Back-calculation of subgrade modulus considering shallow bedrock and viscoelasticity based on multi population genetic algorithm
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School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China

Clc Number:

U416.1

Fund Project:

National Key R & D Program of China (No. 2021YFB2600900); National Major Scientific Instruments and Equipments Development Project (No. 51927814); National Science Fund for Distinguished Young Scholars (No. 52025085); National Natural Science Foundation of China (No. 51878078); Graduate Innovation Program of Changsha University of Science & Technology (No. CX2020SS09)

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    Abstract:

    This article proposes a new idea for back-calculation of the subgrade modulus considering shallow bedrock and the viscoelastic characteristics. For the subgrade model, displacement boundary conditions and the Kelvin model are adopted to describe the depth of the shallow bedrock and the viscoelasticity, respectively. The portable falling weight deflectometer (PFWD) field test is simulated by ABAQUS general finite element (FE) software, and the optimal value of the modulus is iterated by a multi population genetic algorithm (MPGA). Based on the new method, back-calculation results from FE simulation tests show that the modulus average error of the forward model considering shallow bedrock is 7.0%, while that of the forward model considering half space is as high as 16.2%, indicating that negletct of the shallow bedrock in the forward model of the back-calculation program may cause a significant error in the inversion modulus, but its influence decreases with the increase of the depth of the shallow bedrock, and the depth limit is 3 m. Similarly, for the FE model considering viscoelasticity, the maximum error for neglect of this attribute in the forward model reaches 27.9%, compared with the error of only 7.4% when considering viscoelasticity in the forward model. Due to the difficulty exploring the depth of shallow bedrock, examinations are only conducted from the theoretical aspect.

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张军辉,刘杰,范海山,张石平,丁乐.基于多种群遗传算法考虑浅层基岩及粘弹性的路基模量反演方法[J].土木与环境工程学报(中英文),2023,45(2):1~20

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History
  • Received:February 23,2022
  • Revised:
  • Adopted:
  • Online: March 20,2023
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