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    Data-Mining Framework Integrating 3D Random Aggregate Method and Finite-Element Method for Mesoscopic Simulation of Asphalt Concrete

    Source: Journal of Transportation Engineering, Part B: Pavements:;2024:;Volume ( 150 ):;issue: 003::page 04024021-1
    Author:
    Xin Wei
    ,
    Yiren Sun
    ,
    Hongren Gong
    ,
    Mingjun Hu
    ,
    Yanqing Zhao
    ,
    Jingyun Chen
    DOI: 10.1061/JPEODX.PVENG-1505
    Publisher: American Society of Civil Engineers
    Abstract: Mesostructure-based simulation technology offers an effective approach to modeling the mesomechanical responses of asphalt concrete and studying its mesomechanisms. However, current exploration depth and utilization level of the simulation data are both very limited. The intricacies of the vast mesomechanical response data necessitate the development and adoption of advanced data-processing methods that can gain valuable insights. To this end, this study proposed a data-mining framework that integrates a three-dimensional (3D) random aggregate method and the finite-element (FE) method for mesoscopic simulation of asphalt concrete. This framework mainly consists of two stages. In the first stage, a random aggregate method is used to establish 3D mesostructures of asphalt concrete, and FE simulations are performed on these mesostructures to model their mesomechanical responses. Based on the simulated responses, several procedures for data export, processing, and mining are developed and sequentially performed in the second stage. The data-export procedure extracts the mesomechanical responses from the FE result files and stores them into a database for efficient management. The required response data are accessed via SQL and then processed preliminarily using statistical and computational methods. Machine-learning methods and variable importance analysis techniques are used to conduct in-depth mining analyses on the processed data. The efficacy of this framework was demonstrated by applying it to investigating the effects of the volume fractions of coarse aggregates (larger than 2.36 mm) of different sizes on 129 simulated mesostructures of asphalt concrete with a nominal maximum aggregate size of 13.2 mm. The results indicated that the coarse aggregates with the particle sizes between 4.75 and 9.5 mm may make the highest contribution to the overall dynamic modulus of asphalt concrete. The coarse aggregates with larger size differences could cause more significant stress concentrations in the surrounding asphalt mortar.
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      Data-Mining Framework Integrating 3D Random Aggregate Method and Finite-Element Method for Mesoscopic Simulation of Asphalt Concrete

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    contributor authorXin Wei
    contributor authorYiren Sun
    contributor authorHongren Gong
    contributor authorMingjun Hu
    contributor authorYanqing Zhao
    contributor authorJingyun Chen
    date accessioned2024-12-24T09:59:52Z
    date available2024-12-24T09:59:52Z
    date copyright9/1/2024 12:00:00 AM
    date issued2024
    identifier otherJPEODX.PVENG-1505.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4298100
    description abstractMesostructure-based simulation technology offers an effective approach to modeling the mesomechanical responses of asphalt concrete and studying its mesomechanisms. However, current exploration depth and utilization level of the simulation data are both very limited. The intricacies of the vast mesomechanical response data necessitate the development and adoption of advanced data-processing methods that can gain valuable insights. To this end, this study proposed a data-mining framework that integrates a three-dimensional (3D) random aggregate method and the finite-element (FE) method for mesoscopic simulation of asphalt concrete. This framework mainly consists of two stages. In the first stage, a random aggregate method is used to establish 3D mesostructures of asphalt concrete, and FE simulations are performed on these mesostructures to model their mesomechanical responses. Based on the simulated responses, several procedures for data export, processing, and mining are developed and sequentially performed in the second stage. The data-export procedure extracts the mesomechanical responses from the FE result files and stores them into a database for efficient management. The required response data are accessed via SQL and then processed preliminarily using statistical and computational methods. Machine-learning methods and variable importance analysis techniques are used to conduct in-depth mining analyses on the processed data. The efficacy of this framework was demonstrated by applying it to investigating the effects of the volume fractions of coarse aggregates (larger than 2.36 mm) of different sizes on 129 simulated mesostructures of asphalt concrete with a nominal maximum aggregate size of 13.2 mm. The results indicated that the coarse aggregates with the particle sizes between 4.75 and 9.5 mm may make the highest contribution to the overall dynamic modulus of asphalt concrete. The coarse aggregates with larger size differences could cause more significant stress concentrations in the surrounding asphalt mortar.
    publisherAmerican Society of Civil Engineers
    titleData-Mining Framework Integrating 3D Random Aggregate Method and Finite-Element Method for Mesoscopic Simulation of Asphalt Concrete
    typeJournal Article
    journal volume150
    journal issue3
    journal titleJournal of Transportation Engineering, Part B: Pavements
    identifier doi10.1061/JPEODX.PVENG-1505
    journal fristpage04024021-1
    journal lastpage04024021-12
    page12
    treeJournal of Transportation Engineering, Part B: Pavements:;2024:;Volume ( 150 ):;issue: 003
    contenttypeFulltext
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