Multiscale Monitoring and Health Assessment for Effective Management of Flood-Control Infrastructure Systems

Transcription

1 Multiscale Monitoring and Health Assessment for Effective Management of Flood-Control Infrastructure Systems Tarek Abdoun Rensselaer Polytechnic Institute

2 Levees Everywhere

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4 Vision SAR Satellite 4

5 SAR Images InSAR Processing Levee Operators Owners Local Sensor Network isitecentral Feedback Regulators Health Assessment

6 TECHNOLOGY UTILIZED 6

7 Radar Satellites NIST-TIP Project ERS1 & ERS2 RADARSAT-1 ENVISAT Sentinel 1 Sentinel 2 RADARSAT-2 RCM Constellation ALOS-PALSAR SAOCOM TerraSAR-X Tandem-X CosmoSky-Med Constellation 7

8 StripMap Mode Global Monitoring: Satellite (InSAR) Spotlight Image Spotlight Mode Stripmap Image Rate of subsidence in New Orleans between April 2002 and July 2005 based on 33 Radarsat-1 images Dams Sector R&D Workshop 8

9 T-wall N London Ave Canal JAVAD GPS Receiver NovAtel GPS Receiver Extensometer SAAP / SAA EXT 8B I-wall Piezometer ADAS Enclosure Baffle Reflector EXT 8C Trihedral Reflector Canal Side 10 ft EXT 8D Land Side 60 ft 9 Figure Not to Scale!

10 Evaluation of Historic and Current Stripmap TerraSAR-X Data Comparison of Subsidence Rate Grass-Covered Levee PInSAR Distributed Scatterer 10

11 Evaluation of Historic and Current Stripmap TerraSAR-X Data Comparison of Subsidence Rate Grass-Covered Levee PInSAR Displacement-rate mm/year JSInSAR 11

12 GPS Settlement 2.7 in 12

13 UAVSAR NASA AIRBORNE L-BAND RADAR FOR DIFFERENTIAL INTERFEROMETRY The UAVSAR system combines control of the radar instrument with the aircraft flight path and is designed for repeat track interferometry. UAVSAR has several unique features to allow high precision differential interferometry from an airborne platform: Parameter Frequency Bandwidth Resolution Polarization ADC Waveform Antenna Aperture Value L-Band to MHz (23.8 cm wavelength) 80 MHz 0.8 m Azimuth, 1.67 m Range Full Quad-Polarization 12 bit ADC; 180 MHz sampling frequency Nominal Chirp/Arbitrary Waveform 0.5 m range/1.5 m azimuth (electrical) Azimuth Steering Greater than ±20 Transmit Power > 3.1 kw 13

14 Sherman Setback Levee Cumulative Displacement in the Line-of-sight Direction [cm]

15 Sherman Setback Time Series Cumulative Displacement in the cm

16 In-ground Deformation: 3D Shape-Acceleration Arrays (SAA) U B N U B N U Gravity U B N B N 16 16

17 SAA and SAAP Installation CPT Hole Drilled Hole All SAA casings and SAAPs were fully grouted in place Component Weight Ratio by Wt. Portland Cement 94 lbs (1 bag) Water 30 gals Bentonite 25 lbs (as required) 0.3

18 Full-Scale Levee Test River Witham, Boston, UK UrbanFlood

19 Boston, UK Site Value Added: Changing Tides 19

20 Google Earth Control Building at Grand Sluice 129 m from Control Building ~150 m 194 m from Control Building 277 m from Control Building River Witham

21 SAAPs Measurment Neap Tide Spring Tide SAAP at 11.2 m SAAP at 9.3 m

22 Section CC: Measured Deformation From Soil Data and Field Measurements Estimated slip surface near dense sand interface

23 Sensor Aided Health Assessment Spotlight mode InSAR measurements GPS measurements (higher sampling rate) 2D refined model of critical section

24 Health Assessment: Neural Network Approach Displacements from bank of training scenarios Monitored displacements Output No weak zone Weak zone in flood side Weak zone in protected side Neural Network

25 Performance in presence of uncertainty in geometry and properties of levees Displacements from bank of training scenarios Output Weak Zone Neural Network

26 Geo-Centrifuge Model Validation

27 Identification Results (Example) Scenario 1 Weak Zone Degradation of Equivalent Stiffness Weak Zone Area [m 2 ] Level of Degradation [m] Health Factor Identified % Identified % Identified % Actual Zone 89.9%

28 Sensor Aided Health Assessment Identification Algorithm Baseline Conditions Updated levee condition Insar measurements GPS measurements

29 Centrifuge Model and Validation 30g HF = g HF = g HF =

30 Local Identification u, p Control Motion Approach: uses all sensors Independent of the noninstrumented zone Interior node Boundary node

31 Local Identification of soil stress-strain state and properties Monitoring location Material Zone 1 Material Zone 2 Material Zone 3 Material Zone 4

32 Local Identification of soil stress-strain state and properties Material Zone 1 Material Zone 2 Material Zone 3 Material Zone 4

33 Thank You!

34 Question?

35 Detection Results: Impact of Sensor Spacing Output Category Monitoring Locations Configuration a) b) c) d) Protected Side Flood Side No Weak Zone (a) 9 locations 2m separation (c) 4 locations 4m separation (b) 4 locations 4m separation (d) 3 locations 6m separation

36 Neural Networks-Based Localization Neural Network training using computational simulations Multiple scenarios A total of 720 scenarios Modeled using Finite Elements Noise used to mimic real conditions (±2mm) Sample of 3 scenarios Weak zone

37 Local Identification of soil stressstrain state and properties CMP-Control Motion Approach: prescribed motion and pore pressure at all sensor node locations Sensor location boundary node Sensor location interior node

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39 IJKdijk Project Levee Monitoring Full-Scale Testing (The Netherlands) 39