CRC-P – Smart Linings
for Pipe and Infrastructure
Project
24 March 2021
Session 3:
Water Code of Practice,
Sensors & Robotics
This presentation is for individual reference only, do not reproduce materials or publish without permission from WSAA.
CRC-P: Sub-Project 3
Smart Sensing and Application – Water Pipe Linings
Prof. Sarath KodagodaDirector (Acting) UTS Robotics Institute,
iPipes Lab,University of Technology Sydney Australia
24th March 2021
UTS Robotics Institute: water/wastewater
WSAA and partners
Robotic tools for water pipes
Robotic tools for water pipes
Robotic tools for wastewater pipes
• Non-traversable sewers 900mm-
1500mm
• Deployment through a 600mm
diameter manhole
• Expand to different pipe sizes
• Non-destructively assess the intact
concrete cover
• Online real colour three dimensional
view
• Data visualization to make online
decisions
Robotic tools for wastewater pipes
• 2020 AWA national winners, Research Innovation category
• 2020 AWA NSW winners, Research Innovation category
CRC-P Sub-project 3: Smart Sensing and Application
Water Pipe Lining Infrastructure
• Sub-project 3 focuses on the development of sensing tools and deployment strategies
Post-application quality assurance (PAQA)
Long-term performance monitoring (LTPM)
• PAQA: Sets a benchmark for the applicators to deliver the specified liners and enables the
utilities to be confident about the application of products.
• LTPM: Enable utilities to forecast timely repairs to their assets.
CRC-P Sub-project 3: Smart Sensing and Application
Milestones
1. Identification of product-specific defect parameters to be monitored in PAQA and LTPM.
2. Development of an easy to use real-time tool to non-destructively assess the applicationquality and performance of linings.
3. Development of sensor deployment strategies, signal transmission techniques and real-time asset management tools for liner monitoring.
4. Feasibility study on liner embedded sensors.
5. Validate the prototype sensors and robot through lab tests, test beds and field trials
Linings for Water Pipe Infrastructure
Lining Types Product and Manufacturer Test site
Cured In-place Pipe (CIPP) Lining
AquapipeFrom
Sanexen
Sydney Water site–Strathfield testbed at
Sydney
Polymeric Spray Lining
Subcote FLP from
Radius Subterra
Sydney Water site –Strathfield testbed at
Sydney
Identification of Product Specific Defect Parameters
Survey Study
• UTS conducted a survey study to identify the most important product specific defectsparameters to be monitored for PAQA and LTPM of water pipe linings.
• 21 members took part in the survey that includes water utilities, lining manufacturersand applicators, researchers and water associations.
Linings PAQA LTPM Sensor Technology
CIPPLiner
Imperfections**Liner
Imperfections**Laser scanning - Robotic
SprayLiner
Imperfections** & uneven thickness
Liner Imperfections** & uneven thickness
Laser scanning, Ultrasound-Robotic
**Liner imperfections: folds, wrinkles, dimples, bulges, sagging, liner peeling (de-bonding), tears, damages and blisters
Mini Pipeline Inspection Robot – (mini-PIRO)
Features: On-board Sensors
• CCTV – real-time monitoring
• Laser profiler – building 3D pipe map
• IR and RGB camera – to fuse real-time colour information in 3D pipe map
• Ultrasound sensor – uneven thickness monitoring of spray linings
• Wheel encoders – to know the distance travelled by the mini-PIRO mini-PIRO was developed under the Sydney Water funded project,
"Development of sensor suites and robotic deployment strategies for condition assessment of concrete sewer walls" is an in-kind contribution to this CRC-P.
Using Cameras and Laser Profiler to build 3D Pipeline Map
Laser profiling demo
Laboratory Testing: Liner Imperfections of Spray and CIPP Linings
Artifacts for Sensing Evaluation
• The purpose of these experiments was tovalidate the accuracy of 3D map measurements,RGB depth mapping, defects mapping, andorientation detection.
• Artifacts with known dimensions were attachedto the internal pipe surface to validate the sensormeasurements.
• Different color stripes (red, green, and blue)were placed on the pipe surface to validate thecolor alignment.
Laboratory Testing: Liner Imperfections of Spray and CIPP Linings
LocationPhysical
measurement(mm)
Point cloud measurement
(mm)
Error(mm)
Pipe diameter 445 445.36 0.36Point 1 13 13.56 0.56Point 2 6 6.05 0.05Point 3 1 1.68 0.68Point 4 14 31.15 17.15
Liner Imperfection: Defect Size Validations
Location(Fig. 6a)
Physical measurement – Vernier Caliper
(mm)
Point cloud measurement
(mm)
Error(mm)
Right Defect Height Length
110110
111.31109.72
1.312.78
Left Defect Height Length
110110
107.87115.16
2.135.16
Lab setup Unwrapped 3D point cloud
Laboratory Testing: Liner Imperfections of Spray and CIPP Linings
unwrapped point cloud with the defects heat map for the lab setup
Lab setup 3D point cloud
Laboratory Testing: Liner Imperfections of Spray and CIPP Linings
Further Lab Validations
• In addition to the previously mentioned labexperiments, we used a DN600 corroded metalpipe extracted from the Sydney Water networkto perform further tests.
• We scanned the pipe using a commerciallyavailable highly accurate (0.1mm accuracy) 3Dscanner "Creaform EXAscan SYS-H3D-EXAD".This 3D scan is benchmark.
• Mini-PRIO system scans were compared withthe benchmark. Results indicate qualitativelysimilar scan were produced by the mini-PIRO. Benchmark scans mini-PIRO scans
GUI for Evaluation of Liner Imperfections – Spray & CIPP Linings
Colour Image
Unwrapped pipe
view (colour map
represents areas
away from an
ideal cylinder)
Projected laser
light
Slider: Moves the
images and views
IR image
Actual laser light
Frames with pipe
distance
GUI for Evaluation of Liner Imperfections – Spray & CIPP Linings
Ultrasonic Uneven Thickness Sensing of Spray Linings
Operation
• Ultrasound sensors emit pulses in the form of ultrasound wave signals at a particularfrequency. With appropriate coupling, they can penetrate materials and bounce back.
• Spray lining thickness is dependent on the spray lining material propagation speed (𝑣) and thetime taken (τ) by the ultrasound wave signals to pass from the material’s surface to its otherboundary layer.
• With known spray lining material thickness (Mᴷ), the propagation speed (𝑣) is given by
𝑣 =2 x Mᴷ
τ
• Once the propagation speed (𝑣) of the spray lining material is determined, the unknown spraylining material thickness (Mᵁ)
Mᵁ = 𝑣 xτ
2
Ultrasonic Uneven Thickness Sensing of Spray Linings
Sensing System Electronics
• (a) Ultrasound pulser board, (b) Data acquisition unitand, (c) Ultrasound transducer.
• An ultrasound pulser board capable of generatinghigh-frequency ultrasound wave signals.
• Data acquisition board (with an ADC differential inputwith a resolution of 14 bits and a speed of 100 Msps),which are housed in the sensor electronics unit.
• Tests were conducted to identify the most appropriateultrasound frequency.
Ultrasonic Uneven Thickness Sensing of Spray Linings
Sensor Calibration
• Propagation speed of ultrasound wavesvaries depending on the material
• We have fabricated a test piece sample with athickness of 8 mm to determine propagationspeed for the spray lining material.
• The peak signal seen at 16.20 μs is from thetop surface whereas the peak at 23.64 μs isfrom the bottom surface of the test sample.
• The time difference in between the peaksignals is the signal travel 7.44 μs.
• Propagation speed is 2150 m/s
Ultrasonic Uneven Thickness Sensing of Spray Linings
Lab Testing Sample
• A laboratory test sample was made byapplying the spray lining material over a flatmetal sheet.
• This test piece has a varying thickness of spraylining across its area.
• In the centre of the test piece, a grid of 2 rowsand 24 columns were drawn.
Ultrasonic Uneven Thickness Sensing of Spray Linings
Lab Testing – Benchmark Measurements
• Scanned with a highly precise handheld laserscanning device (EXAscan 3D Scanner,Creaform) with a resolution of 0.05 mm beforeand after the spray lining was applied.
• The difference between the two 3D scansenabled us to construct a new 3D model thathas accurate thickness information on theapplied spray linings.
• New 3D model was used as a benchmark totest the thickness measurements of theultrasound sensor.
Validation: Uneven Thickness Sensing of Spray Linings
Sensing Locations Mean Absolute Error Root Mean Square ErrorRow 1 0.11 mm 0.34 mmRow 2 0.16 mm 0.40 mm
Overall 0.14 mm 0.37 mm
Acoustic Coupling Mechanism for Continuous Measurements
Operation
• Important for mini-PIRO toperform continuous sensing.
• Requires a built-in couplingsystem connected to theultrasound probe, which isrealised through a spring-loaded water spraying systemproviding continuous surfacewetting for the pipe liner wall.
• Ultrasound sensor was fitted ina watertight enclosure with anelastomer coupling pad on thesensor head.
Acoustic Coupling Mechanism for Continuous Measurements
Operation
• The coupling agent in this work is water, which isstored in a small reservoir fitted with a pump that iscontinuously in operation.
• Continuous acoustic coupling probe mechanism wastested inside the lab water pipe (PVC made).
• Acoustic coupling system consists of the waterreservoir, the water pump, and the tubes that supplywater.
• mini-PIRO traversed through the pipe whilecontinuously taking measurements without anydrop of signals reinforcing the effectiveness of thecoupling mechanism.
Data Visualization: Spray Lining Uneven Thickness
Overview
• A tablet computer is used in the remote station to visualise the ultrasound sensor readings in real-time as the mini-PIRO traverses inside the pipeline.
• The mini-PIRO transfers sensor measurements to the cable drum located in the pipe pit via a tether cable, from where the sensor data is transmitted to the tablet through WiFi signals.
• The tablet runs on a ROS (Robot Operating System) middleware and it is built with custom-developed programs for ultrasound sensor data visualisation.
GUI for Spray Lining Uneven Thickness Monitoring
mini-PIRO Loading Tests using Winch Mechanism at Lab
Field Deployment Mock Testing (February 2019)
• In the very first run in field, robot traversed 105 meters.
• Data collected in first 60m
Field Deployment and Evaluations – Spray Linings (May 2019)
Field Evaluation: Liner Imperfections – Spray Linings
Pipe Details
• Strathfield Testbed (Sydney Water) Cast iron cement lined pipe with 580mm internal diameter
Field Evaluation: Spray Linings Uneven Thickness
Ultrasound video
Field Evaluation: Spray Linings Uneven Thickness
Ultrasonic Robotic Sensing
• The mini-PIRO continuously took ultrasound measurements in the crown region (12 o’clock position) of the pipe.
• The thickness of the spray lining is about 3.7 mm from 1.8 m to 3.5 m and about 7 mm from 3.5 m to 36 m. This is inline with the spray liner applicator specification.
• The high frequency noise in the data is mainly attributed to the presence of minor ripples observed through CCTV.
Thank You Everyone for Listening!
QUESTIONS?
Contact details:
0409 333 540
Additional Information:
https://waterportal.com.au/smartlinings/