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    Recommendations on Intelligent Compaction Parameters for Asphalt Resurfacing Quality Evaluation

    Source: Journal of Construction Engineering and Management:;2017:;Volume ( 143 ):;issue: 009
    Author:
    Wei Hu
    ,
    Xiang Shu
    ,
    Xiaoyang Jia
    ,
    Baoshan Huang
    DOI: 10.1061/(ASCE)CO.1943-7862.0001361
    Publisher: American Society of Civil Engineers
    Abstract: Intelligent compaction (IC) technology provides an innovative way for continuously and instantaneously evaluating asphalt compaction quality. However, there is a lack of well-accepted standard as to what IC parameters should be used and how they should be interpreted during the compaction. In this study, by analyzing IC parameters in six asphalt resurfacing projects in Tennessee, the feasibility of adopting IC parameters in IC specifications for the asphalt compaction quality was evaluated. Comprehensive laboratory tests were performed on the field cores, including bulk specific gravity, permeability, and indirect tensile (IDT) strength. Results indicated that there is linear relationship among the laboratory test results. Although there were no significant relationships between the final compaction meter value (CMV) and the core results, the difference in 1/CMV between two passes in one project showed significant correlations with the laboratory results. Other IC parameters, especially the starting surface temperature of the breakdown roller, also had a strong correlation with the core density. Based on the results from this study, it is recommended that three IC parameters—the difference in 1/CMV, the starting surface temperature of compaction (>110°C), and the total number of passes (>2 passes)—be adopted for evaluating the compaction quality of the resurfacing asphalt.
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      Recommendations on Intelligent Compaction Parameters for Asphalt Resurfacing Quality Evaluation

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4241166
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    contributor authorWei Hu
    contributor authorXiang Shu
    contributor authorXiaoyang Jia
    contributor authorBaoshan Huang
    date accessioned2017-12-16T09:18:15Z
    date available2017-12-16T09:18:15Z
    date issued2017
    identifier other%28ASCE%29CO.1943-7862.0001361.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4241166
    description abstractIntelligent compaction (IC) technology provides an innovative way for continuously and instantaneously evaluating asphalt compaction quality. However, there is a lack of well-accepted standard as to what IC parameters should be used and how they should be interpreted during the compaction. In this study, by analyzing IC parameters in six asphalt resurfacing projects in Tennessee, the feasibility of adopting IC parameters in IC specifications for the asphalt compaction quality was evaluated. Comprehensive laboratory tests were performed on the field cores, including bulk specific gravity, permeability, and indirect tensile (IDT) strength. Results indicated that there is linear relationship among the laboratory test results. Although there were no significant relationships between the final compaction meter value (CMV) and the core results, the difference in 1/CMV between two passes in one project showed significant correlations with the laboratory results. Other IC parameters, especially the starting surface temperature of the breakdown roller, also had a strong correlation with the core density. Based on the results from this study, it is recommended that three IC parameters—the difference in 1/CMV, the starting surface temperature of compaction (>110°C), and the total number of passes (>2 passes)—be adopted for evaluating the compaction quality of the resurfacing asphalt.
    publisherAmerican Society of Civil Engineers
    titleRecommendations on Intelligent Compaction Parameters for Asphalt Resurfacing Quality Evaluation
    typeJournal Paper
    journal volume143
    journal issue9
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0001361
    treeJournal of Construction Engineering and Management:;2017:;Volume ( 143 ):;issue: 009
    contenttypeFulltext
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