shm_presentation (nestor castaneda)

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Structural Health Monitoring: from algorithms to implementations Nestor E. Castaneda Graduate Research Assistant School of Civil Engineering College of Engineering Purdue University

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Page 1: Shm_presentation (Nestor Castaneda)

Structural Health Monitoring:from algorithms to implementations

Nestor E. CastanedaGraduate Research Assistant

School of Civil EngineeringCollege of EngineeringPurdue University

Page 2: Shm_presentation (Nestor Castaneda)

Outline

Introduction: Structural health monitoring (SHM) A static-based SHM algorithm A vibration-based SHM algorithm Wireless sensor role (WS) in SHM

Previous implementations Current SHM - WS research at the Intelligent

Infrastructure Systems Laboratory (ISSL) Concluding remarks

Page 3: Shm_presentation (Nestor Castaneda)

Introduction: Structural health monitoring (SHM)

Structural health monitoring allows the engineer to use sensing of the structural responses in conjunction with appropriate data aggregation and model updating techniques to evaluate the condition of a structure

STATIC - BASED

DYNAMIC - BASED

Page 4: Shm_presentation (Nestor Castaneda)

Introduction: Structural health monitoring (SHM)

Dynamic measurements of the systems as they vibrate under the influence of ambient and service loads are used to characterize the structural condition at any given time

Localization of damage is achieved by comparing characterizations in the pre- and post-damage states.

Full automation of the data aggregation and analysis is pursued for real-world applications.

Page 5: Shm_presentation (Nestor Castaneda)

A static-based SHM algorithm

Damage Identification Based on Dead Load Redistribution: Methodology

Shenton and Hu. (2006). Journal of Structural Engineering

Hypothesis: Dead load is redistributed when damage occurs in the structure.

Procedure: Static strain measurements due to dead load are used as input to

the identification procedure. The identification scheme is defined as a constrained optimization problem.

Page 6: Shm_presentation (Nestor Castaneda)

A static-based SHM algorithm

10,/)( UDU EIEIEI

Severity of damage:

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Damage location:Damage zone length:

a

Analytical model of damaged fixed-fixed beam

Page 7: Shm_presentation (Nestor Castaneda)

A static-based SHM algorithm

Analytical model with elements of discrete length

k

jmj

mj

tjaf

1

),,(

Minimize:

Subjected to:

LLa ;10;0

However, niiaanLlnLl i ,.......1);1(//

Therefore, minimize:

k

jmj

mj

tj

iaf1

),(

Subjected to: 10;,......,1 nai

Page 8: Shm_presentation (Nestor Castaneda)

A vibration-based SHM algorithm

Vibration-based Damage Detection of Structures by Genetic Algorithm Hao and Xia. (2002). Journal of Computing in Civil Engineering

Hypothesis: Structural damage is usually evidenced as localized modification on the stiffness configuration of an structure, leading to a change on the modal parameter values.

Procedure: Modal parameters, calculated from vibration data, are used as input to the identification procedure. The identification scheme is defined as a optimization problem and solved using a real-coded genetic algorithm

Page 9: Shm_presentation (Nestor Castaneda)

A vibration-based SHM algorithm

EA,

Damage identification scheme: optimization problem

Minimize: EATEAEA VVWVVJVVWJ )()()(22

where:

Frobenius norm

Changes in the modal parameters

Stiffness reduction factor (SRF)

Diagonal positive definite matrix of the weight for each term

V

WAnalytical and experimental data

Subjected to: 01

Page 10: Shm_presentation (Nestor Castaneda)

Number of measured points:j-th component of the i-th mass normalized mode shape:

Undamaged and damaged states:i-th eigenvalue:Number of measured modes:

Three objective functions are proposed:

Frequency (Eigenvalue) changes

Mode shape changes:

Frequency changes combined with mode shape changes:

2

0

0

1

2 })({

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Ui

Di

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np

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A vibration-based SHM algorithm

Page 11: Shm_presentation (Nestor Castaneda)

Wireless sensor role in SHM The SHM system based on Wireless Sensor Networks (WSN) has shown considerable promise.

It has several advantages over most traditional SHM systems: 1. Low production and maintenance cost.2. Fast installation 3. Reprogrammable software and convenient reconfiguration.

Using WSN, a dense deployment of measurement points in a SHM system is possible, which helps to refine the damage detection results

Page 12: Shm_presentation (Nestor Castaneda)

Wireless sensor role in SHM

1. On-board microprocessor2. Sensing capability3. Wireless communication4. Battery powered5. low cost

Berkeley Mote Mica2 (2004)

BTnode rev3 (2004)

U3 (2002)Prototype by Lynch (2002)1. On-board microprocessor2. Sensing capability3. Wireless communication4. Battery powered5. low cost

iMote2 (2004)

Page 13: Shm_presentation (Nestor Castaneda)

Wireless sensor role in SHM

Despite the potentiality offered by WS, some hardware limitations needs to be addressed when pursuing real SHM implementations using wireless sensors. Some of these hardaware limitations are associated to:

Wireless communication Time synchronization among sensors Reduced processing and memory capacity Power management

Page 14: Shm_presentation (Nestor Castaneda)

Previous implementations

WS nodes deployed on one of the beam girders (after Gangone et al, 2007)

Clarkson University researchers have implemented a wireless sensor system for modal identification of a full-scale bridge structure in New York

Page 15: Shm_presentation (Nestor Castaneda)

Previous implementations

Layout of nodes deployed on The Golden Gate Bridge (after Kim et al., 2007)

At the University of California, Berkeley researchers have designed and deployed a wireless sensor network on the Golden Gate Bridge.

Page 16: Shm_presentation (Nestor Castaneda)

Researchers at the UIUC have experimentally validated a SHM system employing a smart sensor network deployed on a scale three-dimensional truss model

Previous implementations

SHM implementation under hierarchical architecture (after Spencer and Nagayama, 2006)

Page 17: Shm_presentation (Nestor Castaneda)

Current SHM – WS research at the Intelligent Infrastructure Systems Laboratory (ISSL)

Researchers have primarily focused on developing Structural health monitoring (SHM) strategies to detect, locate and quantify damage, often using centralized data processing strategies

However, communication and power requirements of such centralized techniques do not match the capabilities offered by current wireless sensor technology

Research efforts at the ISSL are associated to develop distributed processing systems capable of fully utilizing wireless sensor embedded processing capacities to reduce communication load and energy consumption

ISSL : https://engineering.purdue.edu/IISL/

Page 18: Shm_presentation (Nestor Castaneda)

Damage Location Assurance Criterion (DLAC) DLAC approach identifies damage by evaluating the linear

correlation between frequency change vectors obtained by experimental measurements and an analytical model.

jT

jT

jT

jDLAC

2

Experimental frequency change vectors

Analytical frequency change vectors

healthydamagehealthy /

ahealthy

aj

ahealthyj /

Page 19: Shm_presentation (Nestor Castaneda)

DLAC implementation using wireless sensors

N : # SamplesW:# NF

Where: (N >> W)

Page 20: Shm_presentation (Nestor Castaneda)

DLAC implementation using wireless sensors

Page 21: Shm_presentation (Nestor Castaneda)

DLAC implementation using wireless sensors

Page 22: Shm_presentation (Nestor Castaneda)

DLAC implementation using wireless sensors

Page 23: Shm_presentation (Nestor Castaneda)

The implementation initializes the process by grouping the entire WSN in leaf sensor communities, each having cluster or leader nodes.

A first network composed only by cluster nodes perform an initial distributed modal identification, whose results are fed to a level-1 flexibility-based damage detection technique to localize regions of potential damage.

A second distributed modal identification is then performed by a reconfigured network that is composed by clusters and corresponding leaf sensor communities included and surrounded the determined regions of damage.

Finally, updated modal parameters are fed into a level-2 flexibility-based damage detection technique to detect damaged locations. CLUSTER NODES

LEAF NODES IMPLEMENTATION LAYOUT

Evaluation of a distributed flexibility-based damage detection technique for WS

C1,C2,C3,C4,C5 DEFINE SENSOR COMMUNITIESREGION OF DAMAGE

C1 C2 C3 C4 C5

Page 24: Shm_presentation (Nestor Castaneda)

Flexibility-based damage detection strategies

The Angles-between-String-and-Horizon (ASH) flexibility-based damage detection technique is proposed for structures dominated by beam-like behavior. The method computes the changes in angles between string-and-horizon of beam elements induced by the presence of damage.

The Axial Strain (AS) flexibility-based damage detection technique is proposed for structures mainly dominated by truss behavior. The idea is that if members in a structure are dominated by axial forces, the axial strain will be a better damage indicator than deflection.

Both techniques are supposed to be subsequently applied to refine the extent of potential damage locations up to an

accurate detection.

Page 25: Shm_presentation (Nestor Castaneda)

Distributed implementation

Page 26: Shm_presentation (Nestor Castaneda)

The evaluation is performed using a 3D steel truss structure. Wired sensors, deployed on the truss frontal panel joints and idealized as wireless sensor units, are employed to acquire horizontal and vertical acceleration data with Fs=250 Hz. Each damage scenario is recreated by replacing “damaged” members

with members having a reduced area of 52.7% of the original.

Experimental validation and results

Page 27: Shm_presentation (Nestor Castaneda)

The proposed two-level damage detection strategy is then used by considering the truss under two types of structural behaviors:

1. When considered globally, the truss is assumed to behave as a beam. Therefore the ASH method is used as level-1 damage detection technique with bay as a potentially damaged region.

2. Once damaged regions are detected, the AS method is used as level-2 damage detection technique having truss members be potentially damaged.

Experimental validation and results

CLUSTER NODESLEAF NODES

MAGNETIC SHAKER

TRUSS FRONTAL PANEL

D3 D1 D3

D2 D1

BAY # 6BAY # 1 BAY # 14

C1,C6 AND C13 DEFINE 1th 6th and 13th SENSOR COMMUNITIES

C6C1 C13

Page 28: Shm_presentation (Nestor Castaneda)

Experimental results4TH BAY 11TH AND 12TH BAY

0 5 10 150

0.2

0.4

0.6

0.8

1

1.2

x 10-5Level 1 - Damage Detection Results

Truss Bay Number

AS

H F

lexi

bilit

y D

amag

e In

dica

tor

7 8 9 101112131415161718190

1

2

3

4

5

6x 10-6

Level 2 - Damage Detection Results for Elements 12 and 41

Truss Element Number

AS

Fle

xibi

lity

Dam

age

Indi

cato

r

30 35 40 45 50 550

0.5

1

1.5

2

2.5 x 10-5

Truss Element Number

AS

Fle

xibi

lity

Dam

age

Indi

cato

r

Page 29: Shm_presentation (Nestor Castaneda)

Concluding remarks

SHM is a technique involving a set of procedures to determine the condition of a civil structure providing spatial and quantitative information about structural damage

Ability to continuously monitor the integrity of civil infrastructure offers the opportunity to reduce maintenance and inspection costs, and ensure a more reliable inspection than traditional methodologies

However, SHM algorithms must be robust enough to account for real implementation issues that can reduce their usefulness. Corruption of data due to experimental uncertainties, characterization of environment, reliable analytical models or small damage influence on early stages must be considered

Page 30: Shm_presentation (Nestor Castaneda)

Concluding remarks

Wireless sensors have become a promising and novel solution for SHM applications during recent times, due to their low implementation costs and embedded computational capacities.

However, SHM algorithms must be co-designed in parallel with WS hardware limitations to ensure power efficiency and scalability in the network and guarantee successful monitoring results