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     李正印1  ,  何巧靈2  ,  魏汝明2  ,  劉振東3

               ( 1 .  山東省建筑設計研究院有限公司 ，濟南 250000 ;  2.  濟南市勘察測繪研究院 ，濟南 250000 ;

          3 .  濟南市市政工程設計研究院（集團） 有限責任公司 ，濟南 250000)

摘   要 ：為實現基坑沉降的高精度預測 ，基于基坑現場沉降監測成果 ，先通過極點對稱模態分解算法開展 沉降數據的分解處理 ，得到若干模態分量和趨勢分量 ，再利用灰狼算法 、門控循環單元神經網絡構建組合預測 模型 ，并利用此模型開展各模態分量 、趨勢分量的變形預測 ， 以得到基坑沉降的組合預測值。結果表明： 在基 坑施工過程中 ，常見沉降監測項目包括地表沉降 、坑頂沉降和建筑物沉降 ；經監測數據分析 ，坑頂沉降的剩余 變形空間相對最大 ，其次是建筑物沉降和地表沉降 ；總體來說 ，3 類沉降變形項目的剩余變形空間還是較為樂 觀 ，只是局部少量監測點的剩余變形空間較少 ；在數據處理過程中 ，極點對稱模態分解算法具有較強的數據處 理能力 ，且其能力明顯優于小波去噪和模態分解法的數據處理效果 ，且經 GWO - GRU 預測 ，得到此模型在 3 類 沉降項目中 ，預測結果的相對誤差均值是在 2% 左右 ，具有較高的預測精度 ，且其預測結果得出 3 類沉降變形在 后續的發展趨勢較為一致 ，均呈小速率增加趨勢 ，收斂特征均較為明顯 ，側面驗證了基坑支護措施的運營效果 良好。通過研究 ，可為類似工程提供技術參考 ，具有一定的現實意義。

關鍵詞：基坑 ；沉降變形 ；數據分解 ；灰狼算法 ；組合預測

中圖分類號：U459           文獻標志碼：A 

          

 文章編號： 1005- 8249   (2025)  03- 0128- 06 

DOI:10. 19860/j.cnki.issn1005 - 8249.2025 .03 .023

Li Zhengyin¹  He Qiaoling²  Wei Ruming²  Liu Zhendong³

（1. Shandong Provincial Architectural Design Institute Co., Ltd ,Ji’nan, 250000 ,China; 2. Jinan Survey and Mapping Research Institute, Ji’nan, 250000 ,China; 3. Jinan Municipal Engineering Design and Research Institute (Group) Co., Ltd ,Ji’nan, 250000 ,China）

Abstract: To achieve high-precision prediction of foundation pit settlement, based on the on-site settlement monitoring results of the foundation pit, the polar symmetric mode decomposition algorithm is first used to decompose the settlement data, obtaining several modal components and trend components. Then, the grey wolf algorithm and gated recurrent unit neural network are used to construct a combined prediction model, and this model is used to predict the deformation of each modal component and trend component, in order to obtain the combined prediction value of foundation pit settlement. The analysis results indicate that common settlement monitoring items during foundation pit construction include surface settlement, pit top settlement, and building settlement; According to monitoring data analysis, the remaining deformation space of the pit top settlement is relatively the largest, followed by building settlement and surface settlement; Overall, the remaining deformation space for the three types of settlement and deformation projects is still relatively optimistic, with only a small number of monitoring points having limited remaining deformation space in certain areas; In the process of data processing, the pole symmetric mode decomposition algorithm has strong data processing ability, and its ability is significantly better than the data processing effect of wavelet denoising and mode decomposition methods. According to GWO-GRU prediction, the relative error mean of the prediction results of this model in three types of settlement projects is around 2%, which has high prediction accuracy. The prediction results show that the development trend of the three types of settlement deformation is relatively consistent in the future, showing a small rate increase trend and obvious convergence characteristics. This indirectly verifies the good operational effect of foundation pit support measures. Through research, it can provide technical references for similar projects and has certain practical significance.

Keywords: excavation pit; settlement deformation; data decomposition; grey wolf algorithm; combination prediction

作者簡介：李正印  (1989—) ,  男 ，本科 ，高級工程師 ，研究方向：巖土工程勘察 、基坑邊坡設計等相關技術服務。

通信作者：何巧靈  (1987—) ,  女 ，碩士 ，高級工程師 ，研究方向：巖土工程相關生產及科研工作。

收稿日期：2024 - 12 - 19