Dec 3, 2013 ... The SmartEN project example: Civil and Electronic Engineering. Evripidis
Karseras. (
). Papers Discussed: 1.
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
Multi-disciplinary Research - Why should we care ? The SmartEN project example: Civil and Electronic Engineering
Evripidis Karseras (
[email protected])
Papers Discussed: 1. V.K. Dertimanis,“On the use of dispersion analysis for model assessment in structural identification” 2. L. Llano et al. “Stochastic Image-based Feature Extraction and its Correlation with Mechanical Properties of Corroded Rebars for Assessment Purposes”
3 December 2013
The SmartEN Project
Model Based Assessment
Experimental Results
Introduction
The SmartEN Project Model Based Assessment Experimental Results Image-based Feature Extraction
Image-based Feature Extraction
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
Objectives • A joint multidisciplinary research training programme. • Address specific EU needs in the areas of: • Monitoring and smart proactive management of Structural Systems • Heritage and Infrastructure • Transportation Infrastructure Systems • Urban Micro-climate. • To take forward research in WSN, DSP and Non-destructive
Evaluation: successful application of WSN in the smart proactive management of the built and natural environment. • 15 partners: Industry(Network Rail, DFL Systems Ltd,...) and
Academia (Imperial College,...) • Budget: ∼ 4 m. Euros.
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
Challenges in Electrical Engineering
• Work Programme 1. Wireless Sensor Networks • Communication protocols . • Distributed OS for Reconfigurable Sensor Networks. • Energy Harvesting and Conservation. • Localization. • Work Programme 2. Sensor Signal Processing • Distributed processing and Aggregation. • Signal Processing for Event Classification. • Middleware.
The SmartEN Project
Model Based Assessment
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Image-based Feature Extraction
Challenges in Civil Engineering
• Work Programme 3. Non Destructive Evaluation • Optimum Sensor Locations and Requirements for NDE. • Combined Monitoring and Inspection Systems. • Assessment and Long Term Performance Modelling. • Performance Model Updating Based on Sensor Information. • Damage identification. • Work Programme 4. Smart Proactive Management • Proactive Management Strategies. • Life Cycle Design and Assessment. • Multi-objective Optimisation.
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
Challenges in dealing with the Challenges • The ECE: The objectives are not achieved in a “theory then
application” manner. • The CE: The objectives are not achieved in a “we need this data
how to get it” manner. • Multi-disciplinary research is key. Otherwise results will be trivial for
the opposite field, hence no actual gains. • Technical challenges: What does “frequency” mean ? What does
“system” stand for ? What does “parameter” represent ? • Civil Engineering: Large-scale problems affecting great populations. • Sometimes the term “ground truth” might actually mean a bridge
collapsing...
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
Preliminaries
• Sustainable development: evaluate the current state of a system
(performance). • Sensing → Estimation → Decision. V K. Dertimanis. On the use of dispersion analysis for model assessment in structural identification. Journal of Vibration and Control, 2013.
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
Data-driven Modelling
(a) Non-parametric (e.g. STFT)
(b) (some system response)
• System undergoes some form of excitation. • The system’s response is measured. • Analysis: Parametric (robust,consistent,accurate) / Non-parametric
(simplicity,inaccurate,biased).
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
Finite Element Models
(c) An example (source: internet)
(d) Actual: Tacoma Narrows Bridge
• Model may not be available (existing infrastructure). • Model weather (e.g. wind, temperature), stresses (e.g. traffic),
environment (e.g. water).
The SmartEN Project
Model Based Assessment
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Image-based Feature Extraction
Modal Assurance Criteria - I • Modal Analysis: Imagine an old car and slowly accelerating... • Vibration Excitation → Natural mode shapes and frequencies. • Physical Interpretation of eigenvectors, i.e. a mass-spring system:
MU00 + CU0 + KU = F where M is mass, U is displacement, C is damping, K is stiffness and F is force. • The free-vibration (force, damping equal 0) are given by:
(K + λM) = 0 where λ is an eigenvalue and U00 = λU is assumed.
(1)
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Image-based Feature Extraction
Modal Assurance Criteria - II • The modes are excited differently and change as the structure
deteriorates. • The MAC is a statistical indicator. • It basically measures the correlation between measured mode-shapes
and analytical. • For example FEM analysis gave mode-shapes a1 , a2 , ... and the
experiments gave m1 , m2 , .... The MAC is another real matrix defined as: |aT mj |2 MACi,j = T i T |ai ai ||mj mj | • Other criteria exist involving the frequency domain and other.
The SmartEN Project
Model Based Assessment
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Image-based Feature Extraction
The State-Space model System Model ξ(t + 1) = Fξ(t) + Gu(t) + Ke(t) y(t) = Hξ(t) + Du(t) + e(t) where u, ξ, y are the excitation, state and response vectors respectively. It is derived from Equation (1). • Problems involve: • Estimating noise process variance Σe . • Estimating the model order state vector size. • Estimating the associated matrices. • Various cases are of interest (e.g. no excitation, no noise, etc)
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
The Proposed Framework • Introduces a new metric based on the output covariance matrix
decomposition. • Measure the contribution of each mode to the total vibration energy. • Shows how it can be used in parallel with traditional techniques. • The ability of the framework to detect spurious modes. • Can be used both in forward and backward engineering.
Dispersion Metric It is proven that P the output covariance matrix at zero lag can be written as: Γy (0) = nk=1 Qk , where matrix Qk is derived based on the model and denotes the modal dispersion. The k th normalised dispersion matrix is defined as [Qk ]i,j [∆k ]i,j = [Γy ]i,j
The SmartEN Project
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Image-based Feature Extraction
Simulated: NASA Mini-Mast truss structure.
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Image-based Feature Extraction
Experimental Process • Use theoretical results from FEM analysis of the structure. • A 2-input/2-output, 10th order state-space model is available. • Input: Torque wheels, Output: Displacement sensors.
Figure : Vibration modes and metrics.
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Image-based Feature Extraction
Results - I • Simulate data with zero mean Gaussian excitation with a given
covariance matrix. • Consider 10% measurement noise. • Apply a subspace identification method to recover the best model.
Figure : Frequency stabilisation diagram.
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Image-based Feature Extraction
Results - II • All modes have been identified successfully (and spurious). • Results agree with other metrics.
Figure : Vibration modes and metrics for model order n = 14.
The SmartEN Project
Model Based Assessment
Experimental Results
Experimental Structure - I • A suspended steel subframe flexible structure. • Data-set is taken from a database. • 2 force input signals/28 acceleration responses.
Figure : Frequency stabilisation diagram.
Image-based Feature Extraction
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
Experimental Structure - II
Figure : Vibration modes and metrics for model order n = 37.
The SmartEN Project
Model Based Assessment
Experimental Results
Image-based Feature Extraction
Thoughts ?
• Sparsity in the state vector ? • Maybe infer better models ? • The method described performs dimensionality reduction. • ...?
The SmartEN Project
Model Based Assessment
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Image-based Feature Extraction
Preliminaries
Figure : Experimental Setup.
• Chloride induced corrosion on steel reinforcement bars in concrete. • High economical and social costs. • Structural performance is assessed by mechanical models. L. Llano, M.K. Chryssanthopoulos & A. Hagen-Zanker Stochastic Image-based Feature Extraction and its Correlation with Mechanical Properties of Corroded Rebars for Assessment Purposes. SmartEN ITN Final Conference, 2013.
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Image-based Feature Extraction
Accelerated corrosion experiments
• Embed steel rebars in concrete. • 28 days of imposed current. • Imposed on different intervals to achieve different degrees of
corrosion. • After the experiment, a standarised procedure is followed to extract
the rebars. • Specimens were visually and gravimetrically assessed.
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Image-based Feature Extraction
3-D Scanning of the Rebars
Figure : Unwrapped rebar difference profile.
• The corroded rebars are scanned by a 3-D scanner at 0.1mm
accuracy. • The results are compared to an intact rebar. • An optimal discretisation grid is found and the profiles are analysed.
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Image-based Feature Extraction
Relationship to image processing
Figure : Pit depths vs. Pit area for 12% and 3% average mass loss.
• A pit (damaged area) consists of two attributes: general thinning
and a localised part. • Use the 2-D Discrete Wavelet Transform for multi-scale analysis.
The SmartEN Project
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Image-based Feature Extraction
Relationship to image processing (cont’d)
• The wavelet coefficients at each level can be used to quantify
damage. • Statistical models can be built to relate damage to the time scale of
experiments. • Extend the mechanical properties of damaged rebars for a whole
structure.
The SmartEN Project
Model Based Assessment
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Image-based Feature Extraction
Thoughts ?
• The damage profiles are sparse images... • The experimental process is a dynamical system... • The experimental process takes a lot of time. • The results could save the bridge owners a lot of time and money.
The SmartEN Project
Model Based Assessment
Experimental Results
Thanks !
Image-based Feature Extraction
