

Rule based systems

Steps in the process of discovering patterns in Databases


DM and discover of knowledge • Commercials – – –

SAS Enterprise Miner SPPS Clementine IBM Intelligent Miner

• Public Domain – –

WEKA R Environment

• Inserted in a SGBD – –

Commercially available DM software 2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering

Oracle SQL Server


2. Underground Hydroelectric Schemes, Portugal o Venda Nova II o Bemposta II

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DM application to Venda Nova II

access tunnel to the caverns, with about 1.5km, 10.9% slope and 58m2 crosssection

hydraulic circuit with a 2.8km headrace tunnel with 14.8% slope and a 1.4km tailrace tunnel and 2.1% slope, with a 6.3m diameter

upper surge chamber with a 5.0m diameter and 415m height shaft

powerhouse complex located at about 350m depth with two caverns, for the powerhouse and transforming units, connected by two galleries



Initial data of Venda Nova II Excel file – 1230 registers, 60 attributes Q system – RQD, JW, Jn, Jr, Ja, SRF, Q, Q’, RQD/Jn, Jr/Ja, Jw/SRF, log Q, log Q’ RMR system – P1, P2, P3, P4, P5, P6, RMR, P41, P42, P43, P44, P45 GSI system – GSI, RCU, mb-D=0, mb-D=0,5, mb-D=0,8, mb-D=1, s-D=0, s-D=0,5, s-D=0,8, s-D=1, σcm-D=0, σ3max-D=0, φ-D=0, c’-D=0, σcm-D=0,2, σ3max-D=0,2, φD=0,2, c’-D=0,2, σcm-D=0,5, σ3max-D=0,5, φ-D=0,5, c’D=0,5, σcm-D=0,8, σ3max-D=0,8, φ-D=0,8, c’-D=0,8, σcmD=1, σ3max-D=1, φ-D=1, c’-D=1 Others – N, RCR 2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering

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Fluxogram used for the DM tasks 2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering

RMR

Histograms of RMR, Q and GSI

Q

GSI


Expressions used in the calculation of E


Comparison between the number of times the expressions were calculated with the number of times the result was within the considered interval


100

RMRpred = 0,9989RMR R2 = 0,9539

predicted RMR

80 60 40 20 0 0

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predicted Em (GPa)

RMR

80 70 60 50 40 30 20 10 0 -10 0

Empred = 0,9944Em R2 = 0,9759

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40 Em (GPa)


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φ - Importance of variables for Dataset 1




Bemposta dam, Portugal Built in 1964, H=87m, length – 297m


Repowering of Bemposta II, Portugal

Topology of the error function for the theoretical calculation: 3D view


Database at Bemposta II The database is composed by the following information: • 286 lines with RMR values and their parameters (P1 to P6). • 270 lines with the Q values and their parameters (RQD, Ja, Jn, Jr, Jw and SRF). • 686 lines with the values of SMR and parameters P1 to P5 and adjustment factor AF.

• MR models were computed for RMR and E and for this parameter also two multilayer perceptron ANN (with one or two hidden layers) were developed.


Models for RMR Parameters MAD RMSE

R2

Obtained models for RMR with sets of 2 and 3 input parameters

Model RMR= 27.134+0.002xP23+

P2+P4

3.288 4.035

0.685 0.851xP4 RMR= 29.665+0.047xP22+

P2+P5

3.291 4.158

0.665 0.053xP52 RMR= 38.623+0.004xP13+

P1+P2

3.367 4.357

RMR  27.134  0.002  P23  0.851 P4

0.632 0.002xP23

(16) RMR  9.848  1.206  P5  0.913  P4  0.043  P22

RMR= 9.848+1.206xP5+ P2+P4+P5 2.281 2.757

0.853 0.913xP4+0.043xP22 3

RMR=24.771+0.002xP2 + P1+P2+P4 2.257 3.094

0.815 0.004xP13+0.895xP4 RMR=25.467+0.804*P1+

P1+P2+P5 2.894 3.708

0.734 0.041*P22+0.046*P52


Best RMR models with 2 and 3 input parameters.

Developed models for E Comparison of the E values obtained by the in situ tests and by the empirical formulae.



3. Models for Geomechanical Characterization of Rock Mass at DUSEL o Geotechnical investigations at Sandford Lab o Development of Geomechanical Models o Use of Bayesian Networks (BN)

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Geotechnical investigations for the scientific experiences at Sandford Laboratory • In 2000, it was announced that the Homestake Gold Mine, located in Lead, SD, would cease operation. The mine had been the site of a physics experiment, which operated for 30 years, aimed at the detection and quantification of neutrinos originating from the sun. • The ability to install experiments underground is important because the overlying rock can shield sensitive detection experiments from cosmic radiation. Currently, experiments aimed at the detection of neutrinos from directed beam lines, generation of neutrinos from natural radioactive breakdown processes, and the detection of dark matter are either • being contemplated or are being installed in • the underground at the Sanford Laboratory at • Homestake.

Homestake Mine – started in 1876 Depth of 2,5km; 600km of galleries Nobel prize of Physics by Raymond Davis in 2002


Purposes for DUSEL • • • • • • •

Dark Matter (A, B, and C) Neutrinoless Double Beta Decay (F e G) Nuclear Astrophysics (I) Proton Decay (E) Long Baseline Neutrinos (D, F, e H) This panel indicates the meaning of these experiences about physics of our universe and its history (J). The Laboratory incudes a multidisciplinary research program in the Earth Sciences including geomicrobiology (K), ruptures in faults (M and N), monitoring of excavations, coupled processes (L), and seismic monitoring systems (M).

Lesko et al., 2011


Graphical representation of DUSEL

Lesko et al., 2011


Long Baseline Neutrine Experiment (Roggenthen, 2012)

Vertical cross at Fermilab showing (from the right to left) the inclination of the beamline, target hall, decay pipe, and the detector complex. 2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering

Development of concepts for DUSEL

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BIG DATA & DM. Developments of Models in Geo-Engineering


Geotechnical investigations 1. Geology 2. Mapping of galleries 3. Boreholes and analysis of samples 4. Use of Televiewer 5. In situ stress measurements 6. Numerical modelling 7. Laboratory tests 8. Monitoring of vibrations due to the use of blasting 9. Scanning by laser 10. Use of Data Mining techniques 11. Application of Bayesian Networks (BN)


Isometric view of the geological structures in the neighboroud of LC-1 (level 4850)


Geologic map at 4850 L and localization of the boreholes showing the planned in the triangle between shafts Ross e Yates (Golder)

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Respec, 2010


Summary of rock mass classifications for the rock mass zones


Brazilian tests (49) UCS (54)

Triaxial tests (29)

RESPEC Shear tests on discontinuities and discontinuities (28) 2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering

Acoustic and optical techniques were used, depending of the presence of water in the borehole.

Televiewers

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Borehole BH 3.

The knowledge about in situ state of stress in Homestake mine was establishe before in the mine by Pariseau (1985) and Johnson et al. [1993].

1 psi

= 0,0069 MPa

RESPEC, 2010 2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering


3D modelling by FE (Golders)

Numerical results considering 3 large cavities 2 1 3 5 BIG DATA &4DM. Developments of Models in Geo-Engineering

Processes for risk analysis (Popielak & Weinig, 2010)

A process developed by Golders Associates and applied to DUSEL consist in the following activities: i) ii) iii) iv)

Ranking of risk factors System to be modeled Conceptual models of the system Numerical analyses for the study of potential impacts of risk factors, and v) Risk management plan for the management of the different risk factors.


Results obtained for RQD, RMR, GSI and Q

LF&A, 2009


Mapping location map at 4850 level

Mapping location map at 4850 map

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BIG DATA & DM. Developments of Models in Geo-Engineering

RMR Models RMR  13.022  1.195  P2  1.118  P3  1.352  P5

(5)

RMR  29.404  1.270  P2  1.258  P3 2 input parameters

(6)

3 input parameters

MAD

RMSE

MAD

RMSE

MR

1.248

1.543

1.137

1.598

ANN

1.317

1.833

1.330

2.279

SVM

1.584

2.384

1.168

1.828


– Metrics for the predictive models for the RMR index

Real versus predicted RMR values for models using two input parameters


GSI Models

GSI  33.916  0.420  P3  0.472  RQD  0.013  UCS 3 input parameters MAD

RMSE

MR

4.411

5.559

ANN

4.522

5.650

SVM

4.278

5.578

Metrics for the predictive models for GSI


Q Models Q  0.656  0.016  RQD  0.408  J r  0.353  RQD J n

(8)

Q  0.037  0.020  RQD  0.390  RQD J n

(9)

(8)

(9) Table 4 Metrics for the predictive models for the Q index 2 input parameters

3 input parameters

MAD

RMSE

MAD

RMSE

MR

0.031

0.054

0.020

0.028

ANN

0.031

0.065

0.003

0.008

SVM

0.040

0.106

0.009

0.029


Use of Bayesian Networks (BNs) BNs are another possibility that allows the introduction of uncertainties related to the geotechnical and construction aspects, risk management and decision making during construction.

BN for Risk Analysis of storage of CO2 with the existence of activate fault (presented at the 4th Sino-German Conf.)


Several BN where learned and tested with the cases available on the database, using the software GeNIe. In this specific study, only models that allow predicting RMR values were developed. They were trained with about 4/5 of the cases and tested on 25 different cases. The algorithm used for learning the models was the “greedy thick thinning” with a uniform prior. For detailed information on the greedy thick thinning algorithm please refer to Heckerman

Naïve Bayesian network with 5 parameters (P1, P2, P3, P4, P5)

Figure shows the structure of learned models obtained using five parameters (P1, P2, P3, P4 e P5),


Naïve BN with 3 parameters (P2,P3 and P5)


BN with 2 parameters (P2 and P3)


Strength of Influence

The learned networks were tested on 25 randomly selected cases from the database (not used to train the networks).


Accuracy results for RMR predictive BN models

BN Models a) Naïve Bayesian network with 5 parameters (P1, P2, P3, P4, P5) b) Bayesian Network with 3 parameters (P2, P3 and P5) c) Bayesian Network with 2 parameters (P2 and P3)


Accuracy (%) 68%

72% 76%


4. Models for the Prediction of Rockburst Indexes o Rockburst laboratory tests

o Rockburst maximum stresses o Rockburst indexes

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2 4 1 3 5 Developments of Models in Geomechanics Using DM Techniques

Influence diagram of rockburst Fig. 6. Influence diagram of rockburst (Adapted from Sousa 2010).

Type and rock strength

Geometry (Shape and equivalent diameter)

Faults (Folding)

Construction method (Support & advanced rate)

Rockburst

Stress state (Overburden & K=σh/ σv)

Severity (Time delay)

Dimensions of burst (Location)

Damage of tunnel

Fatalities & injuries


Rockburst testing laboratory system


Rockburst testing system

2 4 1 3 5 Developments of Models in Geomechanics Using DM Techniques

Rockburst tests in different rock types.


Fields considered in the database Field Location of the test Dimensions of sample Rock material Main minerals and cracks Stresses before loading (MPa)

Stresses during tests (MPa)

Characteristics of test Critical depth (m)

rockburst

Topics Location sample; depth (m); country; date. Code, length; width; height (mm); volume (cm3) Type of rock; RQD; UCS (MPa); Specific weight (g/cm3); E (GPa) Elastic modulus; ν – Poisson ratio. % clay; % feldspar; % calcite; % carbon; Existence of cracks σv – vertical in situ stress; σh1 –horizontal in situ stress; σh2 – horizontal in situ stress in the face to be unloaded; I1 (first invariant of the stresses); I2 (second invariant of the stresses); I3 (third invariant of the stresses). Rockburst maximum stress (σRB); maximum stress axis; loading rate in MPa/s; unloading rate for vertical stresses in MPa/s; unloading times. Type of burst position; duration of the test in minutes; time of burst delay (minutes); mainly shape of fragments. Critical depth; rockburst risk index.

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Rockburst indexes

rockburst critical depth

Value of IRB IRB