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劉海龍

 

（南昌市建設工程綜合監督事務中心 ，南昌 330038)

摘   要 ：為準確掌握深基坑水平位移變化規律，基于某工程實測現場監測數據，先利用統計方法開展其變形特征分析，再進一步利用動態權重粒子群算法、 自適應模糊神經網絡和極限學習機構建變形預測模型，對基 坑水平位移預測進行研究。結果表明：基坑水平位移變化范圍為 23 . 84 ~ 31 . 96  mm ,  平均值為 29. 03  mm ,  且最 大水平位移主要位于 0. 74H 處 ；基坑預測結果的相對誤差范圍為 2. 02%~ 2. 17% ,  具有較高預測精度，預測模型 合理，預測結果顯示基坑水平位移仍會進一步增加，但增加速率趨于減小，變形總體趨于穩定。通過基坑變形 預測，掌握了基坑水平位移變化規律，為其安全施工提供了技術支持。

關鍵詞：深基坑 ；水平位移 ；數據分析 ；變形預測 ；極限學習機

中圖分類號：TU433            

文獻標志碼：A           

文章編號： 1005- 8249   (2025)  01- 0065- 05 

DOI:10. 19860/j.cnki.issn1005 - 8249.2025 .01 .013

 

LIU Hailong

(  Nanchang  Construction Engineering Comprehensive Supervision Center ,  Nanchang 330038 ,  China)

Abstract: To accurately grasp the variation law of horizontal displacement in deep foundation pits, based on on-site monitoring data, statistical methods are first used to analyze their deformation characteristics. Then, dynamic weight particle swarm optimization algorithm, adaptive fuzzy neural network, and limit learning mechanism are further used to build deformation prediction models to achieve research on horizontal displacement prediction of foundation pits. The case analysis shows that the horizontal displacement range of the foundation pit is 23.84~31.96mm, with an average value of 29.03mm, and the maximum horizontal displacement is mainly located at 0.74H. Meanwhile, through deformation prediction, the relative error range of the predicted results is 2.02%~2.17%, which has high prediction accuracy and verifies the rationality of the prediction model. The subsequent prediction results have high credibility, and the prediction results show that the horizontal displacement of the foundation pit will continue to increase, but the rate of increase tends to decrease, and the deformation overall tends to stabilize. Through research, the horizontal displacement variation law of the foundation pit has been effectively mastered, providing technical support for its safe construction.

Key words: deep foundation pit; horizontal displacement; data analysis; deformation prediction; extreme learning machine

作者簡介：劉海龍  (1980—) ,   男，碩士研究生，工程師， 研究方向： 工程質量安全監督管理。

收稿日期：2023 - 02 - 24