The use of texture in the Swedish road management Thomas Lundberg Leif Sjögren Mika Gustafsson

Outlines The use and assessment of texture in Swedish road management Models for prognosis of macrotexture (MPD) Case study - Initial development of macrotexture on a new asphalt construction (Approval testing of maintenance objects)

Texture and usability The texture has a big influence on many functional characteristics such as Skid-resistance, Homogeneity, surface defects (fretting) Internal and external noise, Rolling resistance, Vehicle and tire wear, Drainage, Visibility and Reflectivity

The importance of texture

Classes of texture Texture can be divided into three main classes covering different wavelengths (varying roughness), Microtexture, Macrotexture Megatexture each having an influence on different user effects.

Routine measurement in Sweden Today texture can be measured in traffic speed. Macrotexture (Mean Profile Depth, MPD) and Megatexture (root mean square, RMS) are included among the indicators measured in Sweden The quality is controlled via comprehensive tests during procurement of the service (presented at RPUG 2009, Atlanta)

Lateral position for the texture measurements

Principles for calculating MPD

Positive and negative texture Surface dressing – Stone Mastic Asphalt

Positive and negative texture MPD and MPD D and combinations can be useful in future studies where texture is involved.

Categorizing positive and negative texture The quota of MPD and MPD D can be used to separate negative texture from positive.

Conclusions on the use of texture in Sweden Aware of the importance of texture Collect MPD and Megatexture data All paved state roads are measured at least every three year other less. Prognoses models are needed. The use of the data is poor Few effect models exists and even fewer are in use in the Swedish PMS

Prognosis model for MPD Purpose: Full coverage are needed when comparing road network conditions over years. Therefore not measured or missing data on sections need to be filled in. Short term prognosis 1-3 years

Prognosis model considerations GOAL: A model for short term prognosis (upto 3 years) of macrotexture levels to achieve a complete representation of the national road network.

Variable importance for SMA roads Effect Stone Mastic Asphalt Surface Dressing MPD level10 Seasonal variation Measurement bias Bias3.54 Aging Traffic flow polishing1.51 Width, Rut depth distance11.5 Max stone size0.5

Case study for a SMA-16 highway

Case study – E687 Sturefors-Linköping Purpose of the study  More knowledge of the initial wear of new asphalt pavements  How do studded tires affect the macrotexture?  Using macrotexture as a control parameter for approval of maintenance objects  Innovative new ways of using macrotexture data

Case study – E687 Sturefors-Linköping  New Construction  Length 3,5 km  SMA16  AADT 5150  Width 7,5 m  Lanewidth 3,5 m  Speedlimit 80 km/h  Opened for traffic

Case study – E687 Sturefors-Linköping Measured with the same vehicle and driver to assure a high quality data trend

PSD – first year of the road Wavelength [m] Spectral density [m 3 ] MPD Megatextur IRI

MPD changes during the first year Winter

Conclusions from Case Study  Large initial change of Megatexture and MPD, MPD was reduced 25 % the first month  After the winter the MPD is higher (due to studded tires)  Why this Large initial changes?  A clean non trafficked surface will be filled with dust.  The first traffic wear will initially affect the surface a great deal  Compaction of the pavement due to traffic load (due to entrepreneur performance and climate)