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Developing the autom atic m easurem ent of surface condition on - - PowerPoint PPT Presentation

Aug 11, 2023 236 likes •545 views

Developing the autom atic m easurem ent of surface condition on local roads Alex W right Alex W right TRL I nfrastructure Division TRL I nfrastructure Division Group m anager, Technology Technology Developm ent Developm

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SLIDE 1

Portorož, Slovenia

  • Alex W right

Alex W right

  • TRL I nfrastructure Division

TRL I nfrastructure Division

  • Group m anager,

Group m anager, Technology Technology Developm ent Developm ent

  • m w right@trl.co.uk

m w right@trl.co.uk

Developing the autom atic m easurem ent of surface condition on local roads

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SLIDE 2

Portorož, Slovenia

Measuring condition at traffic-speed in the UK

  • UK condition surveys measure
  • Longitudinal profile
  • Transverse profile
  • Texture profile
  • Cracking (automatic)
  • Geometry
  • Annual coverage
  • TRACS: 40,000km motorway and trunk

roads

  • SCANNER: 80,000km local road network
  • Surveys carried out to an end result

specification

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SLIDE 3

Portorož, Slovenia

“UK” System s

  • Accredited Systems:
  • Jacobs

– Ramboll RST26, RST27

  • WDM

– RAV1, RAV2, RAV3, RAV4

  • DCL

– Roadware ARAN1, ARAN2

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Portorož, Slovenia

UK trunk roads - TRACS

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Portorož, Slovenia

UK local roads ( rural) - SCANNER

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Portorož, Slovenia

UK local roads ( urban) - SCANNER

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Portorož, Slovenia

Use of the Data

  • Local use
  • Parameters reported over 10m lengths for local use
  • Network use
  • For trunk roads total length of poor values reported
  • Single HA performance indicator (PI)

10 20 30 40 50 60 70 80 90 A Road category Proportion (%) Red Amber Green

  • For local roads a Road Condition Index

(RCI) is produced every 10m

  • Reports “overall” condition score
  • Distribution of RCIs over the local

authority defines network condition (LA Indicator)

  • Potential use in allocation of funding

across authorities

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SLIDE 8

Portorož, Slovenia

  • Local roads differ from trunk roads
  • New methods required to maximise value of local road

data

  • Research to improve the use of the survey data
  • Measuring ride quality on local roads using shape data
  • Using texture to assess surface deterioration on local

roads

  • Measuring edge deterioration on local roads
  • Work concentrated on the use of shape data
  • Began with consultation to find out what users needed in

practice Enhancing the use of data from local roads

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Portorož, Slovenia

“Shape” data collected at traffic-speed

65 66 67 68 69 70 71 72 73 74 75

Chainage (m)

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Portorož, Slovenia

  • Consultation with engineers found that
  • Little importance placed on longitudinal profile data
  • Key structural measure is cracking and rutting
  • Engineers desire a reliable assessment of general ride

quality (functionality)

  • But engineers key concern is defects giving rise to

bumps (user complaints)

  • Concluded that methods needed to
  • Reliably identify lengths with poor ride quality
  • Identify general locations giving rise to bumps

Measuring ride quality on local roads - consultation

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Portorož, Slovenia

Measuring ride quality - data collection

  • A practical investigation to relate surface profile to user opinions on

local roads

  • Several routes surveyed, including sections known to be poor
  • Profile data provided by HARRIS1 profilometer
  • Measurements in both wheel tracks (and across survey width)
  • User surveys:
  • Car surveys
  • Motorbike survey
  • Utilising on-board data collection

with GPS referencing

  • Reported on ride and bumps
  • Repeat surveys for consistency
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Portorož, Slovenia

Considering general ride quality

3m 5m 0.000000001 0.000001 0.001 1 1000 1.00E-01 1.00E+00 1.00E+01 1.00E+02 Wavelength Power

100m lengths where dial >2 100m lengths where dial <=2

0.375916 0.495487 0.653091 0.860827 1.134638 1.495543 1.971245 2.598258 3.42471 4.514041 5.949865 7.842396 10.336902 42400 42410 42420 42430 42440 42450 42460 42470 42480 42490 42500 42510 42520 42530 42540 42550 42560 42570 42580 42590 42600 42610 42620 42630 42640 42650 Wavelength (m) Site Chainage (m)

1.38-1.4 1.35-1.38 1.33-1.35 1.3-1.33 1.28-1.3 1.25-1.28 1.23-1.25 1.2-1.23 1.18-1.2 1.15-1.18 1.13-1.15 1.1-1.13 1.08-1.1 1.05-1.08 1.03-1.05 1-1.03 0.98-1 0.95-0.98 0.93-0.95

  • Wavelet Decomposition
  • PSD

1m – 5m

  • IRI, Ride Number, Profile Index
  • MA and enhanced variance
  • Coefficient de planeite
  • Waveband Energy
  • Standard Deviation
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Portorož, Slovenia

General ride quality - w avelength response

  • I RI
  • 3 m Variance
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Portorož, Slovenia

Param eter for general ride quality

  • Predicting general ride quality on local roads
  • 1-5m wavelength features cause the users most

discomfort.

  • 3m enhanced variance agreed best with user opinion of

underlying ride quality. Other measurements agreed no better with the user’s opinion.

  • 10m enhanced variance showed some agreement (effects
  • f longer wavelengths on truck drivers).
  • Wavelengths over 20m - little or no agreement with user
  • Effect of measurement (line)
  • Offside measurements contributed to 33% of agreement

with user opinion.

  • Multiple measurement lines around the wheelpath did not

improve agreement

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Portorož, Slovenia

Measuring “Bum ps” on local roads

  • User surveys recorded bumps using button presses
  • Wavelet analysis suggested wavelengths of interest lie

between 1 and 3m.

  • Existing measurements (variance, IRI etc) did not reliably

report the locations of the features causing this bump- like discomfort.

0.5m 2.5m

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 5 10 15 20 25 30 35 40 45 Wavelength (m) Normalised Power

Button press No button press

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Portorož, Slovenia

Measuring “Bum ps” on local roads

1 2 3 4 5 6 7 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000

C h a in ag e (m )

Dial value

C at4 3m enh var NS W T C at 4 3m enh var O S W T Dial value B um p

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Portorož, Slovenia

A param eter for “Bum ps” on local roads

  • Considered many approaches, e.g.
  • 1.25m enhanced variance, change of vehicle acceleration,

derivative of longitudinal profile (features too small to impact on a car’s tyre)

  • The Central Difference Method
  • Calculates a “derivative” for each point along the road (profile

measurements {yi}, taken at distances {xi} along the road):

  • Similarly for F’’.
  • The maximum of these values is calculated over 1m lengths.
  • If max(F’) and max(F’’) both exceed set thresholds, then the

length contains a bump and a value of “1” is reported for that

  • length. Otherwise “0” is reported.

1 1 1 1

) ( '

− + − +

− −

=

i i i i

x x y y i

x F

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Portorož, Slovenia

Measuring “Bum ps” w ith the CDM – local roads

  • Tests to review locations where the bump measure responded
  • Reported 84% of user button presses.
  • Potential high number of false positives.
  • Inspection of 3D profile and video showed features of note

where CDM responds, but users had not always pressed the button.

  • Concluded
  • This is an appropriate method for identifying “bumps”.
  • We should use a combination of this and 3m enhanced

variance for assessing general ride and bump density on local roads

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Portorož, Slovenia

Testing on trunk roads

Easting and Northing

152500 162500 172500 182500 465000 470000 475000 480000 485000 490000 495000 500000 505000 510000 515000 Easting Northing

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Portorož, Slovenia

Measuring “Bum ps” – trunk roads

  • Applied to whole of trunk road and motorway network.
  • 0.17% of network reported to contain bumps
  • Subset inspected in closer detail:
  • Inspected 3D profile for 10% of locations
  • Visual inspection on site of 1% of locations
  • Where 3D profile inspected:
  • 87% contained obvious bumps
  • Further 10% showed general unevenness
  • Where site inspected,
  • 64% showed visible bumps on site
  • 24% were not “bumps”, but were poor bridge joints
  • 3% were bumps at surface change
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Portorož, Slovenia

Measuring Edge deterioration - consultation

  • Consultation with engineers found that
  • Edge deterioration universally considered an area for

concern

  • Key requirement for a measure to aid in defining

maintenance treatment

  • Features of interest
  • Potholes in surface near edge
  • Overriding
  • Cracking of surface near edge
  • Edge supported or kerbed
  • Presence of patching
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SLIDE 22

Portorož, Slovenia

Developing param eters for Edge Deterioration

  • A fully automated measure
  • Utilising transverse profile

data

  • Firstly Identify the edge

strip

  • Edge Roughness
  • Roughness within the

edge strip

  • Edge Stepping
  • Stepping at the

nearside of the edge strip

  • Transverse Variance
  • Assessing roughness

across the pavement

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Portorož, Slovenia

Edge deterioration param eters

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SLIDE 24

Portorož, Slovenia

The Edge deterioration param eters

  • Transverse

edge roughness edge step unevenness

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Portorož, Slovenia

Testing the Edge deterioration param eters

5 10 15 20 25 30 20 40 60 80 100 C h a in a g e , k m Number of 10m lengths in each 1km exceeding 95th percentile level 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 CVI Edge Deterioration, average severity over 1km

E dge S tep L2 E dge S tep L1 C V I-B _E D

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Portorož, Slovenia

An I ndicator for Edge Condition

  • Four parameters provide a complicated picture of condition
  • Better to report the general edge condition
  • The ‘Edge Deterioration’ indicator
  • Combines all four SCANNER Initial Edge Deterioration

Parameters

  • Is a weighted combination of parameters after applying

thresholds and normalisation

  • Provides a single number to the engineer
  • Is based on the logic of the SCANNER RCI

Edge Det = Wr yedge roughness + Wtv ytrans variance + WE1 yedge step 1 + WE2 yedge step 2

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SLIDE 27

Portorož, Slovenia

Testing the indicator for Edge Condition

  • Comparison with site assessments

1 2 20 40 60 80 100 Chainage (km) E dge D eterioration Indic ator 1 C V I edge deterioration (y /n?) Edge deterioration seen on video Edge Deterioration Indicator CVI Edge

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SLIDE 28

Portorož, Slovenia

Testing the indicator for Edge Condition

  • Proportion of roads having significant edge deterioration by

manual surveys and the Edge Deterioration Indicator

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 A B C Unclassified

Edge Deterioration % >50

2 4 6 8 10 12 14 16

CVI % with non-zero severity

Edge Condition indicator CVI

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Portorož, Slovenia

Conclusions

  • Traffic-speed surveys have become widely applied in the UK on

local roads under SCANNER (>100,000km/year)

  • Local roads have particular defects
  • A research programme has developed a set of parameters for

reporting local road condition using data collected at traffic-speed

  • For ride quality
  • Enhanced variance
  • A bump measure
  • For edge deterioration
  • A set of edge deterioration parameters
  • An edge condition indicator
  • These new parameters were introduced into SCANNER in 2007

for network level reporting