1. Structural health monitoring for Water Resources Infrastructure
Smart Rivers 2017
Pittsburgh, PA
Matthew D. Smith, PhD, PE
Civil Works R&D Lead – SHM
Research Civil Engineer
USACE – ERDC/CHL
19 September 2017
Now
Time to Fail
TIME
PERF. METRIC
Unaccept.
Acceptable
Fail
Uncertainty
Today we are going to talk to you about Structural Health Monitoring and how it can help provide decision support information from observation data for the full water resources infrastructure
After that I’ll show you some of the technology that we are working on in various phases at ERDC that address specific needs identified by our USACE Districts

2. USACE Infrastructure $250B infrastructure replacement value
12,000+ miles of navigable inland waterways
926 commercial harbors
191 locks
353 hydroelectric power generation units
694 dams
14,700 miles of levees
Over 800 bridges
Buildings, roads, recreation sites, environmental projects, etc…
We have a lot of things to manage. We plan, design, build, own, operate and maintain a large degrading inventory amidst serious fiscal constraints. In order to provide the maximum value returned to taxpayers, USACE must strategically manage this inventory and that requires a consistent, defensible, and reasonable asset management program to inform investment decisions

3. What is Structural Health Monitoring?
More than sensors
Structural Health Monitoring (SHM) – Science of making accurate condition assessments about the current and future ability of a structural component or system to perform its intended design function(s), based on:
Sensor/inspection data
Multi-physics (structural/thermal/hydraulic) models
Statistical models
SHM typically thought of as a lot of sensors that produce a lot of data
We have a broader definition that provides a foundation for all of our work in this field
Combine data from inspections and sensors, with numerical models and statistics to accurately assess the current condition and predict future performance

4. SHM Science that informs decisions
What is Structural Health Monitoring?
SHM Science that informs decisions
Minimize unscheduled down time
Detect conditions leading to catastrophic failures
On-demand post-event (crash, blast, quake) structural evaluation
Remaining life prognosis
Foundation for evaluating effects of alternatives
Repair 1 ($A) – extends life x
Repair 2 ($B) – extends life y
Reduce total cost of ownership
Understanding future performance is important for making decisions
The information extracted from SHM can be used for improving performance, reducing costs, scheduling maintenance/rehab, evaluating risks
All of these support the overall goal of reducing total cost of ownership of the infrastructure over the lifecycle

5. ENGINEERING/STATISTICSLIFECYCLE MODEL
What is Structural Health Monitoring?
SHM
PERFORMANCE METRICS
(Mission Objectives)
Navigation Tonnage
Life/Property Protected
Nonauth. Benefits
Rec. Visitation
Power Produced
Wildlife/Environmental
ENGINEERING/STATISTICSLIFECYCLE MODEL
Failure Modes
Corrosion
ASR
Cracking
Accidents
Natural Hazards
Levee Seepage
Loadings/Usage/Environ.
Uncertainties
PERIODIC INSPECTIONS
Visual
NDT
FEM
MONITORING SYSTEMS
Sensors
Cameras
Now
Time to Fail
TIME
PERF. METRIC
Unaccept.
Acceptable
Fail
Uncertainty
Conventional Design
vs.
New Innovative Design
Major Rehab. vs.
Operate to Failure vs.
Shut Down
Example Decision Alternatives 1
Example Decision Alternatives 2
Our SHM framework encompasses all the things inside this dashed box.
We have lots of data from inspections, testing, FEM, and sensors.
The USACE collectively has a great amount of understanding of our systems and how they perform and may fail
SHM pulls it all together bringing high-end science that analyzes our data to produce information for decisions
Aligning with Asset Management, much of that information will be in the form of performance metrics that describe how successfully we are and will be meeting our missions
SHM can help us accurately compute where an infrastructure system is in its lifecycle and precisely estimate how long the system can continue to perform at a satisfactory level
Further, we will be able to evaluate action alternatives, such as new innovative designs and materials, to predict their effects on the lifecycle.
These failure modes listed are examples of the physical situations we are including in our predictive models. Some of these are new in our consideration and haven’t been fully developed for application, such as corrosion, whereas other applications are being developed now, such as accidents at nav locks
We understand that it is infeasible to sensor every piece of infrastructure. Our goal is to combine inspection data with carefully selected sensor data to improve our assessments. Our long-term challenge is to use sensing systems on a limited number of structures to help develop new inspection procedures and NDT methods which directly result in performance metric computations. Sensing systems can then be deployed, as needed, on the most critical infrastructure.
Of course, these are our goals. We are developing new capabilities incrementally in order to get there.
PREDICTING FUTURE PERFORMANCE
Used by A.M. (Budget Priority)
Used by Ops (Maint. Planning)
Used by E&C (Design Alternatives)
Used by R&D (Identify Gaps)

6. Smart gate
Graphical Sensor Data

7. SMART GATe Sensors at Lock High-Fidelity FEM Surrogate Model
Validation Data
Decision Support
-Warning Lights
-Data Archival/Retrieval
- /Text Alerting
Reachback Capability
Detection Targets
(Verified with Field Needs)
Barge Impact
Quoin/Wall Degradation
Dragging Debris
Moving from the many-sensor data-heavy early SMART Gate systems, to fewer sensors with clear purposes, we assembled a PDT of field lock experts to find out what operational events warranted detection
They identified Barge Impacts, structural degradation of gate quoins, and dragging debris at locks as our first targets for the new automated SHM systems
These systems utilize fewer sensors, advanced numerical models, and statistics to produce simple to understand signals for Lock Operator decisions

8. Barge impact into lock gate
State of the Technology & Current R&D
Barge impact into lock gate
Single accelerometer at each leaf
Autoregressive Time Series
Statistical Process Control
Field data for V&V
BARGE TOW
LOCK
Gate – In Recess (Open)
River Flow
For instance, we are using a single accelerometer on a lock gate along with statistical process control to identify when barges have mild to moderate collisions with recessed lock gates. These frequent collisions can cause damage to accumulate over time. This system, while avoiding false positives, can help to identify which vessels have impacted the gates and record the event. We measured real barge impacts at Lock 27 in MVS to prove concept and are reviewing data from Racine now for this capability.

9. Trapped debris in lock gate
State of the Technology & Current R&D
Trapped debris in lock gate
Forces in anchors and struts indicate dragging
Scale Model V&V
Clean time series and GP-model
Debris time series
Often, debris will become trapped around gates or at the bottom of the lock and cause the gate mechanical systems to work harder than necessary. If the impediment is major enough, the mechanical system will stop. But, moderate impediments will simply cause damaging torsion on the gates, increasing the stresses and contributing to fatigue cracking. We are using gages on the strut arms along with statistical regression methods to identify these situations. We have used laboratory models to verify these techniques and are reviewing field data now from Racine lock and dam for this capability.

10. Digital twin of lock gate
State of the Technology & Current R&D
Digital twin of lock gate
Model without gap k1 k2 k3 k4 k5 k6 k7 k8
Using Gaussian Regression to fit temperature and boundary conditions
High Fidelity FEM
Low k8
Contact coefficients
Elevation of Gate
Force on Pintle!!!
Model with gap
A major uncertainty in analyzing stresses in lock gates is the conditions of contact between the gate and the walls. Using a few strain gages, we are able to identify when contact is poor, which leads to overstressing the pintle and is believed to be the major source of cracks in miter gates. We are working with INDC to model this type of damage in a way that is representative for our full miter gate inventory and not simply one-offs.

11. State of the Technology & Current R&D
Hss crack prediction
Use models to predict crack formation and estimate severity and required time-to-action (remaining life)
Now
Time to Fail
# Lockages
Reliability Index
Unacceptable
Acceptable
Fail
Uncertainty
The next step after identifying when contact has been lost is to use that information to calibrate crack growth models. We are developing techniques, based on very complex existing design guidance, to predict crack formation, growth, and failure in HSS. With this tool, districts should be able to know, prior to dewatering, how many cracks to expect and their severity, reducing surprises. Also, if cracks are found, this tool should be able to assess whether they are critical and require immediate repair or if they are benign and can be left to a future repair schedule.
SHM-Based Prediction (underwater location)
Actual Cracking Found (Markland, not recent)
Based on ETL

12. Other work SHM incorporated in risk and reliability
SHM incorporated in maintenance scheduling
Thin film and flex sensors under paint
New validation data for turbulence models
Model updating / Inverse model problem
Incorporation of data into lifecycle models
Computer vision
Video and static images to produce and update models
Ridolfi, et al., “Accuracy of a dam model from drone surveys,” Sensors, mdpi, 2017, 17, 1777.

13. CONCLUSION
Structural Health Monitoring combines inspection and sensor data with numerical and statistical models to produce information for decisions
ERDC is working with HQ USACE to ensure that technology is being developed targeting business processes to maximize public net benefit
Our SHM framework provides information from
Inspection data
Sensing systems
Engineering models
Statistical prediction of performance

14. Questions?