Seismic Vulnerability Assessment Research Articles

Seismic vulnerability assessment of tunnels in algeria: An innovative hierarchical approach

Tunnels are crucial components of civil infrastructure, serving as vital transportation routes for the public. In Algeria, where many tunnels are located in seismic zones, they face considerable risks from earthquakes, which can potentially impair their operational integrity. The susceptibility of these tunnels to seismic hazards underscores the need for robust assessment frameworks to evaluate their vulnerability. This study introduces a novel hierarchical framework for assessing tunnel vulnerability, drawing on global seismic data and experience. By integrating the Analytic Hierarchy Process (AHP) with the Vulnerability Index (VI), we categorized tunnels into three vulnerability levels. This approach enabled the development of seismic scenarios to forecast their performance during future earthquakes. The methodology was applied to numerous tunnels across Algeria, yielding insightful results that align with observations from post-seismic assessments. By systematically analyzing various influencing parameters, including geological conditions, structural design, and operational factors, the study enhances our understanding of tunnel resilience in seismic contexts. Ultimately, this research contributes to proactive risk management strategies for tunnel infrastructure, aiming to mitigate potential damages and ensure their continued functionality in earthquake-prone regions like Algeria. By refining our assessment methods and predictive capabilities, we strive to bolster the resilience of critical urban infrastructure against seismic risks.

Energy based seismic vulnerability assessment tool for reinforced concrete bridges

Energy based seismic vulnerability assessment tool for reinforced concrete bridges

Energy based seismic vulnerability assessment tool for reinforced concrete bridges

Energy based seismic vulnerability assessment tool for reinforced concrete bridges

Dynamic response and seismic vulnerability assessment of the near-fault steep slope

Existing field data demonstrates that structures situated near faults, such as slopes, dams, and bridges, are particularly vulnerable to damage. Near-fault ground motions exhibit marked disparities from their far-field counterparts, necessitating thorough investigation. This study conducts a comprehensive analysis of ground motion characteristics utilizing response spectra, Hilbert-Huang Transform (HHT), and marginal spectra to discern near-fault pulse-like ground motions (PLGMs), near-fault non-pulse-like ground motions (NPGMs), and far-fault ground motions (FFGMs). PLGMs distinguished by their heightened energy content, typically induce more pronounced amplification effects compared to NPGMs and FFGMs. Employing an efficacy criterion, optimal intensity measures (IMs) are identified and linked to dynamic response, referred to as engineering demand parameters (EDPs). Velocity-based IMs demonstrate substantial correlations across diverse ground motion types for both plastic volume ratio (PVR) and crest displacement (Dc). Lastly, probabilistic seismic demand models are leveraged to establish seismic vulnerability curves for steep slopes exposed to PLGMs and NPGMs. Notably, with Dc as the EDP, the exceedance probabilities associated with the three IMs under PLGMs generally surpass those under NPGMs. This underscores the propensity of PLGMs to trigger larger displacements in slopes, rendering them more prone to failure.

A comprehensive approach to assess the seismic vulnerability of archaeological sites: the Wupatki Pueblo in Arizona

The proposed research work presents a comprehensive approach to assessing the seismic vulnerability of archaeological sites. This approach aims to be a quick and easy-to-use investigation procedure that enables accurate and large-scale evaluations. While the methods employed are well-established in the literature and have been widely applied to buildings, this study contributes by proposing a structured framework that integrates different assessment procedures at different levels of analysis, specifically tailored to archaeological sites. The analysis is divided into three stages within the conceptual framework: (i) the application of the Masonry Quality Index; (ii) seismic vulnerability assessment and prediction of expected damage; and (iii) analysis of individual walls’ structural response through strength domain, capacity and fragility curves. Specifically, the study explores and adapts four Vulnerability Index methods, i.e. GNDT, Formisano, Vicente and Ferreira methods, to suit the specific characteristics of archaeological sites. To this end, a simplified procedure is proposed to estimate the conventional strength in the methods’ forms. The comparison of the index-based methods is then crucial for critically evaluating the reliability of vulnerability estimations. The paper illustrates the application of this framework through a detailed case study, i.e. the archaeological site of Wupatki Pueblo in Arizona (US), demonstrating its effectiveness in evaluating the seismic risk and defining the vulnerability distribution of the site. Consequently, this approach facilitates the identification of the most sensitive areas, which necessitate further investigation, providing useful outcomes for the decision-making process concerning the conservation and protection of archaeological sites.

Seismic vulnerability assessment of spherical and horizontal-cylindrical storage tanks through finite element analyses and observational data

Storage tanks are structures widely employed in various chemical and petroleum industries for the storage of liquids and gases. These structures, which store flammable and explosive substances, can lead to events such as fire and explosion in the event of any hazardous situation. Such accidents can have adverse effects on human life, facilities, the economy, and the environment. Earthquakes, as a natural threat affecting all structures, also trigger events like fire and explosion in storage tanks. Therefore, the assessment of seismic risks for storage tanks, the prediction of damage, is crucial for both existing and newly constructed tanks. In this study, the seismic behaviors of spherical and horizontal cylindrical storage tanks were investigated based on observational and finite element analysis data. Fragility analyses of tanks were conducted considering several commonly used statistical approaches, and fragility curves were derived. By using fragility curves, the probability of structural damage due to earthquakes is observed as a function of ground motion parameters. These curves express the likelihood of reaching or exceeding a pre-determined damage state under seismic effects for the considered type of structure. They are developed to predict potential damage during earthquakes and are outputs of seismic risk assessments. Fragility curves are utilized as indicators to determine physical damage during both the main shock and lower-intensity aftershocks. They can provide insights into whether the structure can be reused after earthquakes. Additionally, they are employed to reduce economic losses and casualties in the face of seismic events. For these reasons, fragility curves are essential statistical tools for decision-making both before and after earthquakes.

Building pose detection for the characterization of reinforced concrete buildings

SummaryThe automated identification of building characteristics for seismic vulnerability remains a challenge for governments due to the high number of buildings in cities. The diverse architectural styles of these buildings complicate the automated identification of building information (e.g., number of stories, structural system, and material type). Deep learning techniques lose accuracy as they generalize information, while the visual contents of a building exhibit a considerable range and diversity. This study leverages the pose detection technique to tackle such issues by focusing on a common construction style: reinforced concrete buildings representing columns, beams, or floors on the façade. With an aim to enable the assessment of seismic vulnerability, the technique developed herein is conceived for buildings with up to six stories that are more likely to be moment‐frame buildings. The AI‐enabled proposed framework starts with collecting building images and categorizing those containing this specific building type. A bounding box detector is then used to isolate building facades, for the subsequent identification of the structural frame with the High‐Resolution Network (HR‐Net). For demonstration, we illustrate this technique by identifying the structural frame on concrete buildings with a sample dataset developed based on buildings found in Mexico City in a pre‐earthquake event state.

Optimal earthquake intensity measures for seismic vulnerability assessment of concrete-faced rockfill dams

Earthquake intensity measure (IM) selection is crucial for developing probabilistic seismic demand models (PSDMs). This study explored the optimal IMs that can be utilized in PSDMs for concrete-faced rockfill dams (CFRDs). A two-dimensional nonlinear dynamic analysis of a CFRD was performed to assess its response to earthquake ground motion. A numerical model of CFRD was developed using the commercial software LS-DYNA with an advanced hysteretic soil model. A total of 20 earthquake IMs were selected for regression analysis. The regression analysis was performed between IMs and the damage index (DI). The IMs were evaluated through a selection process based on the goodness of fit as well as their efficiency, practicality, and proficiency. Accordingly, a range of optimal IMs was determined. The results indicated that effective design acceleration (EDA) is the best earthquake IM correlating with the seismic response of dam settlement and can be considered the optimal IM, whereas peak ground displacement (PGD), ratio of peak ground velocity to peak ground acceleration (PGV/PGA), and predominant period (Tp) are the less efficient. The fragility curves and surfaces were developed employing both scalar and vector IMs for CFRD.

Seismic Vulnerability Assessment of Regular and Irregular Reinforced Concrete Shear Wall Buildings Using Fragility Curves

Seismic Vulnerability Assessment of Regular and Irregular Reinforced Concrete Shear Wall Buildings Using Fragility Curves

Seismic Vulnerability Assessment of Regular and Vertically Irregular Residential Buildings in Nepal

Seismic Vulnerability Assessment of Regular and Vertically Irregular Residential Buildings in Nepal

Multimodal seismic assessment of infrastructures retrofitted with exoskeletons: insights from the Foggia Airport case study

AbstractAddressing the seismic vulnerability of infrastructures is critical, especially for those built before the introduction of the current seismic regulations. One of the primary challenges lies in retrofitting these buildings without interrupting their functionality. In this context, the use of exoskeletons for seismic retrofitting represents an effective solution. This approach increases the seismic resistance and ensures the continuous operation of the building during retrofitting. This advantage is especially crucial for critical infrastructures, such as airports. Nevertheless, traditional seismic assessment methods based on pushover analyses might not accurately predict the seismic capacity of complex infrastructures dominated by local vibration modes. To bridge this gap, the study proposes refining the multimodal pushover analysis tailored for seismic vulnerability assessments of large infrastructures with exoskeletons characterized by low modal participation ratios. The Foggia Airport case study exemplifies these points and highlights the practical applications of the discussed advancements. The authors compared two force distributions for push-over analysis, addressing the fine-tuning of exoskeletons to maximize their seismic resistance.

Seismic fragility and vulnerability assessment of a multi-span irregular curved bridge under spatially varying ground motions

Multi-span reinforced concrete (RC) curved box-girder bridges are commonly designed to facilitate traffic flow at highway interchanges. The Aksemsettin Viaduct (henceforth, A Viaduct for brevity) in Istanbul, Turkey, is an eleven-span interchange bridge with a total length of 596.8 m. Located in a high seismicity zone, the A Viaduct is designed with a curved deck, multiple bearings that have different isolation mechanisms at different bents and directions, ten rectangular columns with unequal heights, and a mix of pile foundations and spread footings. The significant length of the viaduct crossed by eleven spans also makes it susceptible to varying ground motion excitations at different foundations. To evaluate the effects of the degree of modeling detail and analysis complexity on the estimated seismic performance, the present study conducts a comprehensive fragility assessment of the specimen viaduct under various ground motion excitation schemes. First, a three-dimensional finite element model is developed with detailed simulations for the deck, columns, bearings, foundations, and abutment components. To enable different ground motion excitations at each foundation, 57 sets of spatially varying ground motions are simulated by considering the realistic surface topography and soil stratigraphy at the bridge site. Cyclic pushover analyses are performed along multiple loading directions to develop the direction-dependent capacity limit state models for hollow rectangular columns. Subsequently, a demand-capacity ratio method is utilized to develop reliable fragility models for bridge columns. Component- and system-level fragilities of the A Viaduct are then assessed under uniform versus multi-support excitations, vertical motions, and ground motions with varying incidence angles. To further capture the seismic damage discrepancies of the same components at different locations, seismic repair cost ratios of the A Viaduct are assessed when subjected to uniform and multi-support excitations. This study highlights the significance of considering multi-support excitations to achieve more realistic seismic fragility and loss estimates for multi-span long curved highway bridges.

Seismic vulnerability assessment of buried water supply and sanitation pipelines using the analytic hierarchy process: a methodology and application

The evaluation of seismic vulnerability in buried pipelines within water supply and sanitation networks stands as a critical endeavor in safeguarding infrastructure against the impacts of earthquakes. In response, this study introduces a systematic methodology rooted in the Vulnerability Index (VI), leveraging the Analytic Hierarchy Process (AHP) to allocate weights to factors influencing pipeline seismic behavior. Through the derivation of an expression for calculating the VI based on these weighted factors, our objective is to furnish a comprehensive pipeline classification system, thereby providing a strategic overview of the networks' seismic resilience. This method's practical utility will be exemplified through the examination of concrete cases involving drinking water pipelines (DWP). Furthermore, the scope will extend to encompass sanitation pipelines, thereby validating the methodology's effectiveness across both domains. By systematically assessing the seismic vulnerability of these crucial infrastructures, we aim to fortify their resilience against seismic events, ensuring the continued provision of essential services even in the face of natural disasters. This study's significance lies not only in its contribution to the field of infrastructure resilience but also in its practical implications for urban planning and disaster management. By elucidating the factors influencing pipeline vulnerability and providing a robust framework for assessment, decision-makers can better prioritize resource allocation and mitigation efforts, ultimately enhancing community safety and well-being. Furthermore, the methodology's adaptability and scalability render it applicable to diverse contexts, facilitating its integration into broader risk management strategies. As such, this study serves as a valuable tool for policymakers, engineers, and stakeholders seeking to enhance the resilience of water supply and sanitation networks in earthquake-prone regions. Through informed decision-making and proactive measures, we can build more resilient communities capable of withstanding the challenges posed by seismic hazards.

Enhancing seismic assessment and risk management of buildings: A neural network-based rapid visual screening method development

Some of the existing buildings are designed based on lower design standards or even without considering seismic design standards. Recent earthquakes have further highlighted the vulnerability of these buildings when subjected to severe seismic activity. Consequently, it has become imperative to conduct seismic vulnerability assessments of the existing building stock. Therefore, the assessment of the existing building stock is required through the utilization of Rapid Visual Screening (RVS) methods. However, the existing conventional RVS methods used in seismic building assessments have shown limited accuracy. Furthermore, because these methods were developed based on expert opinions and/or due to access limitations to detailed assessment-based generated data used for their development, further enhancing them is challenging. To address these limitations, a new RVS method, which leverages Neural Networks (NN) and building-specific parameters, for reinforced concrete, adobe mud, bamboo, brick, stone, and timber buildings has been proposed in this study. Unlike conventional methods that rely on site seismicity class, the developed data-driven approach incorporates building-specific parameters such as the fundamental structural period and building spectral acceleration. The developed RVS method is specifically tailored to analyze diverse types of buildings in regions with varying seismicity risks, all in preparation for an impending earthquake. In this study, the developed RVS method demonstrated a promising 68% test accuracy, effectively representing the building performance against earthquakes. These findings illustrate the potential of the developed NN based RVS method in assessing existing buildings, thereby mitigating potential loss of life and property during imminent earthquake and alleviating the associated economic burden. Furthermore, this study introduces a new RVS method that can pave the way for future advancements in the field of seismic vulnerability assessment of existing buildings.

Operational modal analysis, seismic vulnerability assessment and retrofit of a degraded RC bell tower

This paper presents damage assessment through Operational Modal Analysis (OMA) and Finite Element (FE) model updating of the bell tower of the church of Castro in Bergamo, Italy. The tower is a 39 m high reinforced concrete structure with hollow cross-section and double-curved shape. The research was dictated by the need to identify the actual damage state of the structure, which was found through visual inspections. Piezoelectric accelerometers were used to record the ambient vibrations in subsequent test setups, using the roving technique for system identification. A detailed FE model was created with shell elements and calibrated to match the system identification results. A simplified beam model was then developed based on the modal analysis results of the detailed model. A sensitivity analysis was performed to identify the most influential model parameters on the modal characteristics of the system. Subsequently, the optimal values of these parameters were determined by an optimisation procedure carried out using a typical global optimization algorithm. The updating results allowed assessment of the actual condition of the bell tower and its seismic vulnerability. Finally, a seismic strengthening solution was recommended.

Various numerical modeling procedures of XIX-century masonry building

The 2020 Croatian earthquake marked a new phase in the assessment of existing buildings throughout the region and especially in the ex-Yugoslav countries, specifically in Bosnia and Herzegovina. This was the first time that a database of 700 buildings in Sarajevo was conducted with the aim to perform a rapid seismic vulnerability assessment. Several buildings were identified, as typical representatives of a certain period, and a detailed numerical assessment was performed. The study systematically investigated the seismic response of a typical masonry building from the Austro-Hungarian era in Sarajevo in the modeling scenarios with diverse horizontal diaphragm typologies. Conventional structures with inferior workmanship and substandard materials in horizontal diaphragms exhibited reduced seismic performance. It was established that the diaphragm’s ability to enhance the seismic behavior of such buildings is dependent upon its stiffness and its connection mechanism to load-bearing walls. Comparative analysis of models, particularly Total Strain Crack Models (TSCMs) and the Engineering Material Model (EMM), clarified distinct behaviors in terms of load capacity and ductility. The investigation considers the building’s resistance to seismic forces in both X and Y directions and explores damage patterns, failure modes, and crack distributions. The type of slab connection emerged as a crucial factor, significantly influencing stiffness, capacity, and ductility. These insights offer valuable implications for retrofitting strategies, enabling precise identification of diaphragm components for targeted strengthening and, consequently, enhancing a structure’s seismic resilience. A comprehensive site investigation and accurate determination of connection types were underscored as crucial prerequisites for effective building modeling, essential for earthquake impact assessment, and informed retrofitting decisions.

Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques

Seismic vulnerability assessment in urban areas would, in principle, require the detailed modeling of every single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at an urban scale where many buildings must be considered; therefore, it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns is the application of machine learning algorithms, which are able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper, machine learning algorithms were applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L’Aquila territory during the intense seismic activity that occurred in 2009. A combination of a trained neural network and a random forest algorithm allows us to identify an opportune “a-posteriori” vulnerability score, deduced from the observed damage, which is compared to an “a-priori” vulnerability one, evaluated taking into account characteristic indexes for building’s typologies. By means of this comparison, an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed.

Machine learning and soft voting ensemble classification for earthquake induced damage to bridges

Seismic vulnerability assessment of bridges is essential for both pre-earthquake planning and post-earthquake rapid loss assessment to facilitate maintenance planning and recovery initiatives. Given the impracticality of creating numerical models for every conceivable attribute variation within bridge classes, data-driven approaches offer a versatile solution to address such variations. While data-driven techniques are widely adopted in bridge engineering, many existing models rely on numerical or experimental datasets. However, such models may not adequately capture the dynamic and complex nature of seismic response of bridges. This presents an opportunity to evaluate the effectiveness of data-driven approaches using post-earthquake damage data. This paper assesses the efficacy of machine learning models and develops a robust framework for the classification of earthquake-induced damage incurred to some bridge classes. We deployed damage data collected following the 2015 Gorkha earthquake, incorporating taxonomical, stiffness, and excitation variables as predictor variables. Considering three distinct damage classes—minor, major, and critical—in a heterogeneous bridge class environment, we evaluated multiple machine learning classifiers. To account for the influence of heterogeneous bridge classes in the dataset, we conducted separate assessments for the reinforced concrete (RCC) bridge class. The Random Forest classifier achieved a substantial accuracy improvement, reaching 83% when compared to the mixed bridge class accuracy of 79% reflected by the ANN classifier. Similarly, a minor improvement was observed when deploying soft voting classifiers for RCC only bridge data. The sum of observations suggest that tree-based classifiers consistently outperform other classifiers when considering single bridge classes. This study identifies stiffness variables as the most critical factor in bridge damage classification.

Building typology classification using convolutional neural networks utilizing multiple ground-level image process for city-scale rapid seismic vulnerability assessment

Several studies have focused on generating seismic vulnerability maps for earthquake-prone areas, particularly in Indonesia. Building typologies are a key factor in determining vulnerability to earthquakes. However, conducting large-scale field surveys to determine the spatial distribution of building typologies in a city is uneconomical. This paper explores the use of a convolutional neural network (CNN) to automatically detect building typologies from diverse regions in Indonesia, utilizing both conventional and automated building image acquisition processes. In this study, datasets from three distinct image acquisition methods are trained with four unique CNN architectures to identify the best-performing model to classify building typologies. The sample size effect on CNN performance is also investigated. The results showed that randomly sampled Google Street View (GSV) images are the most effective dataset for the CNN model, achieving an f1-score of 84.33%. Among the network architectures tested, MobileNet demonstrated superior performance on the majority of evaluated datasets. As the sample size increases by about 350% in the dataset, there is a positive correlation with up to 2.3% f1-score improvement. Using the best-performing CNN model, two building vulnerability models were employed to assess the spatial distribution of building damage in the urban area of Bandung, considering a hypothetical scenario of an M7 earthquake. Incorporating local construction data, one of the generated maps estimated that approximately 55% of buildings in Bandung would experience moderate to severe structural damage. This study showcases the potential of CNN models in automating regional seismic assessments and providing valuable insights for comprehensive seismic mitigation strategies.

First-order approximation towards empirical seismic vulnerability assessment of low-cost unreinforced masonry buildings in South Africa

The 5 August 2014 Orkney earthquake in South Africa caused significant damage to low-cost unreinforced masonry buildings. After the earthquake, post-earthquake surveys were conducted to assess damage and deduce the intensity experienced by these buildings. During these surveys, only buildings that were reported as damaged were investigated. This study conducted an empirical seismic vulnerability assessment using data collected from those surveys. However, for a comprehensive assessment, the data should include all buildings in the study area. Hence in this study, a first-order approximation was applied to gather sufficient data to construct fragility curves for low-cost unreinforced masonry buildings in South Africa. The damage probability matrix technique was used for fragility curve construction. The fragility curves were constructed using the intensity and damage data, statistical models, and model fitting techniques. The fragility curves obtained in this study predicted comparable but slightly lower damage compared to other curves for unreinforced masonry buildings of similar typology. Although the method applied was able to produce fragility curves comparable with other studies, it is recommended that in order to get a reliable fragility curves all the buildings in the study area should be investigated including those that suffered little to no damage.