Structural Health Monitoring using Acceleration Data and ...Structural Health Monitoring using...
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Structural Health Monitoring using Acceleration Data and Machine Learning Techniques
Sifat Muin and Khalid M. Mosalam
2019 PEER Annual MeetingJanuary 17, 2019
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Outline
• Motivation and SHM background
• CAV as a damage feature
• CAV in Machine Learning
• H-MC Framework for SHM
• Conclusion
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• Current US infrastructure systems need
continuous monitoring.
Knowledge about damage Decision:
1. Damage plan proper response.
2.No damage immediate occupancy.
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Motivation
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SHM is the process to develop online damage detection and/or assessmentcapability for engineered systems (aerospace, civil, mechanical).
Sensing
Pre-processing
Feature Extraction
Pattern Recognition
Decision
Processing chain of SHM
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In this study, Machine Learning (ML)
PGA, Drift, Power Spectra, IRF, CAV, etc.
CAV: Cumulative Absolute Velocity
SHM Process
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CAV: Cumulative Absolute Velocity
𝐶𝐴𝑉 = 0
𝑇
𝑢 𝑡 𝑑𝑡
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Bridge column shaking table test
Ground motion 125%
scale
Sensor location
Column cross section
Time (s)
Undamaged
Undamaged
Muin S, Mosalam KM. Earth Spectra. 2017
CAV & Damage
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• Actual damage at ground, 2nd, 3rd & 4th floors
• Undamaged / baseline case from 1992
Landers earthquake
Roof
Ground
6th
3rd
2nd
No damage 6th-roof
Roof 6th 3rd 2nd Ground
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CAV & Damage
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CAV in Machine Learning
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“ML is the science of making computers learn & act as humans toimprove their learning over time in autonomous fashion, using data &information (observations & real-world interactions).”
Traditional Programming Machine Learning
New Data Function
Output
Training Data
Learned Function
Output
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Machine Learning (ML)
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• Supervised learning is inferring a function from labeled training data.• Unsupervised learning is inferring function from unlabeled training data.
Classification Example
Features: words, characters, size, etc.
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• Supervised learning • Regression – continuous output• Classification – discrete output
• Unsupervised learning
• Clustering – unknown output
Supervised & Unsupervised Learning
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• Supervised learning is inferring a function from labeled training data.• Unsupervised learning is inferring function from unlabeled training data.
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• Supervised learning • Regression – continuous output• Classification – discrete output
• Unsupervised learning
• Clustering – unknown output
Supervised & Unsupervised Learning
Classification Example
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SDOF model
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m
m
Story 1
Story 2
m
Story n
f1
f2
fn
m
m
Story 35f35
k(d)
SDOF Analysis
SDOF Results: TEST-1Input Features OLR LR ANN_10 ANN _100 SVM
CAV 80.54 82.88 80.54 81.71 79.38
𝑅𝐶𝐴𝑉 87.16 86.72 88.72 89.49 88.33
∆𝐶𝐴𝑉 75.10 75.10 75.10 77.04 75.10
CAV, 𝑹𝑪𝑨𝑽 90.27 89.44 88.72 90.66 91.05
𝑅𝐶𝐴𝑉, ∆𝐶𝐴𝑉 86.77 84.72 89.11 87.94 87.94
CAV,∆𝐶𝐴𝑉 80.54 83.27 80.54 81.32 79.38
CAV, 𝑅𝐶𝐴𝑉, ∆𝐶𝐴𝑉 90.27 89.05 90.27 90.66 89.88
CAV & 𝑅𝐶𝐴𝑉 used together as features give highest accuracy for both test cases
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SDOF model
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m
m
Story 1
Story 2
m
Story n
f1
f2
fn
m
m
Story 35f35
k(d)
SDOF Analysis
Input Features OLR LR ANN_10 ANN _100 SVM
CAV 36.67 12.50 18.33 15.83 8.33
𝑅𝐶𝐴𝑉 60.00 42.50 30.83 37.50 20.83
∆𝐶𝐴𝑉 61.67 45.00 42.50 40.00 21.67
CAV, 𝑹𝑪𝑨𝑽 74.14 61.67 18.33 40.00 25.00
𝑅𝐶𝐴𝑉, ∆𝐶𝐴𝑉 65.83 45.00 60.00 40.00 22.50
CAV,∆𝐶𝐴𝑉 70.00 60.00 51.67 36.67 24.17
CAV, 𝑅𝐶𝐴𝑉, ∆𝐶𝐴𝑉 70.00 61.67 38.33 54.17 25.00
SDOF Results: TEST-2
CAV & 𝑅𝐶𝐴𝑉 used together as features give highest accuracy for both test cases
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A MDOF model representing a 5-story structure
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MDOF Analysis
Class specific recall values for the two models
Class MDOF-US MDOF-NS
Undamaged 0.993 0.993
Minor 0.286 0.000
Moderate 0.781 0.463
Major 0.922 0.966
MDOF
model
Test
setLocation accuracy
MDOF-USTEST-1 97.5%
TEST-2 97.5%
MDOF-NSTEST-1 93.0%
TEST-2 95.0%
Locations were identified correctly even when damage locations were uncertain with CAV and 𝑅𝐶𝐴𝑉
H
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Human-Machine collaboration (H-MC) is a framework in which humans co-work withmachines to complete specific tasks by using the particular strengths of both human (H) andmachine (M).
Human-Machine Collaboration (H-MC)
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Between supervised and unsupervised learning, lies one class classification.
Available data from only one class.
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Novelty
Learned region
Training Observations
New Normal Observation
Novelty model:• Non-parametric (uncertain) distribution from
training data• Distance measure to detect novelty ≥ 1.5 ×IQR
Limitation: Novelty detection alone may result inFalse Positive (FP) due to lack of data from rare (strongbut undamaging) shaking.
Novelty Detection
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POE Envelope
• Structure-specific SDOF model with basic data
• NTHA using 1,710 ground motions
• Joint distribution using CAV & 𝑅𝐶𝐴𝑉 of damaging
events
CAV
𝑅𝐶𝐴𝑉 P
OE
1.0
0.0
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High
Probability of
damage
Low
Probability of
damage
POE Envelope
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CAV
𝑅𝐶𝐴𝑉
New data
CAV
𝑅𝐶𝐴𝑉
CAV
𝑅𝐶𝐴𝑉
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H-MC for Damage Detection
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CSMIP Buildings
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Undamaged cases correctly detected
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Undamaged Buildings
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Undamaged cases correctly detected
Novelty only gives FP
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Undamaged Buildings
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Damaged Buildings
Damaged case correctly detected
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The Future
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Thank You!
Acknowledgements:
Dr. Selim G unay, Dr. Umberto Alibrandi, & Dr. Yousef Bozorgnia.
Funding Sources:
California Strong Motion Instrumentation Program (CSMIP) &
Taisei Chair in Civil Engineering