基于LHS-WOA-ELM的隧道圍巖參數反演分析
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華南理工大學

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U459.2

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國家自然科學基金資助項目(51878296);國家自然科學基金資助項目(12302502)


Inversion analysis of surrounding rock parameters of tunnel based on LHS-WOA-ELM
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South China University of Technology

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    摘要:

    為提高隧道圍巖力學參數取值的合理性,依托珠海市某超大斷面隧道工程提出一種新型圍巖參數反演模型?;诶〕⒎襟w抽樣(LHS)產生初始樣本后進行參數敏感性分析以確定圍巖的關鍵參數和改進樣本結構,然后利用鯨魚優化算法(WOA)對極限學習機(ELM)的隱含層神經元節點數、初始權重和閾值進行優化進而組成LHS-WOA-ELM反演模型,將反演所得參數代入FLAC3D計算位移并與現場實測數據進行對比分析。結果表明:采用基于LHS進行的參數敏感性分析能夠以較少的樣本考察多參數共同變化的情況,并確定影響圍巖位移的主要參數為彈性模量E、黏聚力c和內摩擦角φ;相比于WOA、ELM、BP算法模型,LHS-WOA-ELM模型反演所獲得的位移計算值與實測值相差更小,表明該反演分析方法能夠很好地反映圍巖參數與變形之間的非線性、不確定性特征,進一步提高圍巖反演的精度和效率,可為地下洞室、礦業工程的設計參數確定提供參考。

    Abstract:

    In order to improve the rationality of mechanical parameters of tunnel surrounding rock, a new inversion model of surrounding rock parameters is proposed based on a tunnel project with a super large-section in Zhuhai. After the initial samples are generated based on Latin hypercube sampling (LHS), the parameter sensitivity analysis is carried out to determine the key parameters of the surrounding rock and improve the sample structure. Then, the whale optimization algorithm (WOA) is used to optimize the number of hidden layer nodes, the initial weights and the thresholds of the extreme learning machine (ELM) to form the LHS-WOA-ELM inversion model. The inversion parameters are substituted into FLAC3D to calculate the deformation and compare with the field measured data. The results show that the parameter sensitivity analysis based on LHS can investigate the co-variation of multi-parameters with fewer samples and and determine the main parameters affecting the displacement of surrounding rock as elastic modulus E, cohesion c and internal friction angle φ. Compared with WOA, ELM and BP algorithm models, the difference between the calculated deformation values obtained by LHS-WOA-ELM inversion model and the measured deformation values is smaller, indicating that the inversion analysis method can well reflect the nonlinear and uncertain characteristics between the surrounding rock parameters and deformation, and further improve the accuracy and efficiency of the surrounding rock inversion in super-large section tunnels, which can provide a reference for determining the design parameters of underground caverns and mining projects.

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  • 收稿日期:2024-04-28
  • 最后修改日期:2024-06-23
  • 錄用日期:2024-06-24
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