理论与试验研究

基于UBPSO算法的隧道围岩力学参数动态反演

  • 罗林 ,
  • 李鹏飞 ,
  • 邢振华 ,
  • 周倩 ,
  • 杨虎
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  • 1.重庆交通大学 土木工程学院,重庆 400074;
    2.中建三局集团有限公司, 武汉 430000;
    3.中建铁路投资建设集团有限公司,北京 100053
罗林(1985—),女,四川泸州人,博士,副教授,主要从事隧道、岩土和结构工程方面的研究。E-mail:346978265@qq.com

收稿日期: 2025-05-22

  网络出版日期: 2026-04-28

基金资助

国家自然科学基金(42277183)

Dynamic Inversion for the Mechanical Parameters of Tunnel Surrounding Rock Based on UBPSO Algorithm

  • Luo Lin ,
  • Li Pengfei ,
  • Xing Zhenhua ,
  • Zhou Qian ,
  • Yang Hu
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  • 1. College of Civil Engineering, Chongqing Jiaotong University, Chongqing 400047, P.R. China;
    2. China Construction Third Engineering Bureau Group Co., Ltd., Wuhan 430000, P.R. China;
    3. China State Construction Railway Investment & Engineering Group Co., Ltd., Beijing 100053, P.R. China

Received date: 2025-05-22

  Online published: 2026-04-28

摘要

隧道开挖过程中,围岩受扰动而损伤,其岩土参数将发生变化。为准确掌握岩土参数动态变化规律,提出基于边界更新粒子群算法(UBPSO)的隧道围岩力学参数反演方法。针对粒子群算法寻优结果波动性大的问题,提出边界更新粒子群算法。通过动态更新搜索边界上下限、个体历史最优值进行交叉与变异操作和惯性权重自适应更新,实现边界更新粒子群算法高精度、快速寻优。Ackley函数稳定性测试实验可知,相对于传统粒子群算法,边界更新粒子群算法具有寻优速度快、结果精度高、结果波动性小和不易陷入局部最优值等优点。基于边界更新粒子群算法,建立MATLAB-PYTHON-ABAQUS和现场监测数据联合使用的隧道围岩力学参数反分析模型。以铜锣山隧道YK76+470~YK76+502.5区段为例,通过该模型反分析隧道拱顶沉降现场监测数据可知,最终崩坡积土层弹性模量从0.200 GPa下降至0.106 GPa、内摩擦角从14°下降至12.072°、黏聚力从22 kPa下降至19.373 kPa。

本文引用格式

罗林 , 李鹏飞 , 邢振华 , 周倩 , 杨虎 . 基于UBPSO算法的隧道围岩力学参数动态反演[J]. 地下空间与工程学报, 2026 , 22(2) : 437 -447 . DOI: 10.20174/j.JUSE.2026.02.06

Abstract

With the excavation of tunnels, the surrounding rock is disturbed and damaged, resulting in changes in its geotechnical parameters. To obtain dynamic variation laws of geotechnical parameters accurately, an inversion method for mechanical parameters of tunnel surrounding rock based on updated boundaries particle swarm optimization (UBPSO) algorithm is proposed. Due to the large fluctuation of optimization results with the particle swarm algorithm, an updated boundaries particle swarm algorithm is proposed. By updating the upper and lower limits of search boundaries dynamically, performing reversal and mutation operations on individual historical optimal values, and updating inertia weights adaptively, the updated boundaries particle swarm algorithm achieves high-precision and fast optimization. The stability test of the Ackley function indicates that compared to the particle swarm optimization algorithm, the updated boundaries particle swarm algorithm has the following advantages, fast optimization speed, high accuracy of results, small fluctuation of outcomes, and less sensitivity to getting stuck in local optima. Based on the updated boundaries particle swarm optimization algorithm, a reverse analysis model for mechanical parameters of tunnel surrounding rock using MATLAB-PYTHON-ABAQUS and on-site monitoring data is established. Taking the YK76+470 to YK76+502.5 section of the Tongluoshan tunnel as an example, the model was used to analyze the on-site monitoring data of the tunnel arch settlement. It was found that the elastic modulus of the colluvial soil layer decreased from 0.200 GPa to 0.106 GPa, the internal friction angle dropped from 14° to 12.072°, and the cohesion value fell from 22 kPa to 19.373 kPa.

参考文献

[1] Monmarche N, Venturini G, Slimane M. On how Pachycondyla apicalis ants suggest a new search algorithm[J]. Future Generation Computer Systems, 2000, 16(8): 937-946.
[2] 高玮. 基于粒子群优化的岩土工程反分析研究[J]. 岩土力学, 2006, 27(5): 795-798. (Gao Wei. Back analysis algorithm in geotechnical engineering based on particle swarm optimization [J]. Rock and Soil Mechanics, 2006, 27(5): 795-798. (in Chinese))
[3] 李金凤, 杨启贵, 徐卫亚. 基于改进粒子群算法CHPSO-DS的面板坝堆石体力学参数反演[J]. 岩石力学与工程学报, 2008, 27(6): 1229-1235. (Li Jinfeng,Yang Qigui,Xu Weiya. Back analyzing mechanical parameters of rockfill based on modified particle swarm optimization CHPSO-DS[J]. Chinese Journal of Rock Mechanics and Engineering, 2008, 27(6): 1229-1235. (in Chinese))
[4] Chen K, Zhou F, Yin L, et al. A hybrid particle swarm optimizer with sine cosine acceleration coefficients[J]. Information Sciences, 2018, 422(1): 218-241.
[5] 苏国韶, 张克实, 吕海波. 位移反分析的粒子群优化-高斯过程协同优化方法[J]. 岩土力学, 2011, 32 (2): 510-515. (Su Guoshao, Zhang Keshi, Lü Haibo. A cooperative optimization method based on particle swarm optimization and Gaussian process for displacement back analysis[J]. Rock and Soil Mechanics, 2011, 32(2): 510-515. (in Chinese))
[6] 夏怡杰, 杨侃, 夏超, 等. 基于GWO-PSO算法的小尺度地区LID布设优化模型研究[J]. 水利水电技术(中英文), 2024, 55(3): 90-101. (Xia Yijie, Yang Kan, Xia Chao, et al. Research on the optimization model of LID deployment in small scale regions based on GWO-PSO algorithm[J]. Water Resources and Hydropower Engineering, 2024, 55(3): 90-101. (in Chinese))
[7] 毛伟琦, 李小珍, 王翔, 等. 基于LSSVM和GWOPSO算法的桥岸边坡位移反演方法研究[J]. 铁道科学与工程学报, 2023, 20(11): 4299-4310. (Mao Weiqi, Li Xiaozhen, Wang Xiang, et al. Inversion method of bridge abutment slope displacement based on LSSVM and GWOPSO algorithm[J]. Journal of Railway Science and Engineering, 2023, 20(11): 4299-4310. (in Chinese))
[8] 袁克阔. 粒子群算法改进及内变量本构模型参数反演[J]. 煤田地质与勘探, 2017, 45(2): 112-117. (Yuan Kekuo. Improved particle swarm optimization and parameter inversion in internal variable constitutive model[J]. Coal Geology & Exploration, 2017, 45(2): 112-117. (in Chinese))
[9] 王学武, 严益鑫, 顾幸生. 基于莱维飞行粒子群算法的焊接机器人路径规划[J]. 控制与决策, 2017, 32 (2): 373-377. (Wang Xuewu, Yan Yixin, Gu Xingsheng. Welding robot path planning based on Levy-PSO[J]. Control and Decision, 2017, 32 (2): 373-377. (in Chinese))
[10] 漆祖芳, 姜清辉, 周创兵, 等. 基于v-SVR和MVPSO算法的边坡位移反分析方法及其应用[J]. 岩石力学与工程学报, 2013, 32(6): 1185-1196. (Qi Zufang,Jiang Qinghui,Zhou Chuangbing, et al. A new slope displacement back analysis method based on v-SVR and MVPSO algorithm and its application[J]. Chinese Journal of Rock Mechanics and Engineering, 2013, 32(6): 1185-1196. (in Chinese))
[11] 张子豪, 靳其兵. 基于社会等级淘汰机制的GWO_PSO算法[J]. 南京理工大学学报, 2021, 45(2): 164-170. (Zhang Zihao, Jin Qibin. GWO_PSO algorithm based on social rank elimination mechanism[J]. Journal of Nanjing University of Science and Technology, 2021, 45(2): 164-170. (in Chinese))
[12] 苏明健, 肖宝弟, 岳丽丽. 基于改进PSO-SA算法的城轨列车ATO节能优化研究[J]. 传感器与微系统, 2023, 42(10): 64-67. (Su Mingjian, Xiao Baodi, Yue Lili. Research on ATO energy saving optimization of urban rail train based on improved PSO-SA algorithm[J]. Transducer and Microsystem Technologies, 2023, 42(10): 64-67. (in Chinese))
[13] 阮永芬, 高春钦, 刘克文, 等. 基于粒子群算法优化小波支持向量机的岩土力学参数反演[J]. 岩土力学, 2019, 40(9): 3662-3669. (Ruan Yongfen, Gao Chunqin, Liu Kewen, et al. Inversion of rock and soil mechanics parameters based on particle swarm optimization wavelet support vector machine[J]. Rock and Soil Mechanics, 2019, 40(9): 3662-3669. (in Chinese))
[14] Gong W P, Tian S, Wang L, et al. Interval prediction of landslide displacement with dual-output least squares support vector machine and particle swarm optimization algorithms[J]. Acta Geotech, 2022, 17(2): 4013-4031.
[15] Dadvar M, Navidi H, Javadi H H S, et al. A cooperative approach for combining particle swarm optimization and differential evolution algorithms to solve single-objective optimization problems[J]. Applied Intelligence, 2022, 52(15): 4089-4108.
[16] 陈秋莲, 郑以君, 蒋环宇, 等. 基于神经网络改进粒子群算法的动态路径规划[J]. 华中科技大学学报: 自然科学版, 2021, 49(2): 51-55. (Chen Qiulian, Zheng Yijun, Jiang Huanyu, et al. Improved particle swarm optimization algorithm based on neural network for dynamic path planning[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49(2): 51-55. (in Chinese))
[17] Zhang L, Shi B, Zhu H H, et al. PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect[J]. Landslides, 2021, 18(1): 179-193.
[18] Kennedy J,Eberhart R. Particle swarm optimization[A] //Proceedings of ICNN'95-International Conference on Neural Networks[C]. Perth, WA, Australia, 1995: 1942-1948.
[19] 凌同华, 秦健, 宋强, 等. 基于改进粒子群算法和神经网络的智能位移反分析法及其应用[J]. 铁道科学与工程学报, 2020, 17(9): 2181-2190. (Ling Tonghua, Qin Jian, Song Qiang, et al. Intelligent displacement back-analysis based on improved particle swarm optimization and neural network and its application[J]. Journal of Railway Science and Engineering, 2020,17(9): 2181-2190. (in Chinese))
[20] 吕昱呈, 莫愿斌. 融入变异交叉的改进天牛须算法求解TSP及工程应用[J]. 计算机应用研究, 2021, 38 (12): 3662-3666. (Lü Yucheng, Mo Yuanbin. Improved beetle antennae search algorithm with mutation crossover in TSP and engineering application [J]. Application Research of Computers, 2021, 38 (12): 3662-3666. (in Chinese))
[21] 戴文智, 杨新乐. 基于惯性权重对数递减的粒子群优化算法[J]. 计算机工程与应用, 2015, 51 (17): 14-19, 52. (Dai Wenzhi, Yang Xinle. Particle swarm optimization algorithm based on inertia weight logarithmic decreasing[J]. Computer Engineering and Applications, 2015, 51(17): 14-19, 52. (in Chinese))
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