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    Load Transfer Restoration with Diamond Grinding on Rigid Pavements: Short-Term and Long-Term Effectiveness

    Source: Journal of Transportation Engineering, Part B: Pavements:;2018:;Volume ( 144 ):;issue: 001
    Author:
    Montgomery Sharlan R.;Labi Samuel;Haddock John E.
    DOI: 10.1061/JPEODX.0000020
    Publisher: American Society of Civil Engineers
    Abstract: Knowledge of pavement treatment effectiveness aids highway agencies in a variety of functions, including treatment comparisons, project prioritization, maintenance schedule development, and cost-effectiveness analyses. For a number of treatments that might potentially aid in these areas, there has been little research that empirically demonstrates their effectiveness. The research in this paper addresses this gap, presenting statistical models that demonstrate the short and long-term effectiveness of load transfer restoration and diamond grinding (LTR-DG) treatments. Specifically, nonlinear multivariate regression techniques were used with Washington State Department of Transportation LTR-DG data collected between 1993 and 26. With regard to short-term effectiveness, a performance jump model was developed that suggests LTR-DG treatments should be performed on pavements younger than 3 years to achieve the greatest benefit. With regard to long-term effectiveness, an annual performance loss model was developed that shows a slight increase in pavement condition for the first year or two following treatment, and a decrease thereafter. Included in this paper are charts for both models, as well as an example of how the models can be combined to estimate the extended service life associated with LTR-DG treatments.
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      Load Transfer Restoration with Diamond Grinding on Rigid Pavements: Short-Term and Long-Term Effectiveness

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    contributor authorMontgomery Sharlan R.;Labi Samuel;Haddock John E.
    date accessioned2019-02-26T07:54:26Z
    date available2019-02-26T07:54:26Z
    date issued2018
    identifier otherJPEODX.0000020.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4250199
    description abstractKnowledge of pavement treatment effectiveness aids highway agencies in a variety of functions, including treatment comparisons, project prioritization, maintenance schedule development, and cost-effectiveness analyses. For a number of treatments that might potentially aid in these areas, there has been little research that empirically demonstrates their effectiveness. The research in this paper addresses this gap, presenting statistical models that demonstrate the short and long-term effectiveness of load transfer restoration and diamond grinding (LTR-DG) treatments. Specifically, nonlinear multivariate regression techniques were used with Washington State Department of Transportation LTR-DG data collected between 1993 and 26. With regard to short-term effectiveness, a performance jump model was developed that suggests LTR-DG treatments should be performed on pavements younger than 3 years to achieve the greatest benefit. With regard to long-term effectiveness, an annual performance loss model was developed that shows a slight increase in pavement condition for the first year or two following treatment, and a decrease thereafter. Included in this paper are charts for both models, as well as an example of how the models can be combined to estimate the extended service life associated with LTR-DG treatments.
    publisherAmerican Society of Civil Engineers
    titleLoad Transfer Restoration with Diamond Grinding on Rigid Pavements: Short-Term and Long-Term Effectiveness
    typeJournal Paper
    journal volume144
    journal issue1
    journal titleJournal of Transportation Engineering, Part B: Pavements
    identifier doi10.1061/JPEODX.0000020
    page4017019
    treeJournal of Transportation Engineering, Part B: Pavements:;2018:;Volume ( 144 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian