Post on 16-Jul-2020
Slope failure prediction using a decision tree: A case of engineered slopes in South Korea
Ivy F. Guevarra1 *, SangGi Hwang1, ByungOk Yu2
1Department of Civil, Environmental and Railroad Engineering Paichai University, Daejon, South Korea
2Geotechnical Research Group, Highway Research Center, Korea Highway Corporation
UP-NIGS AVR, 15 JUNE 2012
Presentation outline
Introduction
Objective
Methodology
Analytical result
Discussion
Conclusion
Slope Failure prediction using a decision tree
Problem: How useful are massive slope databases?
Annual slope survey
Massive data
Costly yet not fully utilized
How should we utilize it?
How useful is the database?
Slope Failure prediction using a decision tree
Objective
Massive Database
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Igneous Metamorphic Sedimentaray Total
Lithology and Average
Failu
re n
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1 km
Total Area KyungGi OgChon YoungNam KyungSang
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Slope Angle
Slo
pe H
eig
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Slope Failure prediction using a decision tree
1. Find informative slope prediction rules from the massive data
2. Quantify success rate 3. Find the most relevant slope
factors influencing slope failures in the Korean slope database
Statistical Analyses
Korean Engineered Slope Database
South Korea Highway slopes Byways slopes
Data source Korea highway corporation (KHC) Korea Institute of Geological, Mining and Materials( KIGAM ) Korea Meteorological Association (KMA)
13,036 slope entries 23 slope factors
Slope Failure prediction using a decision tree
Additional slope factors
Tectonic Domain
Tectonic Domain
Lithology
Precipitation
Selected Slope factors
Slope Failure prediction using a decision tree
METHODOLOGY: Data Mining
A young interdisciplinary field of computer science
Extracts useful information from an existing data set and transforms it into an understandable structure for further use
Does classification,anomaly detection, association, clustering, regression, sequence analysis
Classification generates prediction rules
Best for slope prediction studies
Methods Neural network, decision trees
Nonparametric: does not require the common
normal distribution assumption for input data
Internal structure of the Decision trees can be easily reviewed as compared to the
“black box” structure of the neural network.
Classification using Decision Trees
Decision Tree Classification
Slope Failure prediction using a decision tree
Classification Rules
MINED RULES 1. If Slope Dip > 50°, then slope Fails 2. If Slope Dip < 50°, Precipitation > 500mm,
Then slope Fails 3. If Slope Dip < 50°, Precipitation > 500mm,
Then slope Safe
Classifying a given slope
Classification Modeling and Validation Flow
WEKA 3.5 Software
J48 Decision Tree Technique
C4.5 Algorithm
Dept. of Computer Science
Waikato University
New Zealand
Database is divided into 10 sets 1 held out
9 used to generate rules
Rules applied to the hold out set
Error rate is generated
10 iterations 10 Error rates
Average Error rate as overall error
Least error rules chosen
Extreme case 1: Too much rules
Extreme case 2: Too few rules
Avoiding over and under fitting
Slope Failure prediction using a decision tree
Methodology: Ranking the slope factors
Slope Failure prediction using a decision tree
Attribute Ranking ALGORITHMS
Ranking based on relevant criteria
Uses 5 different rankers
Unique equation for relevance
independent mathematical, information and statistical functions
Weka 3.5 software
Attribute evaluators
Result:
Decision tree 174 rules
Represented in the diagram
Ranking Average ranking
Important attributes
Statistical Measure Overall success rate
Success rate of fail and safe slopes
Slope Failure prediction using a decision tree
Decision tree rules
If seepage is present, slope fails
1
Slope Failure prediction using a decision tree
Rules: Group 1
If seepage is present Upper, then slope fails
Middle , then slope fails
Lower
Rainfall is evaluated
Classified 829 (12%) observation
651 out of 829 failed
Caused by the presence of seepage
Commonly known rules
1
Decision tree rules
If seepage is present, slope fails If rainfall>267 mm,
slope fails
1
2
Slope Failure prediction using a decision tree
Rules: Group 2
If seepage is absent, slope dip >61°
Rainfall
Fracture Orientation
Weathering
Classified 21% observation
1016 out of 1413 failed
Characterized by high slope and high precipitation >267mm rainfall, slopes could fail, unless low angle
>501mm, fails except low angles and least weathered
2
Decision tree rules
Slope Failure prediction using a decision tree
Rules: Group 3 3a
If seepage is absent Slope dip is <61°
Known Fracture Orientation:3a
Unknown fracture orientation:3b
Group 3a
Antislope fails if rainfall>412mm
Low angles are safe unless notches are present
Most syn slopes fail
Safe if notches are absent
Safe in some tectonic domains (Kyungsang, Ogchon, Youngnam)
Decision tree rules
3bRules are more complicated 3b: Banded gneiss fail in high amount of rainfall (>454 mm).
However, if weathering is less intense, it is safe
Least important slope factors
Slope Failure prediction using a decision tree
Rules: Group 3
If seepage is absent Slope dip is <61°
Known Fracture Orientation:3a
Unknown fracture orientation:3b
Notches, rainfall, slope dip,
lithology,tectonic domain
Group 3b
Widely branching, not easy to describe
Granitic rock are strong, except for slope dip>41° and weathering is high
If rainfall is lower than 455mm but dipping >43.2° and lithology are shale, schist, diorite, layered conglomerate and sandstone, then it could fail.
Natural slope, height, length were least used
3b
Ranking Results
Slope Failure prediction using a decision tree
Ranking Results Ranking
1.Fracture
2.Lithology
3.Seepage
4.Rainfall
5.Slope dip
6.Notches
7.Domain
8.Topography
9.Weathering
10.Natural slope
11.Heigh
12.Length
Selectors:
1.Information Gain 2.Gain Ratio
3.Symmetrical Uncertainty 4.ReliefF 5. Chi-square
Slope Failure prediction using a decision tree
Result: Statistical Validity
Slope Failure prediction using a decision tree
Number of instances 100% 6,828
Correctly predicted instances
71.98% 4,915
Incorrectly predicted instances
26.02% 1,916
Success rate
Discussion: Ranking
Observed discrepancy in ranking between selector and decision tree
Higher ranks supports the higher branch
Seepage(3rd), slope dip (5th), rainfall (4th) and fracture orientation (1st) control the tree
Not all highly ranked control the structure of the tree: lithology (2nd)
Discussion: Ranking
Attribute selector credits
homogeneity of the divided data
higher amount divided
ability to make a unique classification
Height, length, natural slope dip
homogeneous, but the divided records are minimal
Contributes little in making the classification unique
Should not be part of sequential ranks
Discussion: Slope Factors
Slope Height: A higher rock slope is likely to generate unstable block geometry,
DT rules did not show this
But engineered slopes are designed and constructed such that height is a critical factors in reinforcement selection.
Detection of common slope stability phenomenon Failure when seepage is noted
Lower slope angle, raises safety factor. Further branching were created using rainfall and lithology rendered safe
rules
BUT has high failure rate 72% in higher slope dip, 48% in lower
Discussion: Slope Factors
Rainfall: Heavy and continuous rainfall could trigger failure
Detected by DT
Limitation: no failure date, cant correlate with the exact amount of rainfall
Limitation: Rainfall data should be improved
Dip direction of fractures Fracture dipping towards slope face, fails slope
Fracture dipping away, safe slope
Discussion: Complex Rules
Slope failure is governed by not just one but a combination of many 3b: If lithology is schist:
slopes with notches fail
Slopes without are safe
If lithology is banded gneiss
Higher rainfall fails Presence of notches, high intensity of weathering
Lower rainfall is safe
Conclusion
Limitations of the study
Safety of the engineered slope has to be evaluated by detailed site specific information
Depending on the trained rule, not all slope factors were used in classification
However, this DB simplifies such information extensively
Generated174 slope rules
to be used to preliminary studies
Conclusion
Commonly known rules If seepage is present, then slope fails
If rainfall has greater amount, then slope fails
If slope dip is higher, then slopes fails
Complex rules If rainfall is high, slopes with banded gneiss tend to
fail.
If rainfall is less, banded gneiss is safe
If the intensity of weathering is high, slope with banded gneiss fail but slopes with lesser intensity are safe.
Least important slope factors
Slope Failure prediction using a decision tree
Conclusion
Despite limitations, the rules predicted 61% of safe and 80% of failed slope, and predicted 72% of combined safe and failed slope
Gathering massive slope data is expensive, requires a lot of effort, suffers from inconsistencies of survey, such people become skeptical for failure analysis studies
72% is encouraging
Rules to be tested in new observation
Conclusion
Important slope attributes are ranked orientation of major fractures, lithology, seepage,
rainfall, slope dip, notches, domain, lithology, topography, weathering
Least important attribute Natural slope, height, length
Slope Failure prediction using a decision tree
Thank you all!
Thank you too!
Department of Civil, Environmental and Railroad Engineering, Paichai University, Doma 2-dong, Seo-Gu, Dajeon, South Korea Geotechnical Research Group, Highway and Transportation Technology Institute HwaSeong-Ri, GyeongGi-Do, South Korea Korea highway corporation (KHC) Seongnam-Si, GyeongGi-Do, South Korea Korea Institute of Geological, Mining and Materials( KIGAM ) Gajeong-dong, Yuseong, Daejon South Korea Korea Meteorological Administration (KMA) Dongjak-Gu, Seoul, South Korea WEKA 3.2 Department of Computer Science, University of Waikato Knighton Road, Hamilton, New Zealand
kMining.com (http://www.kmining.com)