Structural Health Monitoring: from algorithms to implementations

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

2. 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

3. 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

4. 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.

5. 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.

6. Analytical model of damaged fixed-fixed beam A static-based SHM algorithm Severity of damage: Damage location: Damage zone length:

7. A static-based SHM algorithm Minimize: Subjected to: Analytical model with elements of discrete length However, Therefore, minimize: Subjected to:

8. 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

9. Damage identification scheme: optimization problem Minimize: Subjected to: where: Frobenius norm Changes in the modal parameters Stiffness reduction factor (SRF) Diagonal positive definite matrix of the weight for each term Analytical and experimental data A vibration-based SHM algorithm

10. Three objective functions are proposed: Frequency (Eigenvalue) changes Number of measured modes: i-theigenvalue: Undamaged and damaged states: Mode shape changes: Number of measured points: j-th component of the i-th mass normalized mode shape: Frequency changes combined with mode shape changes: A vibration-based SHM algorithm

11. 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: • Low production and maintenance cost. • Fast installation • 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

12. On-board microprocessor • Sensing capability • Wireless communication • Battery powered • low cost • On-board microprocessor • Sensing capability • Wireless communication • Battery powered • low cost Prototype by Lynch (2002) U3 (2002) Berkeley Mote Mica2 (2004) BTnode rev3 (2004) iMote2 (2004) Wireless sensor role in SHM

13. 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

14. Previous implementations • Clarkson University researchers have implemented a wireless sensor system for modal identification of a full-scale bridge structure in New York WS nodes deployed on one of the beam girders (after Gangone et al, 2007)

15. Previous implementations • At the University of California, Berkeley researchers have designed and deployed a wireless sensor network on the Golden Gate Bridge. Layout of nodes deployed on The Golden Gate Bridge (after Kim et al., 2007)

16. Previous implementations • Researchers at the UIUC have experimentally validated a SHM system employing a smart sensor network deployed on a scale three-dimensional truss model SHM implementation under hierarchical architecture (after Spencer and Nagayama, 2006)

17. 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/

18. Analytical frequency change vectors Experimental frequency change vectors 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.

19. DLAC implementation using wireless sensors N : # Samples W:# NF Where: (N >> W)

20. DLAC implementation using wireless sensors

21. DLAC implementation using wireless sensors

22. DLAC implementation using wireless sensors

23. Evaluation of a distributed flexibility-based damage detection technique for WS • 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. • 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. C1 C2 C3 C4 C5 REGION OF DAMAGE C1,C2,C3,C4,C5 DEFINE SENSOR COMMUNITIES

24. 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 techniqueis 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.

25. Distributed implementation

26. Experimental validation and results • 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.

27. Experimental validation and results • The proposed two-level damage detection strategy is then used by considering the truss under two types of structural behaviors: • 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. • Once damaged regions are detected, the AS method is used as level-2 damage detection technique having truss members be potentially damaged.

28. Level 1 - Damage Detection Results Level 2 - Damage Detection Results for Elements 12 and 41 -5 x 10 -6 -5 x 10 x 10 6 2.5 1.2 5 2 1 4 0.8 1.5 ASH Flexibility Damage Indicator AS Flexibility Damage Indicator 3 AS Flexibility Damage Indicator 0.6 1 2 0.4 0.5 0.2 1 0 0 0 0 5 10 15 7 8 9 10 11 12 13 14 15 16 17 18 19 30 35 40 45 50 55 Truss Bay Number Truss Element Number Truss Element Number Experimental results 4TH BAY 11TH AND 12TH BAY

29. 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

30. 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