基于图像识别的碎屑流颗粒分布特征及碎屑流与房屋相互作用探究

彭双麒; 柯灵; 郑体; 徐继忠

doi:10.19509/j.cnki.dzkq.2021.0622

基于图像识别的碎屑流颗粒分布特征及碎屑流与房屋相互作用探究

doi: 10.19509/j.cnki.dzkq.2021.0622

详细信息

作者简介:
彭双麒(1995-), 男, 助理工程师, 主要从事岩土工程与地基基础相关研究工作。E-mail: 2694253535@qq.com

中图分类号: X43
计量
- 文章访问数: 750
- PDF下载量: 207
- 被引次数: 0
出版历程
- 收稿日期: 2021-01-20

Particle distribution characteristics of rock avalanche and the interaction between rock avalanche and houses based on image recognition

摘要

摘要: 由于崩滑-碎屑流具有高隐蔽性、发生时间短暂以及较低的可预见性，较难直观观测崩滑-碎屑流发生的过程。为了对碎屑动力特性及对房屋的破坏情况进行研究，拟对碎屑流堆积体及其粒径分布进行分析。以普洒村崩塌-碎屑流为例，使用PCAS系统对碎屑流堆积体图像进行颗粒识别、统计，并通过量纲分析法，分析堆积体颗粒与房屋破坏之间的定量联系。分析结果发现：与现场筛分统计相比，图像识别堆积体颗粒的方法得到的数据更全面、详细，并且能节省大量的人力、物力资源。另外，对房屋与碎屑流之间的相互作用进行了探究，发现房屋对碎屑流颗粒有"拦粗排细"的作用。同时，利用图像识别得到的颗粒粒径数据对推导出房屋破坏的判别公式，判别效果较好，能够在地质灾害防治、预测领域发挥一定作用。
- 崩滑-碎屑流 /
- 堆积体 /
- 图像识别 /
- 颗粒粒径 /
- 房屋破坏
Abstract: Due to the high concealment, short-lived time and low predictability of the rock avalanche, it is difficult to visually observe the process of it. In order to study the dynamic characteristics and the catastrophicto houses of rock avalanche, this paper analyzes the rock avalanche congeries and its particle size distribution.Taking the rock avalanche in Pusa Village as an example, this paper uses the PCAS system to identify congeries particles by image.And through the dimensional analysis method, the quantitative relationship between the congeries of particles and the damage of the building is analyzed.The author found that: compared with on-site screening statistics, the data obtained by the method of image recognition of congeries particles is more comprehensive and detailed, and can save a lot of manpower and material resources.In addition, the interaction between the house and the rock avalanche is explored, and it is found that the house has the effect of "blocking coarse and fine discharge" on the rock avalanche particles. At the same time, using the particle size data obtained by image recognition to derive the discrimination formula for building damage, the discriminating effect is good, laying the foundation for follow-up research.
- slump rock avalanche /
- congeries /
- image identification /
- particle size /
- house damage

HTML全文

图 1 普洒村崩塌堆积区全貌

Figure 1. Whole picture of the collapse congeries of Pusa Village

下载: 全尺寸图片幻灯片

图 2 普洒村崩塌堆积体工程地质剖面图

T₁y.下三叠统夜郎组；P₂c-d.上二叠统长兴组-大隆组；P₂l.上二叠统龙潭组

Figure 2. Engineering geological section of collapse congerie in Pusa Village

下载: 全尺寸图片幻灯片

图 3 PCAS系统图像识别堆积体粒径过程及结果

Figure 3. Image recognition process and results of particle size of PCAS system

下载: 全尺寸图片幻灯片

图 4 崩塌堆积分区示意图

An/Bn/Cn. 崩塌堆积分区代号

Figure 4. Partition diagram of the collapse congeries body

下载: 全尺寸图片幻灯片

图 5 堆积体纵向区域粒径数统计累计曲线

Figure 5. Cumulative curve of particle size statistics in longitudinal area of congeries

下载: 全尺寸图片幻灯片

图 6 垂直于主滑方向区域颗粒粒径累计曲线

Figure 6. Aggregate curve of particle size perpendicular to the main sliding direction

下载: 全尺寸图片幻灯片

图 7 各区域不同粒径范围所占比例

Figure 7. Proportion of different particle size grades in each area

下载: 全尺寸图片幻灯片

图 8 房屋平面位置分布图

Figure 8. Location of the house

下载: 全尺寸图片幻灯片

图 9 垂直于主滑方向各区域不同粒径范围颗粒所占比例

堆积后部区对应B1、A2、C1区；堆积中部区对应B2、A5、C2区；堆积前缘区对应B3、A8、C3区

Figure 9. Proportion of different particle size grades in each area perpendicular to the main sliding direction

下载: 全尺寸图片幻灯片

图 10 普洒村崩塌碎屑流房屋破坏的判据图

Figure 10. Criterion of the destruction of houses in the rock avalanche in Pusa Village

下载: 全尺寸图片幻灯片

表 1 破坏房屋距崩塌源中部的水平距离以及距崩塌物源高度

Table 1. Horizontal distance of the damaged house from the middle of the collapse source and the height of the damaged house from collapse source

房屋编号	运动距离L/m	高差ΔH/m	房屋编号	运动距离L/m	高差ΔH/m	房屋编号	运动距离L/m	高差ΔH/m
1	613.16	249	16	566.45	242	31	624.43	260
2	645.78	249	17	547.55	240	32	718.49	272
3	678.98	254	18	535.23	241	33	703.67	271
4	682.24	254	19	522.37	238	34	685.68	270
5	658.27	254	20	587.46	249	35	694.40	271
6	639.84	253	21	581.23	249	36	681.21	270
7	641.44	254	22	598.17	251	37	659.85	268
8	708.25	257	23	531.15	239	38	769.52	278
9	558.77	243	24	603.83	250	39	749.19	276
10	574.57	245	25	537.39	246	40	700.61	271
11	592.31	247	26	530.11	246	41	530.47	240
12	582.02	246	27	517.92	246	42	500.80	237
13	559.99	242	28	517.11	244	43	523.11	238
14	602.69	251	29	599.63	256	44	663.77	268
15	578.72	242	30	619.62	258	45	459.26	241

下载: 导出CSV

表 2 普洒村崩塌冲毁房屋判别结果

Table 2. Discriminating results of the houses damaged by collapse in Pusa Village

房屋编号	判别结果F	房屋编号	判别结果F	房屋编号	判别结果F	房屋编号	判别结果F	房屋编号	判别结果F
1	1.27	10	1.42	19	1.67	28	1.75	37	1.18
2	1.14	11	1.35	20	1.38	29	1.36	38	0.9
3	1.05	12	1.39	21	1.41	30	1.29	39	0.94
4	1.04	13	1.48	22	1.34	31	1.28	40	1.06
5	1.12	14	1.32	23	1.62	32	1.01	41	1.63
6	1.18	15	1.38	24	1.31	33	1.05	42	1.81
7	1.18	16	1.44	25	1.63	34	1.1	43	1.66
8	0.98	17	1.53	26	1.68	35	1.08	44	1.16
9	1.49	18	1.61	27	1.75	36	1.11	45	2.19

下载: 导出CSV

参考文献(29)

[1]	黄润秋. 20世纪以来中国的大型滑坡及其发生机制[J]. 岩石力学与工程学报, 2007, 26(3): 433-454. doi: 10.3321/j.issn:1000-6915.2007.03.001 Huang R Q. Large-scale landslides and their sliding mechanisms in China since the 20th century[J]. Chinese Journal of Rock Mechanics and Engineering, 2007, 26(3): 433-454(in Chinese with English abstract). doi: 10.3321/j.issn:1000-6915.2007.03.001
[2]	许强, 黄润秋, 殷跃平, 等. 2009年6·5重庆武隆鸡尾山崩滑灾害基本特征与成因机理初步研究[J]. 工程地质学报, 2009, 17(4): 433-444. doi: 10.3969/j.issn.1004-9665.2009.04.001 Xu Q, Huang R Q, Ying Y P, et al. The Jiweishan landslide of June 5, 2009 in Wulong, Chongqing: Characteristics and failure mechanism[J]. Journal of Engineering Geology, 2009, 17(4): 433-444(in Chinese with English abstract). doi: 10.3969/j.issn.1004-9665.2009.04.001
[3]	许强, 李为乐, 董秀军, 等. 四川茂县叠溪镇新磨村滑坡特征与成因机制初步研究[J]. 岩石力学与工程学报, 2017, 36(11): 2612-2628. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201711002.htm Xu Q, Li W L, Dong X J et al. The Xinmocun landslide on June 24, 2017 in Maoxian, Sichuan: Characteristics and failure mechanism[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(11): 2612-2628(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201711002.htm
[4]	彭双麒, 许强, 郑光, 等. 碎屑流堆积物粒度分布与运动特性的关系: 以贵州纳雍普洒村崩塌为例[J]. 水文地质工程地质, 2018, 45(4): 129-136. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201804019.htm Peng S Q, Xu Q, Zheng G, et al. Realationship between particle size distribution and movement characteristics of rock avalanche deposits in Nayongpusa Village, Guizhou Province[J] Hydrogeology & Engineering Geology, 2018, 45(4): 129-136(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201804019.htm
[5]	郑光, 许强, 巨袁臻, 等. 2017年8月28日贵州纳雍县张家湾镇普洒村崩塌特征与成因机理研究[J]. 工程地质学报, 2018, 26(1): 223-240. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201801023.htm Zheng G, Xu Q, Ju Y Z, et al. The Pusacun rock avalanche on August 28, 2017 in Zhangjiawan Nayongxian, Guizhou: Characteristics and failure mechanism[J]. Journal of Engineering Geology, 2018, 26(1): 223-240(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201801023.htm
[6]	彭双麒, 许强, 郑光, 等. 白格滑坡-碎屑流堆积体颗粒识别与分析[J]. 水利水电技术, 2020, 51(2): 144-154. https://www.cnki.com.cn/Article/CJFDTOTAL-SJWJ202002017.htm Peng S Q, Xu Q, Zheng G, et al. Recognition and analysis of deposit body grain of Baige Landslide-Debris Flow[J] Water Resources and Hydropower Engineering, 2020, 51(2): 144-154(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SJWJ202002017.htm
[7]	Li H J, Xu Q, He Y. et al. Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models[J]. Landslides, 2018, 15(10): 2047-2059. doi: 10.1007/s10346-018-1020-2
[8]	覃瀚萱, 桂蕾, 余玉婷, 等. 基于滑坡灾害预警分级的应急处置措施[J]. 地质科技情报, 2021, 40(4): 187-195. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202104018.htm Qin H X, Gui L, Yu Y T, et al. Emergency measures based on early warning classification of landslide[J]. Bulletin of Geological Science and Technology, 2021, 40(4): 187-195(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202104018.htm
[9]	吴益平, 唐辉明, 殷坤龙. 物元模型在滑坡灾害风险预测中的应用[J]. 地质科技情报, 2003, 22(4): 96-100. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ200304018.htm Wu Y P, Tang H M, Yin K L. Application of matter-element model in landslide hazard risk assessment[J]. Geological Science and Technology Information, 2003, 22(4): 96-100(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ200304018.htm
[10]	Yang Q, Cai F, Su Z, et al. Numerical simulation of granular flows in a Large Flume using discontinuous deformation analysis[J]. Rock Mechanics and Rock Engineering, 2014, 47(6): 2299-2306. doi: 10.1007/s00603-013-0489-1
[11]	Yuan R M, Tang C I, Hu J C, et al. Mechanism of the Donghekou landslide triggered by the 2008 Wenchuan earthquake revealed by discrete element modeling[J]. Natural Hazards and Earth System Sciences, 2014, 14(6): 1195-1205. http://www.onacademic.com/detail/journal_1000040545889410_b1b1.html
[12]	Yu X, Chen X. Variational laws of debris flow impact force on the Check Dam surface based on orthogonal experiment design[J]. Geotechnical and Geological Engineering, 2017, 35(6): 2511-2522. doi: 10.1007/s10706-017-0258-0
[13]	毕钰璋, 何思明, 王东坡, 等. 碎屑流冲击下的桥墩动力响应特征分析[J]. 中国地质灾害与防治学报, 2017, 28(4): 16-21. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201704004.htm Bi Y Z, He S M, Wang D P, et al. Discrete-element investigation of rock avalanches impact on the bridge pier[J]. The Chinese Journal of Geological Hazard and Control, 2017, 28(4): 16-21(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201704004.htm
[14]	Jiang Y J, Zhao Y, Towhata I, et al. Influence of particle characteristics on impact event of dry granular flow[J]. Powder Technology, 2015, 270(A): 53-67. http://www.researchgate.net/profile/Yuan-Jun_Jiang/publication/267100618_Influence_of_particle_characteristics_on_impact_event_of_dry_granular_flow/links/54f5893f0cf2f28c1364e99c.pdf
[15]	Jiang Y J, Towhata I. Experimental study of dry granular flow and impact behavior against a rigid retaining wall[J]. Rock Mechanics and Rock Engineering, 2013, 46(4): 713-729. doi: 10.1007/s00603-012-0293-3
[16]	Adel A, Stéphane L, Thierry F. Dry granular avalanche impact force on a rigid wall: Analytic shock solution versus discrete element simulations[J]. Physical Review E, 2018, 97(5): 052903. doi: 10.1103/PhysRevE.97.052903
[17]	赵辉. 粗颗粒堆积体塌滑蔓延试验与遮挡措施研究[D]. 北京: 北京交通大学, 2018. Zhao H. Experimental studies on sliding and scattering characteristics of coarse-aggregate soil mass and blocking measures of sliding soil mass[D]. Beijing: Beijing Jiaotong University, 2018(in Chinese with English abstract).
[18]	王品, 徐则民. 头寨大型高速远程滑坡碎屑流堆积体的粒度组成[J]. 山地学报, 2013, 31(6): 745-752. doi: 10.3969/j.issn.1008-2786.2013.06.014 Wang P, Xu Z M. The grain size composition of Touzhai rock-avalanche deposits[J]. Journal of Mountain Science, 2013, 31(6): 745-752(in Chinese with English abstract). doi: 10.3969/j.issn.1008-2786.2013.06.014
[19]	Zhang L M, Xu Y, Huang R Q, et al. Particle flow and segregation in a giant landslide event triggered by the 2008 Wenchuan earthquake, Sichuan, China[J]. Natural Hazards & Earth System Sciences, 2011, 11(4): 1153-1162. http://www.onacademic.com/detail/journal_1000040545869710_3773.html
[20]	徐丽坤, 刘晓东, 向小翠. 基于深度信念网络的遥感影像识别与分类[J]. 地质科技情报, 2017, 36(4): 244-249. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201704032.htm Xu L K, Liu X D, Xiang X C. Recognition and classification for remote sensing image based on depth belief network[J]. Geological Science and Technology Information, 2017, 36(4): 244-249(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201704032.htm
[21]	Hitzl A P, Jörres R A, Heinemann F, et al. Aerial photography collected with a multirotor drone reveals impact of Eurasian beaver reintroduction on ecosystem structure[J]. Journal of Unmanned Vehicle Systems, 2015, 3(3): 123-130. doi: 10.1139/juvs-2015-0005
[22]	Xu Q, Li H J, He Y, et al. Comparison of data-driven models of loess landslide runout distance estimation[J]. Bulletin of Engineering Geology and the Environment, 2019, 78: 1281-1294. doi: 10.1007/s10064-017-1176-3
[23]	Jiao K, Yao S, Liu C, et al. The characterization and quantitative analysis of nanopores in unconventional gas reservoirs utilizing FESEM-FIB and image processing: An example from the lower Silurian Longmaxi Shale, upper Yangtze region, China[J]. International Journal of Coal Geology, 2014, 128/129(3): 1-11. http://www.sciencedirect.com/science/article/pii/S0166516214000664
[24]	Liu Y, Wu L. Geological disaster recognition on optical remote sensing images using deep learning[J]. Procedia Computer Science, 2016, 91: 566-575. doi: 10.1016/j.procs.2016.07.144
[25]	彭双麒, 许强, 李骅锦, 等. 基于高精度图像识别的堆积体粒径分析[J]. 工程地质学报, 2019, 27(6): 1290-1301. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201906011.htm Peng S Q, Xu Q, Li H J, et al. Grain size distribution analysis of landslide deposits with reliable image identification[J]. Journal of Engineering Geology, 2019, 27(6): 1290-1301(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201906011.htm
[26]	Liu C, Shi B, Zhou J. Quantification and characterization of microporosity by image processing, geometric measurement and statistical methods: Application on SEM images of clay materials[J]. Applied Clay Science, 2011, 54(1): 97-106. doi: 10.1016/j.clay.2011.07.022
[27]	Lin Q, Yan J, Zhou J, et al. Microstructure study on intact clay behavior subjected to cyclic principal stress rotation[J]. Procedia Engineering, 2016, 143: 991-998. doi: 10.1016/j.proeng.2016.06.088
[28]	Liu C, Pollard D D, Aydin A, et al. Mechanism of formation of wiggly compaction bands in porous sandstone: Observations and conceptual model[J]. Journal of Geophysical Research, 2015, 120(12): 8138-8152. doi: 10.1002/2015JB012374
[29]	彭双麒. 滑坡-碎屑流堆积体粒度分布研究[D]. 成都: 成都理工大学, 2020. Peng S Q. The study for grain size distribution of rock avalanche deposit[D]. Chengdu: Chengdu University of Technology, 2020 (in Chinese with English abstract).