土木与环境工程学报（中英文）2023，Vol.45Issue(2)：1-20,20.DOI:10.11835/j.issn.2096-6717.2022.021
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
Back-calculation of subgrade modulus considering shallow bedrock and viscoelasticity based on multi population genetic algorithm
摘要
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 is7.0％, while that of the forward model considering half space is as high as16.2％, 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 is3 m. Similarly, for the FE model considering viscoelasticity, the maximum error for neglect of this attribute in the forward model reaches27.9％, compared with the error of only7.4％ 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.关键词
反演/路基模量/浅层基岩/粘弹性/遗传算法Key words
back-calculation/subgrade modulus/shallow bedrock/viscoelasticity/genetic algorithm分类
交通工程引用本文复制引用
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):1-20,20.基金项目
National Key R&D Program of China(No.2021YFB2600900) （No.2021YFB2600900）
National Major Scientific Instruments and Equipments Development Project(No.51927814) （No.51927814）
National Science Fund for Distinguished Young Scholars(No.52025085) （No.52025085）
National Natural Science Foundation of China(No.51878078) （No.51878078）
Graduate Innovation Program of Changsha University of Science&Technology(No.CX2020SS09) （No.CX2020SS09）
