Fusion of Optical and SAR Data for Seismic Vulnerability Mapping of Buildings

The main purpose of the present study was to mathematically integrate different decision support systems to enhance the accuracy of seismic vulnerability mapping in Sanandaj City, Iran. An earthquake is considered to be a catastrophe that poses a serious threat to human infrastructures at different scales. Factors affecting seismic vulnerability were identified in three different dimensions; social, environmental, and physical. Our computer-based modeling approach was used to create hybrid training datasets via fuzzy-multiple criteria analysis (fuzzy-MCDA) and multiple criteria decision analysis-multi-criteria evaluation (MCDA-MCE) for training the multi-criteria evaluation–logistic regression (MCE–LR) and fuzzy-logistic regression (fuzzy-LR) hybrid model. The resulting dataset was validated using the seismic relative index (SRI) method and ten damaged spots from the study area, in which the MCDA-MCE model showed higher accuracy. The hybrid learning models of MCE-LR and fuzzy-LR were implemented using both resulting datasets for seismic vulnerability mapping. Finally, the resulting seismic vulnerability maps based on each model were validation using area under curve (AUC) and frequency ratio (FR). Based on the accuracy assessment results, the MCDA-MCE hybrid model (AUC = 0.85) showed higher accuracy than the fuzzy-MCDA model (AUC = 0.80), and the MCE-LR hybrid model (AUC = 0.90) resulted in more accurate vulnerability map than the fuzzy-LR hybrid model (AUC = 0.85). The results of the present study show that the accuracy of modeling and mapping seismic vulnerability in our case study area is directly related to the accuracy of the training dataset.

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GIS-based Seismic Vulnerability Mapping: A Comparison of Artificial neural networks Hybrid Models

Geocarto International ◽

10.1080/10106049.2021.1892208 ◽

2021 ◽

pp. 1-21

Author(s):

Peyman Yariyan ◽

Rahim Ali Abbaspour ◽

Alireza Chehreghan ◽

MohammadReza Karami ◽

Artemi Cerdà

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Seismic Vulnerability ◽

Hybrid Models ◽

Vulnerability Mapping ◽

Artificial Neural

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GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w6-2021-125-2021 ◽

2021 ◽

Vol XLVI-4/W6-2021 ◽

pp. 125-132

Author(s):

M. J. D. De Los Santos ◽

J. A. Principe

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Seismic Vulnerability ◽

Model Building ◽

Data Access ◽

Machine Learning Algorithms ◽

Training Data ◽

Vulnerability Mapping ◽

Support Vector ◽

Surface Model

Abstract. Disaster risk reduction and management (DRRM) not only requires a thorough understanding of hazards but also knowledge of how much built-up structures are exposed and vulnerable to a specific hazard. This study proposed a rapid earthquake exposure and vulnerability mapping methodology using the municipality of Porac, Pampanaga as a case study. To address the challenges and limitations of data access and availability in DRRM operations, this study utilized Light Detection and Ranging (LiDAR) data and machine learning (ML) algorithms to produce an exposure database and conduct vulnerability estimation in the study area. Buildings were delineated through image thresholding and classification of the normalized Digital Surface Model (nDSM) and an exposure database containing building attributes was created using Geographic Information System (GIS). ML algorithms such as Support Vector Machine (SVM), logistic regression, and Random Forest (RF) were then used to predict the model building type (MBT) of delineated buildings to estimate seismic vulnerability. Results showed that the SVM model yielded the lowest accuracy (53%) while logistic regression and RF models performed fairly (72% and 78% respectively) as indicated by their F-1 scores. To improve the accuracy of the exposure database and vulnerability estimation, this study recommends that the proposed building delineation process be further refined by experimenting with more appropriate thresholds or by conducting point cloud classification instead of pixel-based image classification. Moreover, ground truth MBT samples should be used as training data for MBT prediction. For future work, the methodology proposed in this study can be implemented when conducting earthquake damage assessments.

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Improvements to the DRASTIC ground-water vulnerability mapping method

Fact Sheet ◽

10.3133/fs06699 ◽

1999 ◽

Cited By ~ 12

Author(s):

Michael G. Rupert

Keyword(s):

Ground Water ◽

Mapping Method ◽

Vulnerability Mapping ◽

Water Vulnerability

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SEISMIC CAPACITY OF MEDIUM SIZE CONTINUOUS BRIDGES WITH LEAD-RUBBER BEARINGS

NED University Journal of Research ◽

10.35453/nedjr-stmech-2019-0091 ◽

2019 ◽

Vol 3 (Special Issue on First SACEE'19) ◽

pp. 199-206

Author(s):

Bertha Olmos ◽

José Jara ◽

José Luis Fabián

Keyword(s):

Seismic Performance ◽

Seismic Vulnerability ◽

Base Isolation ◽

Elastic Behaviour ◽

Medium Size ◽

Nonlinear Behaviour ◽

Seismic Capacity ◽

Damage Scenarios ◽

Isolation Systems ◽

Rubber Bearings

This paper investigates the effects of the nonlinear behaviour of isolation pads on the seismic capacity of bridges to identify the parameters of base isolation systems that can be used to improve seismic performance of bridges. A parametric study was conducted by designing a set of bridges for three different soil types and varying the number of spans, span lengths, and pier heights. The seismic responses (acceleration, displacement and pier seismic forces) were evaluated for two structural models. The first model corresponded to the bridges supported on elastomeric bearings with linear elastic behaviour and the second model simulated a base isolated bridge that accounts for the nonlinear behaviour of the system. The seismic demand was represented with a group of twelve real accelerograms recorded on the subduction zone on the Pacific Coast of Mexico. The nonlinear responses under different damage scenarios for the bridges included in the presented study were estimated. These results allow determining the seismic capacity of the bridges with and without base isolation. Results show clearly the importance of considering the nonlinear behaviour on the seismic performance of bridges and the influence of base isolation on the seismic vulnerability of medium size bridges.

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SEISMIC VULNERABILITY ASSESSMENT FOR SAN FRANCISCO TEMPLE IN MORELIA, MEXICO

NED University Journal of Research ◽

10.35453/nedjr-stmech-2019-0071 ◽

2019 ◽

Vol 3 (Special Issue on First SACEE'19) ◽

pp. 207-2016

Author(s):

Guillermo Martinez ◽

David Castillo ◽

José Jara ◽

Bertha Olmos

Keyword(s):

San Francisco ◽

Seismic Vulnerability ◽

Seismic Analysis ◽

Three Dimensional ◽

Middle Part ◽

Seismic Demands ◽

Modeling And Analysis ◽

Seismic Records ◽

Cracking Pattern ◽

The Temple

This paper presents a first approximation of the seismic vulnerability of a sixteenth century building which is part of the historical center of Morelia, Mexico. The city was declared World Heritage by United Nations Educational, Scientific and Cultural Organization in 1991. The modeling and analysis of the building was carried out using a three-dimensional elastic tetrahedral finite elements model which was subjected to probabilistic seismic demands with recurrences of 500 yrs and 1000 yrs in addition to real seismic records. The model was able to correctly identify cracking pattern in different parts of the temple due to gravitational forces. High seismic vulnerability of the arched window and the walls of the middle part of the bell tower of the temple was indicated by the seismic analysis of the model.

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