| Project/Area Number |
17560413
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Civil engineering materials/Construction/Construction management
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| Research Institution | Tokyo Denki University |
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Principal Investigator |
MATSUI Kunihito Tokyo Denki University, School of Science and Engineering, Professor, 理工学部, 教授 (70112878)
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| Co-Investigator(Kenkyū-buntansha) |
岳本 秀人 (独)北海道開発土木研究所, 道路部, 維持管理研究室長
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2006: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2005: ¥2,800,000 (Direct Cost: ¥2,800,000)
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| Keywords | Asphalt Concrete Pavement / Temperature Prediction Model / Thermal Characteristics / Temperature Dependent Model / FWD test / Backcalculation |
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| Research Abstract |
It has been known that a modulus of asphalt concrete depends highly on its temperature. Civil Engineering Research Institute for Cold Region, Incorporated Administrative Agency Public Works Research Institute conducted pavement at the test sites in Wakkanai, Hokkaido in 2004 for the purpose of establishing pavement design code in the cold region. In June, August, November 2006, FWD tests were conducted and pavement temperature was also measured. We made use of these data. Backcalcalculat5ion was performed by both static and dynamic methods to estimate layer moduli. From both backcalculated Layer moduli and there temperature, a temperature dependent model of asphalt concrete layer was established. We also measured layer thickness by GPR test, we confirmed that GPR could identify thickness of asphalt concrete layer.
Since asphalt concrete modulus is sensitive to its temperature, it is important to predict pavement temperature without measuring it directly. Temperature in pavement is influenced by meteorological change and thermal properties of pavement layers. Last year we developed a method to estimate thermal properties such as heat capacity and thermal conductivity from measured temperature data. In order to predict pavement temperature, we found atmospheric radiation is as important as solar radiation. We developed the atmospheric radiation model using downward infrared radiation observed at our test site at Tokyo Denki University.
A common approach to estimate pavement layer moduli utilizes peak load and peak deflections from FWD data. Considering that FWD applies impulsive load to pavement surface, we have developed efficient dynamic backcalculation software called DBALM. DBALM can identify not only layer moduli but also the corresponding layer damping coefficients. In addition to layer modulus and damping coefficients, we have developed new algorithm which can identify a layer density. Reliability confrimation of the algorithm is in progress.
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