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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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