基于滑坡致灾强度预测的建筑物易损性定量评价

曾韬睿; 殷坤龙; 桂蕾; 金必晶; 刘谢攀; 刘真意; 郭子正; 蒋宏伟; 邬礼扬

doi:10.3799/dqkx.2022.429

Volume 48 Issue 5

May 2023

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2023 > 48(5): 1807-1824

Zeng Taorui, Yin Kunlong, Gui Lei, Jin Bijing, Liu Xiepan, Liu Zhenyi, Guo Zizheng, Jiang Hongwei, Wu Liyang, 2023. Quantitative Vulnerability Analysis of Buildings Based on Landslide Intensity Prediction. Earth Science, 48(5): 1807-1824. doi: 10.3799/dqkx.2022.429

Citation:

Zeng Taorui, Yin Kunlong, Gui Lei, Jin Bijing, Liu Xiepan, Liu Zhenyi, Guo Zizheng, Jiang Hongwei, Wu Liyang, 2023. Quantitative Vulnerability Analysis of Buildings Based on Landslide Intensity Prediction. Earth Science, 48(5): 1807-1824. doi: 10.3799/dqkx.2022.429

Citation:

Zeng Taorui, Yin Kunlong, Gui Lei, Jin Bijing, Liu Xiepan, Liu Zhenyi, Guo Zizheng, Jiang Hongwei, Wu Liyang, 2023. Quantitative Vulnerability Analysis of Buildings Based on Landslide Intensity Prediction. Earth Science, 48(5): 1807-1824. doi: 10.3799/dqkx.2022.429

PDF( 14843 KB)

Quantitative Vulnerability Analysis of Buildings Based on Landslide Intensity Prediction

doi: 10.3799/dqkx.2022.429

1.
Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China
2.
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
3.
School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China
4.
School of Environmental and Safety Engineering, Changzhou University, Changzhou 213164, China

Received Date: 2022-09-23
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

In view of the lack of research about landslide intensity prediction in the current quantitative vulnerability analysis of buildings, in this paper it innovatively proposes a quantitative analysis method of the combination of intensity empirical curve based on InSAR technology and spatial displacement prediction of secondary development of ABAQUS.Taking the Shilongmen landslide in the Three Gorges Reservoir area as an example, the PS-InSAR method was adopted to calculate the cumulative displacement of the landslide in 2017‒2020 and obtained the empirical curve of landslide intensity. The ABAQUS software was used to compile the subroutine of load and pore water pressure to calculate the cumulative displacement under extreme scenarios (reservoir water level drop with heavy rainfall) and predicted the vulnerability of buildings. The evaluation system of resistance was constructed by weighting eight indicators of PSO-Fuzzy AHP model, which can be combined with the landslide intensity to quantitatively evaluate the vulnerability of buildings. The results indicate follows: (1) The evaluation system of resistance proposed in this paper can well present the structural characteristics of rural buildings in the Three Gorges Reservoir area, and has high evaluation accuracy. (2) The retrieved upper-intensity curve obtained by PS-InSAR is I_pu=0.065×D_tot^0.236 which has higher prediction accuracy and effectively reduces false-negative errors. (3) The landslide intensity of extreme conditions simulated by ABAQUS increases with the increase of rainfall, the predicted vulnerability level of buildings increases, and the buildings with obvious deformation in the previous investigation are successfully warned. It is concluded that the landslide intensity prediction method and vulnerability analysis method proposed in this paper has high spatial identification and early warning accuracy, and real-time vulnerability mapping of buildings can be obtained through landslide intensity information.
- landslide intensify,
- vulnerability,
- building,
- PS-InSAR,
- ABAQUS,
- hazard geology

FullText(HTML)

References(57)

References

Agliata, R., Bortone, A., Mollo, L., 2021. Indicator-Based Approach for the Assessment of Intrinsic Physical Vulnerability of the Built Environment to Hydro- Meteorological Hazards: Review of Indicators and Example of Parameters Selection for a Sample Area. International Journal of Disaster Risk Reduction, 58: 102199. doi: 10.1016/j.ijdrr.2021.102199

Bera, S., Guru, B., Oommen, T., 2020. Indicator-Based Approach for Assigning Physical Vulnerability of the Houses to Landslide Hazard in the Himalayan Region of India. International Journal of Disaster Risk Reduction, 50: 101891. doi: 10.1016/j.ijdrr.2020.101891

Bianchini, S., Pratesi, F., Nolesini, T., et al., 2015. Building Deformation Assessment by Means of Persistent Scatterer Interferometry Analysis on a Landslide- Affected Area: The Volterra (Italy) Case Study. Remote Sensing, 7(4): 4678-4701. doi: 10.3390/rs70404678

Cascini, L., Fornaro, G., Peduto, D., 2010. Advanced Low- and Full-Resolution DInSAR Map Generation for Slow-Moving Landslide Analysis at Different Scales. Engineering Geology, 112(1-4): 29-42. doi: 10.1016/j.enggeo.2010.01.003

Cruden, D. M., Varnes, D. J., 1996. Landslide Types and Processes. In: Landslide Investigation and Mitigation. National Academy Press, Washington, 36-75.

Dai, C., Li, W., Wang, D., et al., 2021. Active Landslide Detection Based on Sentinel-1 Data and InSAR Technology in Zhouqu County, Gansu Province, Northwest China. Journal of Earth Science, 32(5): 1092-1103. doi: 10.1007/s12583-020-1380-0

Del Soldato, M., Bianchini, S., Calcaterra, D., et al., 2017. A New Approach for Landslide-Induced Damage Assessment. Geomatics, Natural Hazards and Risk, 8(2): 1524-1537. doi: 10.1080/19475705.2017.1347896

Du, J., 2012. Risk Assessment of Individual Landslide (Dissertation). China University of Geosciences, Wuhan (in Chinese with English abstract).

Fell, R., Corominas, J., Bonnard, C., et al., 2008. Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning. Engineering Geology, 102(3-4): 85-98. doi: 10.1016/j.enggeo.2008.03.022

Ferretti, A., Prati, C., Rocca, F., 2000. Nonlinear Subsidence Rate Estimation Using Permanent Scatterers in Differential SAR Interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(5): 2202-2212. doi: 10.1109/36.868878

Guo, Z., Chen, L., Yin, K., et al., 2020. Quantitative Risk Assessment of Slow-Moving Landslides from the Viewpoint of Decision-Making: A Case Study of the Three Gorges Reservoir in China. Engineering Geology, 273: 105667. doi: 10.1016/j.enggeo.2020.105667

Guo, Z. Z., Yin, K. L., Tang, Y., et al., 2017. Stability Evaluation and Prediction of Maliulin Landslide under Reservoir Water Level Decline and Rainfall. Geological Science and Technology Information, 36(4): 260-265, 270 (in Chinese with English abstract). http://www.researchgate.net/publication/324390103_Stability_Evaluation_and_Prediction_of_Maliulin_Landslide_under_Reservoir_Water_Decline_and_Rainfall

Huang, F., Huang, J., Jiang, S., et al., 2017. Landslide Displacement Prediction Based on Multivariate Chaotic Model and Extreme Learning Machine. Engineering Geology, 218: 173-186. doi: 10.1016/j.enggeo.2017.01.016

Huang, F. M., Cao, Y., Fan, X. M., et al., 2021a. Effects of Different Landslide Boundaries and Their Spatial Shapes on the Uncertainty of Landslide Susceptibility Prediction. Chinese Journal of Rock Mechanics and Engineering, 40(S02): 3227-3240 (in Chinese with English abstract).

Huang, F. M., Chen, J. W., Tang, Z. P., et al., 2021b. Uncertainties of Landslide Susceptibility Prediction Due to Different Spatial Resolutions and Different Proportions of Training and Testing Datasets. Chinese Journal of Rock Mechanics and Engineering, 40(6): 1155-1169 (in Chinese with English abstract).

Huang, F. M., Chen, B., Mao, D. X., et al., 2023. Landslide Susceptibility Prediction Modeling and Interpretability Based on Self-Screening Deep Learning Model. Earth Science, 48(5): 1696-1710 (in Chinese with English abstract).

Huang, F. M., Chen, J. W., Fan, X. M., et al., 2022a. Logistic Regression Fitting of Rainfall-Induced Landslide Occurrence Probability and Continuous Landslide Hazard Prediction Modelling. Earth Science, 47(12): 4609-4628 (in Chinese with English abstract).

Huang, F. M., Hu, S. Y., Yan, X. Y., et al., 2022b. Landslide Susceptibility Prediction and Identification of Its Main Environmental Factors Based on Machine Learning Models. Bulletin of Geological Science and Technology, 41(2): 79-90 (in Chinese with English abstract).

Huang, F. M., Li, J. F., Wang, J. Y., et al., 2022c. Modelling Rules of Landslide Susceptibility Prediction Considering the Suitability of Linear Environmental Factors and Different Machine Learning Models. Bulletin of Geological Science and Technology, 41(2): 44-59 (in Chinese with English abstract).

Huang, F. M., Ye, Z., Yao, C., et al., 2020. Uncertainties of Landslide Susceptibility Prediction: Different Attribute Interval Divisions of Environmental Factors and Different Data-Based Models. Earth Science, 45(12): 4535-4549 (in Chinese with English abstract).

Huang, F. M., Yin, K. L., Jiang, S. H., et al., 2018. Landslide Susceptibility Assessment Based on Clustering Analysis and Support Vector Machine. Chinese Journal of Rock Mechanics and Engineering, 37(1): 156-167 (in Chinese with English abstract). http://search.cnki.net/down/default.aspx?filename=YSLX201801016&dbcode=CJFD&year=2018&dflag=pdfdown

Javanbarg, M. B., Scawthorn, C., Kiyono, J., et al., 2012. Fuzzy AHP-Based Multicriteria Decision Making Systems Using Particle Swarm Optimization. Expert Systems with Applications, 39(1): 960-966. doi: 10.1016/j.eswa.2011.07.095

Kalia, A., 2018. Classification of Landslide Activity on a Regional Scale Using Persistent Scatterer Interferometry at the Moselle Valley (Germany). Remote Sensing, 10(12): 1880. doi: 10.3390/rs10121880

Li, Z., Nadim, F., Huang, H., et al., 2010. Quantitative Vulnerability Estimation for Scenario-Based Landslide Hazards. Landslides, 7(2): 125-134. doi: 10.1007/s10346-009-0190-3

Lin, X. S., 2001. The Study of Landslide Related to Rainfall. Journal of Geological Hazards and Environment Preservation, 12(3): 1-7 (in Chinese with English abstract). doi: 10.3969/j.issn.1006-4362.2001.03.001

Notti, D., Herrera, G., Bianchini, S., et al., 2014. A Methodology for Improving Landslide PSI Data Analysis. International Journal of Remote Sensing, 35(6): 2186-2214. https://doi.org/10.1080/01431161.2014.889864

Papathoma-Köhle, M., Neuhäuser, B., Ratzinger, K., et al., 2007. Elements at Risk as a Framework for Assessing the Vulnerability of Communities to Landslides. Natural Hazards and Earth System Sciences, 7(6): 765-779. doi: 10.5194/nhess-7-765-2007

Peduto, D., Ferlisi, S., Nicodemo, G., et al., 2017. Empirical Fragility and Vulnerability Curves for Buildings Exposed to Slow-Moving Landslides at Medium and Large Scales. Landslides, 14(6): 1993-2007. doi: 10.1007/s10346-017-0826-7

Peduto, D., Nicodemo, G., Caraffa, M., et al., 2018. Quantitative Analysis of Consequences to Masonry Buildings Interacting with Slow-Moving Landslide Mechanisms: A Case Study. Landslides, 15(10): 2017-2030. doi: 10.1007/s10346-018-1014-0

Peduto, D., Oricchio, L., Nicodemo, G., et al., 2021. Investigating the Kinematics of the Unstable Slope of Barbera de la Conca (Catalonia, Spain) and the Effects on the Exposed Facilities by GBSAR and Multi-Source Conventional Monitoring. Landslides, 18(1): 457-469. doi: 10.1007/s10346-020-01500-9

Peng, L., Xu, S., Hou, J., et al., 2015. Quantitative Risk Analysis for Landslides: The Case of the Three Gorges Area, China. Landslides, 12(5): 943-960. doi: 10.1007/s10346-014-0518-5

Pereira, S., Santos, P. P., Zêzere, J. L., et al., 2020. A Landslide Risk Index for Municipal Land Use Planning in Portugal. The Science of the Total Environment, 735: 139463. doi: 10.1016/j.scitotenv.2020.139463

Silva, V., Brzev, S., Scawthorn, C., et al., 2022. A Building Classification System for Multi-Hazard Risk Assessment. International Journal of Disaster Risk Science, (2): 161-177. doi: 10.1007/s13753-022-00400-x?utm_source=xmol&utm_content=meta

Singh, A., Kanungo, D. P., Pal, S., 2019. Physical Vulnerability Assessment of Buildings Exposed to Landslides in India. Natural Hazards, 96(2): 753-790. doi: 10.1007/s11069-018-03568-y

Subasinghe, C. N., Kawasaki, A., 2021. Assessment of Physical Vulnerability of Buildings and Socio- Economic Vulnerability of Residents to Rainfall Induced Cut Slope Failures: A Case Study in Central Highlands, Sri Lanka. International Journal of Disaster Risk Reduction, 65: 102550. doi: 10.1016/j.ijdrr.2021.102550

Uzielli, M., Catani, F., Tofani, V., et al., 2015. Risk Analysis for the Ancona Landslide—Ⅱ: Estimation of Risk to Buildings. Landslides, 12(1): 83-100. doi: 10.1007/s10346-014-0477-x

Wu, Y., Liu, D. S., Lu, X., et al., 2011. Vulnerability Assessment Model for Hazard Bearing Body and Landslide Risk Index. Rock and Soil Mechanics, 32(8): 2487-2492, 2499 (in Chinese with English abstract). doi: 10.3969/j.issn.1000-7598.2011.08.039

Wu, Y., Miao, F., Li, L., et al., 2017. Time-Varying Reliability Analysis of Huangtupo Riverside No. 2 Landslide in the Three Gorges Reservoir Based on Water-Soil Coupling. Engineering Geology, 226: 267-276. http://www.sciencedirect.com/science/article/pii/S0013795217309468

Zeng, T., Jiang, H., Liu, Q., et al., 2022. Landslide Displacement Prediction Based on Variational Mode Decomposition and MIC-GWO-LSTM Model. Stochastic Environmental Research and Risk Assessment, 36: 1353-1372. doi: 10.1007/s00477-021-02145-3

Zhang, Y. S., Liu, X. Y., Yao, X., 2020. InSAR-Based Method for Early Recognition of Ancient Landslide Reactivation in Dadu River, China. Journal of Hydraulic Engineering, 51(5): 545-555 (in Chinese with English abstract).

Zhang, Z., Qian, M., Wei, S., et al., 2018. Failure Mechanism of the Qianjiangping Slope in Three Gorges Reservoir Area, China. Geofluids, (5): 1-12. http://www.onacademic.com/detail/journal_1000040884440310_b6c7.html

Zhou, C. , 2018. Landslide Identification and Prediction with the Application of Time Series InSAR (Dissertation). China University of Geosciences, Wuhan (in Chinese with English abstract).

Zhou, C., Cao, Y., Yin, K., et al., 2022. Characteristic Comparison of Seepage-Driven and Buoyancy-Driven Landslides in Three Gorges Reservoir Area, China. Engineering Geology, 301: 106590. doi: 10.1016/j.enggeo.2022.106590

杜娟, 2012. 单体滑坡灾害风险评价研究(博士学位论文). 武汉: 中国地质大学.

郭子正, 殷坤龙, 唐扬, 等, 2017. 库水位下降及降雨作用下麻柳林滑坡稳定性评价与预测. 地质科技情报, 36(4): 260-265, 270. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201704035.htm

黄发明, 曹昱, 范宣梅, 等, 2021a. 不同滑坡边界及其空间形状对滑坡易发性预测不确定性的影响规律. 岩石力学与工程学报, 40(S02): 3227-3240. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2021S2023.htm

黄发明, 陈佳武, 唐志鹏, 等, 2021b. 不同空间分辨率和训练测试集比例下的滑坡易发性预测不确定性. 岩石力学与工程学报, 40(6): 1155-1169. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX202106008.htm

黄发明, 陈彬, 毛达雄, 等, 2023. 基于自筛选深度学习的滑坡易发性预测建模及其可解释性. 地球科学, 48(5): 1696-1710. doi: 10.3799/dqkx.2022.247

黄发明, 陈佳武, 范宣梅, 等, 2022a. 降雨型滑坡时间概率的逻辑回归拟合及连续概率滑坡危险性建模. 地球科学, 47(12): 4609-4628. doi: 10.3799/dqkx.2021.164

黄发明, 胡松雁, 闫学涯, 等, 2022b. 基于机器学习的滑坡易发性预测建模及其主控因子识别. 地质科技通报, 41(2): 79-90. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202202008.htm

黄发明, 李金凤, 王俊宇, 等, 2022c. 考虑线状环境因子适宜性和不同机器学习模型的滑坡易发性预测建模规律. 地质科技通报, 41(2): 44-59. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202202005.htm

黄发明, 叶舟, 姚池, 等, 2020. 滑坡易发性预测不确定性: 环境因子不同属性区间划分和不同数据驱动模型的影响. 地球科学, 45(12): 4535-4549. doi: 10.3799/dqkx.2020.247

黄发明, 殷坤龙, 蒋水华, 等, 2018. 基于聚类分析和支持向量机的滑坡易发性评价. 岩石力学与工程学报, 37(1): 156-167. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201801016.htm

林孝松, 2001. 滑坡与降雨研究. 地质灾害与环境保护, 12(3): 1-7. https://www.cnki.com.cn/Article/CJFDTOTAL-DZHB202102002.htm

吴越, 刘东升, 陆新, 等, 2011. 承灾体易损性评估模型与滑坡灾害风险度指标. 岩土力学, 32(8): 2487-2492, 2499. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201108040.htm

张永双, 刘筱怡, 姚鑫, 2020. 基于InSAR技术的古滑坡复活早期识别方法研究: 以大渡河流域为例. 水利学报, 51(5): 545-555. https://www.cnki.com.cn/Article/CJFDTOTAL-SLXB202005005.htm

周超, 2018. 集成时间序列InSAR技术的滑坡早期识别与预测研究(博士学位论文). 武汉: 中国地质大学.

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(9) / Tables(7)

Get Citation

PDF

XML

Article views (1479) PDF downloads(116)

Quantitative Vulnerability Analysis of Buildings Based on Landslide Intensity Prediction

doi: 10.3799/dqkx.2022.429

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content