Identification of Stratum Reinforced by Vibro-replacement Stone Column Based on Fuzzy C-means Clustering Algorithm

WEI Yong-xin; ZHAO Gu-yao; TUO Xiao-jun; ZHAO Yu-fei; LIU Biao

doi:10.11988/ckyyb.20211361

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Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (5) : 111-117. DOI: 10.11988/ckyyb.20211361

Rock-Soil Engineering

Identification of Stratum Reinforced by Vibro-replacement Stone Column Based on Fuzzy C-means Clustering Algorithm

WEI Yong-xin¹, ZHAO Gu-yao², TUO Xiao-jun¹, ZHAO Yu-fei³, LIU Biao³

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Abstract

Accurately obtaining the geological information of soft foundation is an essential basis for determining the construction technique and controlling the pile quality of vibro-replacement stone columns. The existing geological exploration technology used to determine stratum information is considerably random and discrete, which makes it impossible to comprehensively understand the geological conditions of the reinforced areas. To overcome these limitations, this study relies on a large amount of data related to stratum classification attributes collected by the real-time monitoring system during the construction process of vibro-replacement stone columns at Lawa Hydropower Station. By cleaning big data, features such as penetration depth, speed, and current related to stratum classification attributes were selected for fuzzy C-means clustering algorithm-based study of stratum identification of the soft foundation. The results indicate that compared to the traditional K-means algorithm, the method proposed in this paper exhibits higher accuracy and superiority in identifying strata and enables real-time research and judgment of geological conditions. The research findings presented in this paper are of great significance in the rational evaluation of vibro-replacement stone column construction quality and the intelligent construction of the pile formation process.

Key words

vibro-replacement stone column / stratum identification / fuzzy C-means clustering algorithm / real-time monitoring system / construction process parameters

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WEI Yong-xin, ZHAO Gu-yao, TUO Xiao-jun, ZHAO Yu-fei, LIU Biao. Identification of Stratum Reinforced by Vibro-replacement Stone Column Based on Fuzzy C-means Clustering Algorithm[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(5): 111-117 https://doi.org/10.11988/ckyyb.20211361

References

[1] FUERKATE K, LI H, PENG Y, et al. Experimental Study on the Treatment Effect of Vibroflotation Gravel Piles for Saturated Sand Foundations in Coastal Areas[J]. Arabian Journal for Science and Engineering, 2021, 47(4): 4833-4848.
[2] CHEN F, JIAO H, HAN L, et al. Real-Time Monitoring of Construction Quality for Gravel Piles Based on Internet of Things[J]. Automation in Construction, 2020, 116: 103228.
[3] 李进元. 振冲碎石桩法地基处理在阴坪水电站中的应用[J]. 岩石力学与工程学报, 2013(增刊1):2968-2976.
[4] 曹星, 刘小峰, 定培中. 碎石桩处理深厚层软土地基效果分析[J]. raybet体育在线院报, 2000, 17(3):47-50.
[5] 蒋学林, 李武, 吴火兵. 锦屏3#营地液化地基振冲碎石桩加固处理技术[J]. raybet体育在线院报, 2009, 26(3):28-31.
[6] 侯卫强. 振冲碎石桩在水利工程中的应用[J]. 科技资讯, 2009(22):10.
[7] 卢鹏云, 张广彪, 臧成新. 实时过程数据在振冲碎石桩全过程质量控制中的应用[J]. 地基处理, 2020(5): 404-413.
[8] 王俊智,杜朋召,牛兆轩.基于K-means聚类方法和I Index聚类有效性检验指标的岩体结构面自动分组及应用[J]. raybet体育在线院报,2018,35(9):109-113,120.
[9] 曹继飞, 盖瑜. 模糊聚类分析在石油钻井地层分类中的应用[J]. 西部探矿工程, 2013, 25(9):57-59.
[10] 张幼振, 张宁, 邵俊杰, 等. 基于钻进参数聚类的含煤地层岩性模糊识别[J]. 煤炭学报, 2019, 44(8): 2328-2335.
[11] 谭卓英, 王思敬, 蔡美峰. 岩土工程界面识别中的地层判别分类方法研究[J]. 岩石力学与工程学报, 2008, 27(2):316-322.
[12] 李建斌, 郑赢豪, 荆留杰, 等. 基于岩体聚类分级的TBM掘进参数预测方法[J]. 岩石力学与工程学报, 2020, 39(增刊2): 3326-3337.
[13] 米允龙, 米春桥, 刘文奇. 海量数据挖掘过程相关技术研究进展[J]. 计算机科学与探索, 2015, 9(6): 641-659.
[14] 张忠平, 刘伟雄, 张玉停, 等. ERDOF: 基于相对熵权密度离群因子的离群点检测算法[J]. 通信学报, 2021, 42(9): 133-143.
[15] DUNN J. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-separated Clusters[J]. Journal of Cybernet, 1974,3: 32-57.
[16] BEZDEK J C, EHRLICH R, FULL W. FCM: The Fuzzy c-Means Clustering Algorithm[J]. Computers & Geosciences, 1984, 10(2/3): 191-203.
[17] BU F. An Efficient Fuzzy C-Means Approach Based on Canonical Polyadic Decomposition for Clustering Big Data in IoT[J]. Future Generation Computer Systems, 2018, 88: 675-682.
[18] PAL N R, BEZDEK J C. On Cluster Validity for the Fuzzy C-Means Model[J]. IEEE Transactions on Fuzzy Systems, 1995, 3(3): 370-379.
[19] DING Y, FU X. Kernel-Based Fuzzy C-Means Clustering Algorithm Based on Genetic Algorithm[J]. Neurocomputing, 2016, 188: 233-238.
[20] MANIMALA K, DAVID I G, SELVI K. A Novel Data Selection Technique Using Fuzzy C-Means Clustering to Enhance SVM-based Power Quality Classification[J]. Soft Computing:A Fusion of Foundations, Methodologies and Applications, 2015, 19(11): 3123-3144.
[21] 胡贵良, 魏永新, 刘保柱, 等. 超深振冲碎石桩施工技术及应用[J]. 水力发电, 2020, 46(11):76-80.
[22] 朱连江, 马炳先, 赵学泉. 基于轮廓系数的聚类有效性分析[J]. 计算机应用, 2010(增刊2): 139-141, 198.