Quantitative Vulnerability Analysis of Buildings Based on Landslide Intensity Prediction

被引：0

作者：

Zeng T. ^{[1
]}

Yin K. ^{[2
]}

Gui L. ^{[2
]}

Jin B. ^{[2
]}

Liu X. ^{[2
]}

Liu Z. ^{[2
]}

Guo Z. ^{[3
]}

Jiang H. ^{[4
]}

Wu L. ^{[2
]}

机构：

[1] Institute of Geological Survey, China University of Geosciences, Wuhan

[2] Faculty of Engineering, China University of Geosciences, Wuhan

[3] School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin

[4] School of Environmental and Safety Engineering, Changzhou University, Changzhou

来源：

Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences | 2023年 / 48卷 / 05期

关键词：

ABAQUS; building; hazard geology; landslide intensify; PS-InSAR; vulnerability;

D O I：

10.3799/dqkx.2022.429

中图分类号：

学科分类号：

摘要：

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 Ipu=0.065×Dtot0.236 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. © 2023 China University of Geosciences. All rights reserved.

引用

页码：1807 / 1824

页数：17

共 43 条

[1]

Agliata R., Bortone A., Mollo L., 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, (2021)

[2]

Bera S., Guru B., Oommen T., 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, (2020)

[3]

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

[4]

Cascini L., Fornaro G., Peduto D., Advanced Low- and Full-Resolution DInSAR Map Generation for Slow - Moving Landslide Analysis at Different Scales, Engineering Geology, 112, 1−4, pp. 29-42, (2010)

[5]

Cruden D. M., Varnes D. J., Landslide Types and Processes, Landslide Investigation and Mitigation, pp. 36-75, (1996)

[6]

Dai C., Li W., Wang D., Et al., Active Landslide Detection Based on Sentinel-1 Data and InSAR Technology in Zhouqu County, Gansu Province, Northwest China, Journal of Earth Science, 32, 5, (2021)

[7]

Del Soldato M., Bianchini S., Calcaterra D., Et al., A New Approach for Landslide - Induced Dam - age Assessment, Geomatics, Natural Hazards and Risk, 8, 2, pp. 1524-1537, (2017)

[8]

Du J., Risk Assessment of Individual Landslide, (2012)

[9]

Fell R., Corominas J., Bonnard C., Et al., Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning, Engineering Geology, 102, 3−4, (2008)

[10]

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

← 1 2 3 4 5 →