基于模糊聚类最大树算法的高层建筑基坑工程风险识别方法

doi:10.13229/j.cnki.jdxbgxb20211278

摘要/Abstract

摘要：

针对基坑工程勘察局限性和工程临时性等实质问题，提出了基于模糊聚类最大树算法的高层建筑基坑工程风险识别方法。通过绝对值减数法建立了模糊相似矩阵，标准化处理风险指标数据，使用风险评价指数划分基坑施工风险等级。利用模糊聚类定量计算影响元素之间的耦合关系，运用无向连通赋权图形式描述最大树结构，通过连接树结构内互相连通的顶点实现风险识别。仿真结果证明：本文方法能够在判定基坑工程风险等级的同时，准确描述风险的具体位置，识别正确率和效率均可达到预期要求，鲁棒性强。

关键词: 基坑施工, 模糊聚类, 最大树算法, 风险识别, 评价指标

Abstract:

In view of the limitations of foundation pit engineering investigation and the temporary nature of the project， a risk identification method of high-rise building foundation pit engineering based on fuzzy clustering maximum tree algorithm was proposed. The fuzzy similarity matrix was established by the absolute value subtraction method， the risk index data was standardized， and the risk evaluation index was used to divide the risk level of foundation pit construction. Using fuzzy clustering to quantitatively influence the coupling relationship between elements， the maximum tree structure in the form of undirected connected weight graph was described， and the risk identification was realized through the connected vertices in the connection tree structure. The simulation results show that the proposed method can accurately describe the specific location of the risk while determining the risk level of foundation pit engineering， and the recognition accuracy and efficiency can meet the expected requirements with strong robustness.

Key words: foundation pit construction, fuzzy clustering, maximum tree algorithm, risk identification, evaluating indicator

中图分类号:

TU94

廖汉超,单汨源. 基于模糊聚类最大树算法的高层建筑基坑工程风险识别方法[J]. 吉林大学学报(工学版), 2022, 52(12): 2892-2897.

Han-chao LIAO,Mi-yuan SHAN. Risk identification method of foundation pit engineering of high⁃rise buildings based on fuzzy clustering maximum tree algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2892-2897.

图/表 6

图1

表1

表2

图2

图3

图4

参考文献 20

1	钟春玲,梁东,张云龙,等. 体外预应力钢-混凝土组合简支梁自振频率计算[J].吉林大学学报:工学版,2020,50(6):2159-2166.
	Zhong Chun-ling, Liang Dong, Zhang Yun-long, et al. Calculation of natural frequency of externally prestressed steel-concrete composite simply supported beams[J]. Journal of Jilin University(Engineering and Technology Edition), 2020,50(6):2159-2166.
2	钱雪忠,姚琳燕. 面向稀疏高维大数据的扩展增量模糊聚类算法[J]. 计算机工程,2019,45(6):75-81, 88.
	Qian Xue-zhong, Yao Lin-yan. Extended incremental fuzzy clustering algorithm for sparse high-dimensional big data[J]. Computer Engineering, 2019,45(6):75-81, 88.
3	Ren X, Fan W, Li J, et al. Building information model-based finite element analysis of high-rise building community subjected to extreme earthquakes[J]. Advances in Structural Engineering, 2019, 22(4):971-981.
4	王飞球,黄健陵,符竞,等. 基于BP神经网络的跨既有线高速铁路桥梁施工安全风险评估[J].铁道科学与工程学报,2019,16(5):1129-1136.
	Wang Fei-qiu, Huang Jian-ling, Fu Jing, et al. Risk assessment of construction safety of high-speed railway bridge across existing lines based on BP neural network[J]. Journal of Railway Science and Engineering, 2019,16(5):1129-1136.
5	华莹,何军,赵金城. 高层建筑施工现场危险区域识别及评估方法研究[J].施工技术,2019,48(6):100-104.
	Hua Ying, He Jun, Zhao Jin-cheng. Research on identification and evaluation method of hazardous area of high-rise building construction site[J]. Construction Technology, 2019, 48(6):100-104.
6	Nakaguro M, Sato Y, Tada Y, et al. Prognostic implication of histopathologic indicators in salivary duct carcinoma: proposal of a novel histologic risk stratification model[J]. American Journal of Surgical Pathology, 2019, 44(4):526-535.
7	荀志远,张丽敏,徐瑛莲,等. 基于组合赋权云模型的装配式建筑安全风险评价[J]. 数学的实践与认识,2020,50(7):302-310.
	Xun Zhi-yuan, Zhang Li-min, Xu Ying-lian, et al. Evaluation of prefabricated buildings safety risk based on combination weighting and cloud model[J]. Mathematics in Practice and Theory, 2020,50(7):302-310.
8	Wang W Y, Wang Y. Diagnosis index system setup for implementation status management in large-scale construction projects[J]. Mathematical Problems in Engineering, 2021(1): 1-9.
9	郭文娟. 大数据背景下的房屋建筑施工风险评估模型[J]. 科技通报,2019,35(4):198-201.
	Guo Wen-juan. Risk assessment model for building construction under the background of big data[J]. Bulletin of Science and Technology, 2019,35(4):198-201.
10	Chen W, Zhang G, Jiao Y, et al. Unascertained measure-set pair analysis model of collapse risk evaluation in mountain tunnels and its engineering application[J]. KSCE Journal of Civil Engineering, 2020, 25(6):1-17.
11	李蒙,邹健,刘苹,等. 基于BIM的隧道施工安全风险辨识模型研究[J].工业安全与环保,2019,45(5):69-71.
	Li Meng, Zou Jian, Liu Ping, et al. Research on tunnel construction safety risk identification model based on BIM[J]. Industrial Safety and Environmental Protection, 2019,45(5):69-71.
12	Lu Y, Gong P, Tang Y, et al. BIM-integrated construction safety risk assessment at the design stage of building projects[J]. Automation in Construction, 2021, 124(2): No.103553.
13	黄俊斌,张国维,闫肃,等. 基于物联网技术的建筑火灾风险动态评估[J]. 消防科学与技术,2020,39(10):1371-1375.
	Huang Jun-bin, Zhang Guo-wei, Yan Su, et al. Dynamic assessment of building fire risk based on internet of things technology[J]. Fire Science and Technology, 2020,39(10):1371-1375.
14	Baat M, Wieringa N, Droge S, et al. Smarter sediment screening: effect-based quality assessment, chemical profiling, and risk identification[J]. Environmental Science and Technology, 2019, 53(24):14479-14488.
15	吴贤国,冯宗宝,秦文威,等. 基于物元理论和证据理论的盾构隧道施工邻近建筑物风险评价[J].铁道标准设计,2020,64(4):104-110.
	Wu Xian-guo, Feng Zong-bao, Qin Wen-wei, et al. Risk assessment of adjacent buildings induced by shield tunneling construction based on matter-element theory and extension theory[J]. Railway Standard Design, 2020,64(4):104-110.
16	Man S S, Chan A, Alabdulkarim S. Quantification of risk perception: development and validation of the construction worker risk perception(CoWoRP) scale[J]. Journal of Safety Research, 2019, 71:25-39.
17	Yang J, Wang J, Wei G, et al. An adaptive probabilistic mapping matrix search algorithm for vulnerability analysis of PPS[J]. Annals of Nuclear Energy, 2019, 131:433-442.
18	李依霖. 复杂网络隐私信息传输入侵风险评估仿真[J].计算机仿真,2020,37(6):156-159, 164.
	Li Yi-lin. Intrusion risk assessment simulation of big data privacy information transmission in complex network[J]. Computer Simulation, 2020,37(6):156-159, 164.
19	Kayhan B M, Cebi S, Kahraman C. Determining and prioritizing main factors of supplier reliability in construction industry[J]. Journal of Multiple-valued Logic and Soft Computing, 2019, 32(1/2):111-134.
20	Stewart M G, Li J. Risk-based assessment of blast-resistant design of ultra-high performance concrete columns[J]. Structural Safety, 2021, 88(3):No.102030.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

风险评价系数取值	施工风险水平	风险级别
0~0.2	微弱风险	1
0.2~0.4	弱风险	2
0.4~0.6	中风险	3
0.6~0.8	强风险	4
0.8~1.0	高强风险	5

监测项目指标	桩体水平位移		地表沉降		地下水位
监测项目指标	累积量/mm	速率/（mm·d^?1）	累积量/mm	速率/（mm·d^?1）	累积量/mm	速率/（mm·d^?1）
监测值	36.52	0.61	15.43	0.94	1084	254
指标风险概率	0.93	0.04	0.15	0.11	0.13	0.11
监测项风险概率	0.743		0.115		0.156
总体风险概率	0.548

[1]	薛锋,何传磊,黄倩,罗建. 多式轨道交通网络的耦合协调度[J]. 吉林大学学报(工学版), 2021, 51(6): 2040-2050.
[2]	董伟智,张爽,朱福. 基于可拓层次分析法的沥青混合料路用性能评价[J]. 吉林大学学报(工学版), 2021, 51(6): 2137-2143.
[3]	王喆, 杨柏婷, 刘昕, 刘群, 宋现敏. 基于模糊聚类的驾驶决策判别[J]. 吉林大学学报(工学版), 2015, 45(5): 1414-1419.
[4]	司建波, 姚燕, 郭蔚莹, 杨芳. 基于模糊聚类的Web用户聚类方法与实现[J]. 吉林大学学报(工学版), 2013, 43(增刊1): 485-488.
[5]	吉淑娇, 朱明, 胡汉平, 邢笑雪. 基于特征匹配的视频稳像算法[J]. 吉林大学学报(工学版), 2013, 43(增刊1): 322-325.
[6]	王彦会, 郭孔辉, 卢炳武, 应国增, 秦民. 基于多元统计分析的车辆操纵稳定性客观评价[J]. 吉林大学学报(工学版), 2011, 41(增刊2): 6-11.
[7]	申铉京^1,2,王开业^1,2,千庆姬³,刘英杰⁴,李想⁴. 基于自适应气球力Snake模型的图像分割[J]. 吉林大学学报(工学版), 2011, 41(05): 1394-1400.
[8]	周显国,陈大可,苑森淼. 基于改进模糊聚类分析的医学脑部MRI图像分割[J]. 吉林大学学报(工学版), 2009, 39(增刊2): 381-0385.
[9]	姜桂艳，郭海锋，吴超腾 . 基于感应线圈数据的城市道路交通状态判别方法 [J]. 吉林大学学报(工学版), 2008, 38(增刊): 37-0042.
[10]	张建国，雷雨龙，王健，陆晓惠 . 基于BP神经网络的换档品质评价方法[J]. 吉林大学学报(工学版), 2007, 37(05): 1019-1022.
[11]	邱长波，张佳，施梦. 企业信息化成熟度阶段分类模型[J]. 吉林大学学报(工学版), 2007, 37(04): 976-980.
[12]	徐新卫，周良，徐晓明，丁秋林 . Web主动服务中基于混合挖掘的用户意图辨识[J]. 吉林大学学报(工学版), 2007, 37(02): 419-0423.
[13]	鹿应荣，杨印生，吕锋 . 基于模糊聚类分析的车辆优化调度[J]. 吉林大学学报(工学版), 2006, 36(增刊2): 147-151.
[14]	马飞, 王利政. 基于内在结构的企业核心竞争力评价体系[J]. 吉林大学学报(工学版), 2004, (1): 163-167.