4.1 Identification of Potential Water Ponding 4.2 Monitoring of Road Inventory 4.3 Monitoring of surface condition

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1 TRIMM is supported by funding from the 7 th Framework Programme Call: SST Theme: Advanced and cost effective road infrastructure construction, management and maintenance Alex Wright

2 4.1 Identification of Potential Water Ponding 4.2 Monitoring of Road Inventory 4.3 Monitoring of surface condition Fretting (Ravelling) TRL / Yotta Cracking IFSTTAR / Yotta 4.4 Monitoring of structural condition 4.5 Monitoring road functionality in real time with data collected from vehicles.

3 Surface condition key to Identifying Maintenence need Designing schemes Budget planning Challenging to measure at the network level Expanding use of traffic-speed techniques Concerns over performance and range of defects measured Focussed on major roads What about minor road networks? How could improvement and expansion be achieved?

4 Minor networks e.g.england English Strategic Road network ( highway ) Motorway road length 3,600km A road length 8,500km A 2,000km decrease since 2002 Represents 3% of network Principal Road network (non- highway ) A Road length 38,000km 10% of network Minor Roads: B, C, U Roads Length 342,000km 87% of network

5 On highways traffic-speed surveys beginning to replace slow speed walked surveys Devices typically utilise image collection and processing to identify cracking Most current surveys use 2D imaging Various levels of performance have been identified in studies TRIMM WP4.3 investigated the automatic identification of cracking Using 2D image collection/processing On a range of road types Using two types of algorithms System Origin Country Roadcrack CSIRO Australia ARAN Fugro USA (Roadware) DHDV Waylink USA PAVUE Ramboll Sweden HARRIS-2 TRL UK Aigle-RN CEREMA France Tempest 2 Yotta-DCL UK LRIS Pavemetrics Canada

6 Image surveys using Yotta Tempest vehicle A, B, C roads 400km surveyed, with 134km Pseudo- Ground-Truth (PGD) reference data Manual analysis using grid approach Is time consuming and a little subjective Enables quantitative comparison of automatic and manual data

7 HA a traditional method Clean the image Detect small objects distinct from the background using simple thresholding Clean and connect objects Filter and decide on final objects For TRIMM apply grid to quantify results Or apply expert interpretation

8 Is practical to apply over long lengths Data matches shape of reference, but disappointing performance in direct comparison Over sensitive, False Positives Using condition categories improves apparent performance Highlights need for customization Ranges of road texture Image unevenness But this should be achievable with further development

9 MPS Again relies on dark pixels in defects (assumes grey level and the path cost along cracks is lower than anywhere else) Pixels are classified as crack or background Potential crack end points are identified Minimal joining paths are determined These are filtered to identify the most likely joining paths The identified crack is grown to fill the crack width Much slower to process Many hours to process a few m But provides much more detail TRIMM unable to demonstrate on large C road network sites

10 Image collection surveys are practical on local roads Pseudo Ground Truth grid method robust but care needed Traditional image processing for cracking can segment poor from good lengths But detail is poor False detection high More computationally intensive techniques are better Currently impractical May be potential to combine techniques New 3D techniques could offer the information required to filter out false positives Automated crack detection can be practically achieved on Local Roads

11 An important an increasing defect Surveys carried out manually Difficult to identify, quantify, trend Quality and repeatability issues Progress made on highways to automate the process Netherlands UK Stoneway / 3D stoneway Highway Agency Fretting algorithm TRIMM investigated transfer of UK Highway Ravelling algorithm to Local Roads

12 Raw texture measued in multiple lines (4mm spacing) Filtered and processed to obtain RMST values

13 Compare distribution of RMST values over local 10m with surrounding 100m length Correlation coefficient Correlation between Nearside/Middle/Offside regions Comparison of percentiles The proportion of values that are locally high compared with the global region Then we further analyse Statistical parameters can describe the differences By applying thresholds to each and reporting a scaled value Is automatic and surface independent

14 Can Highways Ravelling detection be extended to local roads with: More patching, detritus debris, ironwork, road markings etc High frequency of surface changes Larger variety of surfacing materials Tests were carried out over A, B, C roads 130km Multiple line texture collected at traffic speed Collection of ground truth data was challenging Site surveys vs Forward video Defect not always clear

15 Algorithm directly applied to local road data to identify strengths and weaknesses Algorithm able to quantify ravelling on all types Problems dealing with roads containing long lengths of ravelling Core assumptions about Highways not valid on local roads Approaches applied to reduce problem based on global length Solutions not entirely successful Highlights a limitation of the approach But may not affect coverage?

16 Problems due to patches Many more on local roads Complex solutions considered but simplest approach simply uses thresholds Removes most of problem

17 Highway algorithm reports only overall ravelling Ravelling present in more positions across road Highlighted need for more information from the algorithm Developed a spatial analysis parameter to report transverse position

18 Having tuned algorithm can it perform as a network measure? Can the fretting algorithm be used to assess extent of fretting over all road types? Highway thresholds inappropriate Improved thresholds developed A B C

19 Ravelling measures successfully implemented on highways using lane width texture measurement These multiple line texture surveys are practical on local roads Local roads present more challenges to the ravelling algorithms Many of the issues can be overcome The approach can be successfully applied TRIMM has shown potential for their application as a network measure (e.g. overall extent of ravelling) on local roads These could be included in Asset Management tools for network condition monitoring There remain some obstacles to overcome to deliver improved performance at the project level There is always difficulty in obtaining reliable ground truth data