LEAKAGE DETECTION IN WATER PIPE NETWORKS USING

GROUND PENETRATING RADAR (GPR)

Professor: Sébastien LAMBOT Student: Dai SHI

19th June 2013

CONTEXT

MAJOR CURRENT LEAK DETECTION TECHNIQUES

OBJECTIVES

PROCESS STEPS

NUMERICAL SIMULATION

LAB EXPERIMENT

FIELD APPLICATION

CONCLUSION AND PERSPECTIVE

WHY ARE WE INTERESTED IN WATER LEAKAGE?

In developed countries 20-30% leakage of total

water produced

In Wallonia, ~15-20% of unsold water is due to

leakage

In developing countries, the leakage represents

> 50%

Economic reasons

Public health safety

Natural resource conservation

Can be reduced via adequate detection techniques

Water Supply Compagny of Nanjing, China

LEAK DETECTION PROCEDURE

SECTORIZATION

ACCURATE LOCALIZATION

1. WATER AUDIT

2. SECTORIAL LEAK DETECTION

3. ACCURATE LOCALIZATION & REPAIR

REPAIR

MINIMUM NIGHT FLOW

MONITORINGANOMALY DETECTION

WATER PIPE

NETWORK

MAPPING

FLOW MEASUREMENTS DATA TRANSFER

CONTEXT

MAJOR CURRENT LEAK DETECTION TECHNIQUES

OBJECTIVES

PROCESS STEPS

NUMERICAL SIMULATION

LAB EXPERIMENT

FIELD APPLICATION

CONCLUSION AND PERSPECTIVE

MAJOR CURRENT LEAK DETECTION TECHNIQUES

Leak noise correlatorAcoustic

Other techniques

Gas tracer

Thermography

Smartball

Listening rod Ground microphone

Sewerin, Gütersloch,

Germany

Methods Pipe materials

Asbestos

cement

Metal

(Iron & Steel)Plastic

Listening devices ± ✓ ±

Leak noise

correlator

X ✓ ±

Gaz tracer ± ✓ ✓

Thermography X ✓ X

ADEQUACY OF LEAK DETECTION METHODS FOR

VARIOUS TYPES OF PIPES

GROUND PENETRATING RADAR (GPR)

Principle

Emits electromagnetic microwaves and records the reflected signal from subsurface

Advantages

✓ Nondestructive method

✓ High resolution images of subsurface

✓ Independent of the pipeline material

✓ Can detect objects, changes in material and voids

✓ Good penetration depth

Major limitation

X Data processing and interpretation

X Signal attenuation with conductive soil

SPECIFIC OBJECTIVES

1. 2D Numerical simulation and analysis

Simulate different key parameters

Evaluate the sensitivity of GPR to different parameters

Obtain a first visualization of a leak’s radar signature

2. Laboratory testing

Measure an artificial leaky pipeline with GPR in sand

Analyze 2D and 3D images

3. Field application

Measure a pre-located leak with GPR in an urban area

Discuss the applicability and limitations of GPRComplexity

GENERAL OBJECTIVE

Assess the limitations of GPR for leak detection and test deployment routines

CONTEXT

MAJOR CURRENT LEAK DETECTION TECHNIQUES

OBJECTIVES

PROCESS STEPS

NUMERICAL SIMULATION

LAB EXPERIMENT

FIELD APPLICATION

CONCLUSION AND PERSPECTIVE

Hydrus 2DWater content

Θ (x, z, t)

GprMAX 2D

Input

Domain geometry

Flow and transport parameters

(e.g. main processes, time information)

Domain properties(e.g. soil texture )

Initial conditions(e.g. water content)

Boundary conditions(e.g. flow type)

Output

Input

Permittivityε (x, z, t)

Conductivityσ (x, z, t)

Output

GPR signal reflection data Model geometry

Model design

Antenna frequency 400 [MHz]

Water content

Soil moisture (t0 : dry)

Parameters

Simulation time : 1 day and 1 week

Leak type : TOP

1.2 m

Simulation domain: 6 m x 4 m

Pipe diameter : 0.09 m

Soil type: sand

Pipe type: PVC

Pipe position: x = 3 m, 1.2 depth

1.2 m

Surface

reflectionPipe reflection

GPR reflection

TOP leak after 1 day

INITIAL SITUATION

Configuration 1

Surface

reflectionPipe reflection

Pipe reflection

TOP leak after 1 week

INITIAL SITUATION

Configuration 1 (Field)

Surface

reflectionPipe reflection

Pipe reflection

Attenuation of reflection

SYNTHESIS

Water content has the most impact on the reflected signal

and significantly influences the detection performance

400 MHz antenna is a good trade-off between resolution

and penetration depth to detect a pipe with 0.09 m outer

diameter at depth of 1.2 m

It is difficult to determine visually the type and extent of a

leak from the reflected signal

CONTEXT

MAJOR CURRENT LEAK DETECTION TECHNIQUES

OBJECTIVES

PROCESS STEPS

NUMERICAL SIMULATION

LAB EXPERIMENT

FIELD APPLICATION

CONCLUSION AND PERSPECTIVE

Vivaldi antenna

OPERATION STEPS

MATERIALS

LABORATORY EXPERIMENT : 2D IMAGE ACQUISITION

Hole location

1.5 m

1 m

Scan area

Scan directions

Scan during the leak (2 hours)

Number of scans: 38

Far field (i.e., 25 cm above surface) & Near field

1.5 m

0.2 m

Copper area

1 m

19

Surface reflection

Pipe

line

Longitudinal section (Far field)

Copper reflection

Surface reflection

Pipe hyperbola

Transversal section (Far field)

Copper reflection

LABORATORY EXPERIMENT : 2D IMAGE ACQUISITION

Hole location

1.5 m

1 m

Scan area

Scan directions

Scan during the leak (2 hours)

Number of scans: 38

Far field (i.e., 25 cm above surface) & Near field

1.5 m

0.2 m

Copper area

1 m

21

Surface reflection

Pipe line

Longitudinal section (Far field)

Copper reflection

LABORATORY EXPERIMENT : 3D IMAGE ACQUISITION

Scan before and after leak

Number of scans: 101

Far field (i.e., 25 cm above surface)

1.5 m

1 m

1.5 m

1 m

Transversal scan area

0.2 m

Scan direction

Copper area

3D IMAGE OF INITIAL DRY CONDITIONS

Surface reflection

Pipe reflection

Copper reflection

3D IMAGE OF POST-LEAK CONDITIONS

Surface reflection

Pipe reflection

Copper plate reflectionAttenuation

Leak location

SYNTHESIS

Soil homogeneity and a priori knowledge about the leak

configuration ease the image interpretation step

Different reflections (e.g., surface, pipe and copper plate) are

identifiable in dry conditions

An interruption of pipe and copper plate reflection continuity in the

2D longitudinal image (after 20 minutes) and the 3D image (after

leak) due to the leak was observed

CONTEXT

MAJOR CURRENT LEAK DETECTION TECHNIQUES

OBJECTIVES

PROCESS STEPS

NUMERICAL SIMULATION

LAB EXPERIMENT

FIELD APPLICATION

CONCLUSION AND PERSPECTIVE

STUDY AREA

DESCRIPTION

Water supply

system

Drain system

Drain system

SCAN STEPS

y

x 33 X

21 X

0,0

RESULTS

Water supply pipe

reflection (?)

Transition between 2 media

reflection (?)

No Name Detection

1 Leak area ✓

(metal plate only)

2 Manhole ✓

3 Floor drain ✓

4 Floor drain

connection

X

5 Water supply pipe Maybe

(to be verified)

6 Water connection X

7 Sewer X (?)

8 Sewer connection X

9 Sewer gallery X

DETECTABILITY OF VARIOUS COMPONENTS

SYNTHESIS

The metal objects (e.g., manhole, floor drain and metal valve cover) on the road

were easily detected since the metal is a perfect reflector

The water supply pipe was not detected in a continuous manner, its reflection is

supposed to be observed in 3 transversal scans in hyperbolic shape, despite the

fact that the pipe is made of cast iron

Since the leak area had been reworked, it is difficult to identify 2 unexpected

reflections

The leak was not directly detected

CONCLUSION

Numerical analysis

The water content is the most limiting factor for detection

A plastic pipe of 0.09 m outer diameter at 1.2 m of depth is detectable

in leak conditions

It is difficult to classify the type of the leak

It is possible to determine the signal attenuation by observing the

whole sequence of images (dry to saturated soil)

Laboratory testing

Leak can be detected by observing the discontinuity of the pipe and

the copper plate reflections in longitudinal scans of 2D images and in

3D images (dry to wet soil)

Field validation

Difficulty to detect pipes and no insight regarding the leak

PERSPECTIVES

Smart water monitoring with

Online database of the measurement conditions (e.g., weather

conditions, pipe characteristics, GPR antenna frequency used,

etc.)

Exchange information between GPR operators & SWDE (e.g.,

leak location and area, pipe configuration, soil type, feed back of

measurements results, etc.) → ensure a gain in time to identify

leak conditions suitable for GPR detection

Improved protocol with

Standardization between numerical simulations and the lab

experiment

Development of image processing to classify radar images

Filtering of near field antenna effects, including antenna medium

coupling, for improved subsurface imaging

EXAMPLE OF ANTENNA FILTERING

Far-field initial image Enhanced image

Antenna filtering

Range gain

Special thanks go to the Water Supply Company of Wallonia (SWDE)

Persons I would like to THANK

Prof. Sébastien LAMBOT

Prof. Alain HOLEYMAN

GPR ASSISTANCE

Mohamed MAHMOUDZADEH

Laurence MERTENS

Albéric DE COSTER

HYDRUS-2D ASSISTANCE

Félicien MEUNIER

EQUIPMENT ASSISTANCE

Frédéric LAURENT & Sébastien FRANCOIS

ACKNOWLEDGEMENTS