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  • 1. Alzubaidi, H.
    et al.
    Magnusson, Rolf
    Dalarna University, School of Technology and Business Studies, Road Technology.
    Statistical Analysis of Gravel Road Rating2002In: The international journal of pavement engineering, ISSN 1029-8436, E-ISSN 1477-268X, Vol. 3, no 1, p. 35-42Article in journal (Refereed)
  • 2.
    Edvardsson, Karin
    et al.
    Dalarna University, School of Technology and Business Studies, Road Technology. KTH.
    Gustafsson, Alf
    Magnusson, Rolf
    Dalarna University, School of Technology and Business Studies, Road Technology.
    Dust suppressants efficiency study: in situ measurements of dust generation on gravel roads2012In: The international journal of pavement engineering, ISSN 1029-8436, E-ISSN 1477-268X, Vol. 13, no 1, p. 11-31Article in journal (Refereed)
    Abstract [en]

    Dust suppressants were applied on fifteen 1 km long test sections at four sites in Sweden during three summer seasons with the objective to compare their relative efficiency and determine minimum application rates in Nordic climate. Dust generation from the test sections was measured both visually and by PM10 measurements. All products except lignosulphonate, sugar and bitumen emulsion showed acceptable efficiency. Chloride solutions were the most efficient. Results indicate the possibility to reduce application rates of chlorides by applying them as solutions instead of solids. The minimum application rate for a chloride solution was estimated at 0.8m(3)/km, which is equivalent to a rate reduction of 50% by weight compared with traditionally applied rates of solid chloride. The results are expected to reduce life cycle costs for gravel roads and contribute to environmental gains by reducing the release of dust into the atmosphere and chemicals into the environment.

  • 3. Offrell, P
    et al.
    Magnusson, Rolf
    Dalarna University, School of Technology and Business Studies, Road Technology.
    In Situ Photographic Survey of Crack Propagation in Flexible Pavements2004In: The international journal of pavement engineering, ISSN 1029-8436, E-ISSN 1477-268X, Vol. 5, no 2, p. 91-102Article in journal (Refereed)
  • 4.
    Svenson, Kristin
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    McRobbie, S.
    Alam, Moudud
    Dalarna University, School of Technology and Business Studies, Statistics.
    Detecting road pavement deterioration with finite mixture models2019In: The international journal of pavement engineering, ISSN 1029-8436, E-ISSN 1477-268X, Vol. 20, no 4, p. 458-465Article in journal (Refereed)
    Abstract [en]

    Budget restrictions often limit the number of possible maintenance activities in a road network each year. To effectively allocate resources, the rate of road pavement deterioration is of great importance. If two maintenance candidates have an equivalent condition, it is reasonable to maintain the segment with the highest deterioration rate first. To identify such segments, finite mixture models were applied to road condition data from a part of the M4 highway in England. Assuming that data originates from two different normal distributions – defined as a ‘change’ distribution and an ‘unchanged’ distribution – all road segments were classified into one of the groups. Comparisons with known measurement errors and maintenance records showed that segments in the unchanged group had a stationary road condition. Segments classified into the change group showed either a rapid deterioration, improvement in condition because of previous maintenance or unusual measurement errors. Together with additional information from maintenance records, finite mixture models can identify segments with the most rapid deterioration rate, and contribute to more efficient maintenance decisions.

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