• Earthquakes and Structures
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• v.25 no.6
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• pp.429-442
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• 2023

To study the seismic vulnerability of the composite material structure of adobe and timber, we collected and statistically analysed empirical observation samples of 542,214,937 m² and 467,177 buildings that were significantly impacted during the 179 earthquakes that occurred in mainland China from 1976 to 2010. In multi-intensity regions, combined with numerical analysis and a probability model, a non-linear continuous regression model of the vulnerability, considering the empirical seismic damage area (number of buildings) and the ratio of seismic damage, was established. Moreover, a probability matrix model of the empirical seismic damage mean value was provided. Considering the coupling effect of the annual and seismic fortification factors, an empirical seismic vulnerability curve model was constructed in the multiple-intensity regions. A probability matrix model of the mean vulnerability index (MVI) was proposed, and was validated through the above-mentioned reconnaissance sample data. A matrix model of the MVI of the regions (19 provinces in mainland China) based on the parameter (MVI) was established.

• Earthquakes and Structures
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• v.19 no.5
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• pp.379-393
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• 2020

The purpose of this study is to first evaluate the seismic behavior of ageing arch bridges by using the Intensity Measure - based demand and DCFD format, which is referred to as the fragility-hazard format. Then, an investigation is performed for their seismic vulnerability. Analytical models are created for bridges concerning different features and these models are subjected to Incremental Dynamic Analysis (IDA) analysis using a set of 22 earthquake records. The hazard curve and results of IDA analysis are employed to evaluate the return period of exceeding the limit states in the IM-based probabilistic performance-based context. Subsequently, the fragility-hazard format is used to assess factored demand, factored capacity, and the ratio of the factored demand to the factored capacity of the models with respect to different performance objectives. Finally, the vulnerability curves are obtained for the investigated bridges in terms of the loss ratio. The results revealed that decreasing the span length of the unreinforced arch bridges leads to the increase in the return period of exceeding various limit states and factored capacity and decrease in the displacement demand, the probability of failure, the factored demand, as well as the factored demand to factored capacity ratios, loss ratio, and seismic vulnerability. Finally, it is derived that the probability of the need for rehabilitation increases by an increase in the span length of the models.

• Earthquakes and Structures
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• v.2 no.1
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• pp.25-42
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• 2011

This paper discusses a mechanical model for the vulnerability assessment of old masonry building aggregates that takes into account the uncertainties inherent to the building parameters, to the seismic demand and to the model error. The structural capacity is represented as an analytical function of a selected number of geometrical and mechanical parameters. Applying a suitable procedure for the uncertainty propagation, the statistical moments of the capacity curve are obtained as a function of the statistical moments of the input parameters, showing the role of each one in the overall capacity definition. The seismic demand is represented by response spectra; vulnerability analysis is carried out with respect to a certain number of random limit states. Fragility curves are derived taking into account the uncertainties of each quantity involved.

• Structural Monitoring and Maintenance
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• v.2 no.2
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• pp.115-132
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• 2015

This paper presents a new method for seismic vulnerability assessment of buildings with reference to their operational limit state. The importance of this kind of evaluation arises from the civil protection necessity that some buildings, considered strategic for seismic emergency management, should retain their functionality also after a destructive earthquake. The method is based on the identification of experimental modal parameters from ambient vibrations measurements. The knowledge of the experimental modes allows to perform a linear spectral analysis computing the maximum structural drifts of the building caused by an assigned earthquake. Operational condition is then evaluated by comparing the maximum building drifts with the reference value assigned by the Italian Technical Code for the operational limit state. The uncertainty about the actual building seismic frequencies, typically significantly lower than the ambient ones, is explicitly taken into account through a probabilistic approach that allows to define for the building the Operational Index together with the Operational Probability Curve. The method is validated with experimental seismic data from a permanently monitored public building: by comparing the probabilistic prediction and the building experimental drifts, resulting from three weak earthquakes, the reliability of the method is confirmed. Finally an application of the method to a strategic building in Italy is presented: all the procedure, from ambient vibrations measurement, to seismic input definition, up to the computation of the Operational Probability Curve is illustrated.

• Earthquakes and Structures
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• v.6 no.6
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• pp.611-625
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• 2014

This paper presents a simple methodology that integrates an improved storey shear modelling, Incremental Dynamic Analysis and Monte Carlo Simulation in order to carryout vulnerability analysis towards development of fragility curves for Unreinforced Brick Masonry buildings. The methodology is demonstrated by developing fragility curves of a single storey Unreinforced Brick Masonry building for which results of experiment under lateral load is available in the literature. In the study presented, both uncertainties in mechanical properties of masonry and uncertainties in the characteristics of earthquake ground motion are included. The research significance of the methodology proposed is that, it accommodates a new method of damage grade classification which is based on 'structural performance characteristics' instead of 'fixed limiting values'. The usefulness of such definition is discussed as against the existing practice.

• Earthquakes and Structures
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• v.22 no.6
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• pp.609-623
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• 2022

To study the empirical seismic fragility of a reinforced concrete girder bridge, based on the theory of numerical analysis and probability modelling, a regression fragility method of a rapid fragility prediction model (Gaussian first-order regression probability model) considering empirical seismic damage is proposed. A total of 1,069 reinforced concrete girder bridges of 22 highways were used to verify the model, and the vulnerability function, plane, surface and curve model of reinforced concrete girder bridges (simple supported girder bridges and continuous girder bridges) considering the number of samples in multiple intensity regions were established. The new empirical seismic damage probability matrix and curve models of observation frequency and damage exceeding probability are developed in multiple intensity regions. A comparative vulnerability analysis between simple supported girder bridges and continuous girder bridges is provided. Depending on the theory of the regional mean seismic damage index matrix model, the empirical seismic damage prediction probability matrix is embedded in the multidimensional mean seismic damage index matrix model, and the regional rapid prediction matrix and curve of reinforced concrete girder bridges, simple supported girder bridges and continuous girder bridges in multiple intensity regions based on mean seismic damage index parameters are developed. The established multidimensional group bridge vulnerability model can be used to quantify and predict the fragility of bridges in multiple intensity regions and the fragility assessment of regional group reinforced concrete girder bridges in the future.

• Korean Journal of Social Welfare Studies
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• v.45 no.2
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• pp.233-261
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• 2014

There are two competing models explaining the causal relationship of depression and self-esteem, and they are cognitive vulnerability model and the scar model. Cognitive vulnerability model explains that low self-esteem poses as a risk factor for development of depressive symptoms/depression while the scar model asserts that the experiences of depression scars the cognitive function of individuals, resulting in negative self-perception. This study was set out to test two models on Korean adults, and to identify factors that are associated with depression and self-esteem relationship. The first four waves (wave 1~4) of the Korea Welfare Panel Study (KOWEPS) were used for analyses, and latent growth curve modeling was employed to examine the relationship. The findings show that the relationship was reciprocal, one affecting the growth trajectory of another over a four year period. Furthermore, education, poverty status, health status, and satisfaction with social relationships were found to be significantly associated with both depression and self-esteem trajectories. Implications for practice and theory are provided.

• Journal of the Korea Concrete Institute
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• v.20 no.1
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• pp.51-58
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• 2008

Analytical probabilistic vulnerability analysis requires extensive computing effort as a result of the randomness in both input motion and response characteristics. In this study, a new methodology whereby a set of vulnerability curves are derived based on the fundamental response quantities of stiffness, strength and ductility is presented. A response database of coefficients describing lognormal vulnerability relationships is constructed by employing aclosed-form solution for a generalized single-degree-of-freedom system. Once the three fundamental quantities of a wide range of structural systems are defined, the vulnerability curves for various limit states can be derived without recourse to further simulation. Examples of application are given and demonstrate the extreme efficiency of the proposed approach in deriving vulnerability relationships.

• Earthquakes and Structures
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• v.12 no.5
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• pp.489-499
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• 2017

The effects of past earthquakes have demonstrated the seismic vulnerability of confined masonry structures (CMSs) to earthquakes. The results of experimental analysis indicate that damage to these structures depends on lateral displacement applied to the walls. Seismic evaluation lacks an analytical approach because of the complexity of the behavior of this type of structure; an empirical approach is often used for this purpose. Seismic assessment and risk analysis of CMSs, especially in area have a large number of such buildings is difficult and could be riddled with error. The present study used analytical and numerical models to develop a simplified nonlinear displacement-based approach for seismic assessment of a CMS. The methodology is based on the concept of ESDOF and displacement demand and is compared with displacement capacity at the characteristic period of vibration according to performance level. Displacement demand was identified using the nonlinear displacement spectrum for a specified limit state. This approach is based on a macro model and nonlinear incremental dynamic analysis of a 3D prototype structure taking into account uncertainty of the mechanical properties and results in a simple, precise method for seismic assessment of a CMS. To validate the approach, a case study was considered in the form of an analytical fragility curve which was then compared with the precise method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.