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首页 > 过刊浏览>2023年第45卷第2期 >1-20. DOI:10.11835/j.issn.2096-6717.2022.021
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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
CSTR:
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作者:
作者单位:

长沙理工大学 交通运输工程学院，长沙 410114

作者简介:

ZHANG Junhui (1978- ), PhD, professor, doctorial supervisor, main research interests: subgrade and geotechnical engineering, E-mail: zjhseu@csust.edu.cn.

中图分类号:

U416.1


Back-calculation of subgrade modulus considering shallow bedrock and viscoelasticity based on multi population genetic algorithm
Author:
Affiliation:

School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China

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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    摘要:

    提出一种考虑浅层基岩和粘弹性的路基模量反算新思路。对于路基模型，分别采用位移边界条件和开尔文模型来描述浅层基岩深度和粘弹性，并利用有限元软件对便携式落锤弯沉仪现场试验进行模拟，采用多种群遗传算法对路基模量最优值进行迭代。有限元模拟计算结果表明，新方法考虑浅层基岩的正算模型的反演模量平均误差为7.0%，而考虑半空间的正算模型却高达16.2%，说明忽略浅层基岩会使反演模量产生较大误差；随着浅层基岩深度的增加，误差减小，当深度为3 m时，影响几乎不存在。对于考虑粘弹性的有限元模型，在正算模型中忽略该特性的最大误差可达27.9%，而考虑粘弹性的最大误差仅为7.4%。由于浅层基岩深度勘探难度大，仅从理论方面进行研究。

    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.

    参考文献
    [1] JIANG Z W, GUO X Y, LI W T, et al. Self-shrinkage behaviors of waste paper fiber reinforced cement paste considering its self-curing effect at early-ages [J]. International Journal of Polymer Science, 2016, 2016: 8690967.
    [2] NOIKE T, GOO I S, MATSUMOTO H, et al. Development of a new type of anaerobic digestion process equipped with the function of nitrogen removal [J]. Water Science and Technology, 2004, 49(5/6): 173-179.
    [3] HEIDARABADIZADEH N, GHANIZADEH A R, BEHNOOD A. Prediction of the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine method and colliding bodies optimization algorithm [J]. Construction and Building Materials, 2021, 275: 122140.
    [4] LIN B, ZHANG F, FENG D C. Long-term resilient behaviour of thawed saturated silty clay under repeated cyclic loading: Experimental evidence and evolution model [J]. Road Materials and Pavement Design, 2019, 20(3): 608-622.
    [5] CHEN S H, DU D Y. Degradation of n-butyl xanthate using fly ash as heterogeneous Fenton-like catalyst [J]. Journal of Central South University, 2014, 21(4): 1448-1452.
    [6] AI X M, YI J Y, ZHAO H, et al. An empirical predictive model for the dynamic resilient modulus based on the static resilient modulus and California bearing ratio of cement- and lime-stabilised subgrade soils [J]. Road Materials and Pavement Design, 2021, 22(12): 2818-2837.
    [7] YAO Y S, ZHENG J L, ZHANG J H, et al. Model for predicting resilient modulus of unsaturated subgrade soils in South China [J]. KSCE Journal of Civil Engineering, 2018, 22(6): 2089-2098.
    [8] SAHA S, GU F, LUO X, et al. Development of a modulus of subgrade reaction model to improve slab-base interface bond sensitivity [J]. International Journal of Pavement Engineering, 2020, 21(14): 1794-1805.
    [9] ASLI C, FENG Z Q, PORCHER G, et al. Back-calculation of elastic modulus of soil and subgrade from portable falling weight deflectometer measurements [J]. Engineering Structures, 2012, 34: 1-7.
    [10] MA Q, SUN Z. Direct inversion of Young's modulus and Poisson's ratio using exact Zoeppritz equations based on double constraints [J]. Journal of Seismic Exploration, 2019, 28(2): 175-204.
    [11] GOPALAKRISHNAN K, KIM S. Global optimization of pavement structural parameters during back-calculation using hybrid shuffled complex evolution algorithm [J]. Journal of Computing in Civil Engineering, 2010, 24(5): 441-451.
    [12] SALTAN M, TERZI S, Kü?üKSILLE E U. Backcalculation of pavement layer moduli and Poisson's ratio using data mining [J]. Expert Systems with Applications, 2011, 38(3): 2600-2608.
    [13] TERZI S, SALTAN M, Kü?üKSILLE E U, et al. Backcalculation of pavement layer thickness using data mining [J]. Neural Computing and Applications, 2013, 23(5): 1369-1379.
    [14] PARK S W, PARK H M, HWANG J J. Application of genetic algorithm and finite element method for backcalculating layer moduli of flexible pavements [J]. KSCE Journal of Civil Engineering, 2010, 14(2): 183-190.
    [15] WANG M H, CHI S C, XIE Y F, et al. Dynamic parameters inversion analysis of rockfill materials considering interaction effects based on weak earthquakes [J]. Soil Dynamics and Earthquake Engineering, 2020, 130: 105968.
    [16] SHI X M, QUILTY S M, LONG T, et al. Managing airport stormwater containing deicers: Challenges and opportunities [J]. Frontiers of Structural and Civil Engineering, 2017, 11(1): 35-46.
    [17] ZHANG X R, OTTO F, OESER M. Pavement moduli back-calculation using artificial neural network and genetic algorithms [J]. Construction and Building Materials, 2021, 287: 123026.
    [18] VARMA S, EMIN KUTAY M. Backcalculation of viscoelastic and nonlinear flexible pavement layer properties from falling weight deflections [J]. International Journal of Pavement Engineering, 2016, 17(5): 388-402.
    [19] ZHANG J H, FAN H S, ZHANG S P, et al. Back-calculation of elastic modulus of high liquid limit clay subgrades based on viscoelastic theory and multipopulation genetic algorithm [J]. International Journal of Geomechanics, 2020, 20(10): 04020194.
    [20] DENG W, XU J J, ZHAO H M. An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem [J]. IEEE Access, 2019, 7: 20281-20292.
    [21] ZAABAR I, CHATTI K, LEE H S, et al. Backcalculation of asphalt concrete modulus master curve from field-measured falling weight deflectometer data [J]. Transportation Research Record: Journal of the Transportation Research Board, 2014, 2457(1): 80-92.
    [22] JIAO Y L, XING X C, ZHANG P, et al. Multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm [J]. Concurrent Engineering, 2018, 26(4): 367-377.
    [23] YUN K K. Comparison of static and dynamic strains of asphalt concrete pavement from FWD tests [J]. KSCE Journal of Civil Engineering, 1997, 1(1): 39-48.
    [24] LIU G S, LI L, YANG X C, et al. Numerical analysis of stress distribution in backfilled stopes considering interfaces between the backfill and rock walls [J]. International Journal of Geomechanics, 2017, 17(2): 06016014.
    [25] UDDIN W, MEYER A, HUDSON W R. Rigid bottom considerations for nondestructive evaluation of pavements (discussion) [M]. Transportation Research Board, 1986.
    [26] BRIGGS R C, NAZARIAN S. Effects of unknown rigid subgrade layers on backcalculation of pavement moduli and projections of pavement performance [M]. Transportation Research Board, 1989.
    [27] HUDSON W R, ELKINS G E, UDDIN W, et al. Evaluation of pavement deflection measuring equipment [M]. Composite Pavements, 1987.
    [28] ROHDE G T, SMITH R E. Determining depth to apparent stiff layer from FWD data [R]. Federal Highway Administration, 1991.
    [29] SEOK Y T, SUNG S J. Effect of structural geometry of jointed concrete pavement on backcalculation using AREA method [J]. International Journal of Highway Engineering, 2007, 9(1): 39-46.
    [30] UZAN J, LYTTON R L, GERMANN F P. General procedure for backcalculating layer moduli [M]//Nondestructive Testing of Pavements and Backcalculation of Moduli, 1989, 1026: 217-228.
    [31] ZHANG J H, PENG J H, ZHANG A S, et al. Prediction of permanent deformation for subgrade soils under traffic loading in Southern China [J]. International Journal of Pavement Engineering, 2022, 23(3): 673-682.
    [32] SIRITHEPMONTREE H, SAPSATHIARN Y. Dynamic soil models for backcalculation of material properties from falling weight deflectometer deflection data [J]. Procedia Engineering, 2017, 189: 152-157.
    [33] ZHA X D. Subgrade dynamic backcalculation based on PFWD [J]. Advanced Materials Research, 2013, 723: 220-229.
    [34] GERRARD C M, WARDLE L J. Rational design of surface pavement layers [J]. Australian Road Research, 1980， 10(2): 3-15.
    [35] SENSENEY C T, KRAHENBUHL R A, MOONEY M A. Genetic algorithm to optimize layer parameters in light weight deflectometer backcalculation [J]. International Journal of Geomechanics, 2013, 13(4): 473-476.
    [36] PARK J, PARK M W, KIM D W, et al. Multi-population genetic algorithm for multilabel feature selection based on label complementary communication [J]. Entropy, 2020, 22(8): 876.
    [37] GUAN B X, ZHANG C S, NING J X. Genetic algorithm with a crossover elitist preservation mechanism for protein-ligand docking [J]. AMB Express, 2017, 7: 174.
    [38] HUSSAIN A, CHEEMA S A. A new selection operator for genetic algorithms that balances between premature convergence and population diversity [J]. Croatian Operational Research Review, 2020, 11(1): 107-119.
    [39] YU F, XU X Z. A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network [J]. Applied Energy, 2014, 134: 102-113.
    [40] BORJA R I, WU W H, SMITH H A. Nonlinear response of vertically oscillating rigid foundations [J]. Journal of Geotechnical Engineering, 1993, 119(5): 893-911.
    [41] LI M Y, WANG H. Prediction of asphalt pavement responses from FWD surface deflections using soft computing methods [J]. Journal of Transportation Engineering, Part B: Pavements, 2018, 144(2): 04018014.
    [42] WANG H, AL-QADI I L. Importance of nonlinear anisotropic modeling of granular base for predicting maximum viscoelastic pavement responses under moving vehicular loading [J]. Journal of Engineering Mechanics, 2013, 139(1): 29-38.
    [43] WANG H, LI M Y. Comparative study of asphalt pavement responses under FWD and moving vehicular loading [J]. Journal of Transportation Engineering, 2016, 142(12): 04016069.
    [44] NAM B H, KEE S H, YOUN H, et al. Methodology to improve the AASHTO subgrade resilient modulus equation for network-level use [J]. Journal of Transportation Engineering, 2015, 141(12): 04015027.
    [45] LEI L G. Backcalculation of asphalt concrete complex modulus curve by layered viscoelastic solution [D]. Michigan, USA: Michigan State University, 2011.
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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. ZHANG Junhui, LIU Jie, FAN Haishan, ZHANG Shiping, DING Le. Back-calculation of subgrade modulus considering shallow bedrock and viscoelasticity based on multi population genetic algorithm[J]. JOURNAL OF CIVIL AND ENVIRONMENTAL ENGINEERING,2023,45(2):1-20.10.11835/j. issn.2096-6717.2022.021

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  • 收稿日期:2022-02-23
  • 在线发布日期: 2023-03-20
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