Public Abstract Structural Health-Monitoring System for Develop a - PowerPoint PPT Presentation

Integration of Self-Sustained Wireless Public Abstract Structural Health-Monitoring System for • Develop a self-sustained Integrated Structural Health Highway Bridges Monitoring (ISHM) system with remote sensing capability by • Holds promise of system scalability and autonomousness Chung C. Fu & Yunfeng Zhang (UMD) in remote monitoring large complex highway Fuh-Gwo Yuan (NCSU) and Ed Y. Zhou (URS) infrastructures. Sponsored by • Particularly suited for fatigue condition assessment of highway steel bridges USDOT/RITA • With a potential to extend to evaluate other types of To bridge damages, such as breaks and corrosion of steel The Transportation Research Board strands of pre-stressed concrete bridges. 91st Annual Meeting DISCLAIMER: The views, opinions, findings and conclusions reflected in this presentation are the responsibility of the authors January 22, 2012, Washington, D.C. only and do not represent the official policy or position of the USDOT/RITA, or any State or other entity Comparison of current state-of-art SHM technology and proposed ISHM system Comparison of current state-of-art SHM technology and proposed ISHM system Comparison of current state-of-art SHM technology and proposed ISHM system Comparison of current state-of-art SHM technology and proposed ISHM system Architecture of ISHM for Remote Sensing Merits of the ISHM System Impact to remote sensing Thrust 1 - (Sensor technology) Flexible piezo paint sensor practice dot array • Innovative, autonomous, Thrust 2 - (AE diagnostics) Passive interrogation of self-sustained, scalable evolving damage • Ready for field validation Thrust 3 - (Energy scavenging) Hybrid-mode energy • Improving current bridge scavenger inspection and monitoring practices Thrust 4 - (Wireless sensing) Wireless smart sensor Thrust 5 - (Prognostics) Prognostics using Bayesian updating and continuous remote sensing data

Distortion Induced Fatigue Project Planning and Preliminaries ∆σ is secondary stress due to LL induced local distortion. • Deliverables: Depending on detailing of primary /secondary connections. Table 1.1: Potential Failure Maps • Formed Technical Advisory (Global/local FE approach to identify hot ‐ Committee (TAC) and spots) conduct kick-off meeting. Global • Determined baseline field test procedure • Established and updating project web site Local • Conducted baseline field test and finite element Tension Tension Tension Tension Tension Tension analysis on pre-selected Potential Failure Map through FEM Analysis bridges Piezo Paint AE Sensor Deliverables Thrust 1: Piezo Paint AE sensor • Deliverables: Advantages: • Design, fabricate and characterize piezo paint AE • Tunable bandwidth sensor and measure the performance • Reconfigurable sensor dots Preamplifier & filter • Conformable to complex geometry or curved surface • Application to large area piezo. paint AE sensor • Low profile Piezo. Paint • Low cost Sensor Flexible piezoelectric paint sensor tested in the UMD laboratory

Piezoelectric Paint AE Sensor Fatigue test of Steel Orthotropic Deck with Broad Bandwidth Steel orthortropic • Piezo paint AE sensors have non-resonance characteristics in general. deck specimen under fatigue testing • All signals will be received with more or less equal sensitivity over a wide range of frequency. Piezo paint • High fidelity signal measurement because of its wideband feature enables AE sensor advanced waveform-based signal interpretation for structural damage AE signal detected detection when fatigue crack 90 90 Impedance Amplitude (kOhms) 1000 opened Impedance Amplitude (kOhms) Amplitude Amplitude Phase Phase 1000 45 45 Phase (deg) 100 Phase (deg) 0 0 100 10 -45 -45 10 -90 -90 0 200 400 600 800 1000 0 200 400 600 800 1000 Frequency (kHz) Frequency (kHz) Piezoelectric paint with 45% volume PZT patch with a 0.2-mm thickness fraction of PZT powder, 0.63-mm thickness Field Test of Piezo Paint AE Sensor on a Field Tests of Piezo Paint AE Sensor on Two Railway Bridge Steel Bridges in Korea Piezo Paint AE sensor Existing fatigue Voltage (V) crack AE signal collected by piezo paint AE sensor 0.2 Amplitude (Volt) 0.1 0 -0.1 -0.2 0 20 40 60 80 100 120 11 T ime (  s)

Thrust 2: Time-Reversal (T-R) Method Current AE Analysis (Signal-based) for AE Source Identification • Develop and evaluate TR method for continual passive damage interrogation Waveform based approach: Reconstructing t t • Crack location • Crack characterization Paint sensors Conventional signal-based Transient wave theory approach: AE initiation Refocus back to damage Need physics-based • Amplitude • AE parameter analysis approach! • Phase • AE activity analysis • Waveform • AE frequency analysis Time-reversal AE wave propagation T-R wave back-propagation Preliminary Verification of Impulse Preliminary Verification of T-R Method Characterization Actual location: (-76,-46) mm 1.6% error in 420  420mm plate Effect of number of sensors Effect of Noise Estimated location: (-80,-40) mm C. L. Chen and F. G. Yuan, “Impact Source Identification of Isotropic Plate Structures using C. L. Chen and F. G. Yuan, “Impact Source Identification of Isotropic Plate Structures using Time-Reversal Method: Theoretical Study” Smart Materials and Structures , Vol. 19, 105028, 2010. Time-Reversal Method: Theoretical Study,” Smart Materials and Structures , Vol. 19, 105028, 2010.

Thrust 3: Hybrid-mode energy scavenger Planning on Detecting Cracks & Thrust 4: Wireless smart sensor Central on Long-Span Bridge Station • Deliverables: 3G Network AE Sensors • Develop and experimentally evaluate wireless smart Base Station (mains powered) sensor and hybrid-mode energy harvester Large Rigid Solar Panels • Implement passive damage interrogation T-R algorithm in the wireless smart sensor on Wireless Fatigue Data link Cracks Mini. Wind Turbines Rotor Generator Sensor Attachments Resistive load Signal Paths Sensors A compact modularized high speed wireless sensor platform Miniature Wind Turbine system Tested and predicted output voltage developed by NCSU researchers. developed by NCSU to harvest wind versus resistive load energy Thrust 5: Prognostics using Bayesian Benefits and the Potential Impact Updating • Deliverables: • A cost-effective remote infrastructure sensing/monitoring • Integrate and validate AE sensors with system wireless smart sensor and hybrid-mode energy harvester • Expected to be commercialized and incorporated into the nation’s infrastructure system • Develop and conduct field implementation/validation of commercial- • Improved performance will benefit both the DOTs and ready ISHM system with remote sensing general public in ensuring the safety and lowering the capability maintenance costs • Recommend strategy to incorporate remote • Technology transfer and commercialization of the new 1.2 sensing and prognosis into BMS Prior σ msm =1X10 −4 technologies developed in this project. 1 σ msm =2X10 −4 0.8 Probability Density 0.6 0.4 0.2 0 0 0.005 0.01 0.015 0.02 0.025 0.03 0. Crack Length (m)

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