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作 者:Wei HU Xiang SHU Baoshan HUANG Mark WOODS
机构地区:[1]Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville TN 37996, USA [2]Division of Materials and Tests, Tennessee Department of Transportation, 6601 Centennial Blvd., Nashville TN 37243, USA
出 处:《Frontiers of Structural and Civil Engineering》2017年第1期47-55,共9页结构与土木工程前沿(英文版)
摘 要:Intelligent compaction (IC) is a relatively new technology for asphalt paving industry. The present study evaluated the effectiveness and potential issues of the IC technology for flexible pavement resurfacing construction using two field projects. In the first project, a geostatistical semivariogram model was established and the parameters derived from it were compared with univariate statistical parameters for the Compaction Meter Value (CMV) data. Further analyses illustrated the effect of temperature on the CMV value and compaction uniformity. In the second project, a multivariate analysis was performed between in situ tests and IC data. The possibility of combining various IC data to predict the asphalt layer density and improve the current quality control and assurance system was discussed.Intelligent compaction (IC) is a relatively new technology for asphalt paving industry. The present study evaluated the effectiveness and potential issues of the IC technology for flexible pavement resurfacing construction using two field projects. In the first project, a geostatistical semivariogram model was established and the parameters derived from it were compared with univariate statistical parameters for the Compaction Meter Value (CMV) data. Further analyses illustrated the effect of temperature on the CMV value and compaction uniformity. In the second project, a multivariate analysis was performed between in situ tests and IC data. The possibility of combining various IC data to predict the asphalt layer density and improve the current quality control and assurance system was discussed.
关 键 词:intelligent compaction compaction meter value (CMV) SEMIVARIOGRAM multivariate analysis
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