Seismic Risk Research Articles

Near-Real-Time Event-Driven System for Calculating Peak Ground Acceleration (PGA) in Earthquake-Affected Areas: A Critical Tool for Seismic Risk Management in the Campi Flegrei Area

Peak Ground Acceleration (PGA) is a measure of the maximum ground shaking intensity during an earthquake. The estimation of PGA in areas affected by earthquakes is a fundamental task in seismic hazard assessment and emergency response. This paper presents an automated service capable of rapidly calculating the PGA’s values in regions impacted by seismic events and publishing its results on an interactive website. The importance of such a service is discussed, focusing on its contribution to timely response efforts and infrastructure resilience. The necessity for automatic and real-time systems in earthquake-prone areas is emphasized, enabling decision-makers to assess damage potential and deploy resources efficiently. Thanks to a collaboration agreement with the Civil Protection Department, we are able to acquire accelerometric data from the Italian National Accelerometric Network (RAN) in real time at the monitoring center of the Osservatorio Vesuviano. These data, in addition to those normally acquired by the INGV network, enable us to utilize all available accelerometric data in the Campi Flegrei area, enhancing our capacity to provide timely and accurate PGA estimates during seismic events in this highly active volcanic region.

Digital Mapping and Resilience Indicators, as Pillars of Bucharest’s Seismic Resilience Strategy

This study presents relevant elements of seismic resilience strategy containing an innovative digital mapping tool tailored for Bucharest, one of Europe’s most seismically vulnerable areas. The framework integrates seismic resilience indicators and expert input with Bucharest’s seismic micro-zonation map to systematically identify critical relocation areas, including educational institutions, medical facilities, and open spaces for emergency use. A seven-step methodology underpins the strategy: identifying resilience indicators, gathering local data, conducting expert workshops, mapping vulnerable areas, designating emergency open spaces, incorporating educational institutions as shelters, and evaluating the framework through a SWOT (strengths, weaknesses, opportunities, and threats) analysis. The digital mapping tool developed using Google My Maps provides a practical and accessible platform for emergency management professionals and the public, enabling real-time response coordination and informed long-term planning. District 2 is identified as the most vulnerable area due to high population density and peak ground acceleration (PGA), while District 4 faces challenges stemming from limited medical and relocation resources, despite experiencing lower seismic activity. The SWOT analysis demonstrates the tool’s potential as a robust disaster management framework, while highlighting the need for continuous updates, enhanced collaboration, and integration of additional data. This study offers a scalable model for other urban contexts, bridging the gap between strategic planning and operational readiness for seismic risk reduction.

Enhancing Preparedness and Resilience for Seismic Risk Reduction: The “Minoas 2024” Full-Scale Exercise for Earthquakes and Related Geohazards in Crete (Southern Greece)

Enhancing Preparedness and Resilience for Seismic Risk Reduction: The “Minoas 2024” Full-Scale Exercise for Earthquakes and Related Geohazards in Crete (Southern Greece)

Analysis of Seismic Site Effects in Plio-Quaternary Intermontane Basin (L’Aquila, Central Italy)

This study presents a comprehensive analysis of site effects in the highly seismic area of L’Aquila in central Italy, which has been conducted within the framework of a seismic microzonation project funded by the Abruzzo Region’s Department of Government of the Territory and Environmental Policies. The project was aimed at best practices on the management of urban and land territory for seismic risk mitigation. Through the integration of detailed geophysical and geotechnical data with numerical modeling, we provide an accurate assessment of local seismic amplification. Two-dimensional numerical simulations using the LSR 2D code were performed on many representative geological sections to compute amplification factors for various period ranges. This case study allowed us to outline some key considerations for best practices in local seismic response analysis and seismic microzonation studies in Italy. Given the prevalence of 2D basin edge, buried morphology, and topographic effects in Plio-Quaternary geologically complex intermontane basins in central Italy, as demonstrated in the L’Aquila case study, use of two-dimensional models is suggested. In order to validate the numerical models and their associated spectra and amplification factors, it is also suggested to compare transfer functions with HVSR microtremor measurements at control points along the studied sections.

Rising groundwater table due to restoration projects amplifies earthquake induced liquefaction risk in Beijing

Groundwater restoration is increasingly common to mitigate groundwater overexploitation, which proves effective in resolving urban water scarcity and regional unsustainable development. China’s South-to-North Water Diversion Project is one of the largest water transfer projects to restore groundwater and resolve water shortage in Beijing. However, how the rapidly restored groundwater of this magnitude changes regional seismic stability is largely unknown. Here, we explore the relation between elevated groundwater table and seismic ground liquefaction based on the case of Beijing under the impact of the South-to-North Water Diversion Project. We collect groundwater table depth records and use them to drive three-dimensional geotechnical models that generate ground liquefaction hazard maps. We find a remarkable increase in coverage and severity of liquefaction due to groundwater table rise. Infrastructures built during the rapid urbanization process are often under low groundwater table and thus illy prepared for this increased seismic risk. These findings highlight the necessity to consider the seismic consequence of large-scale groundwater restoration projects.

Statistical Characteristics of Strong Earthquake Sequence in Northeastern Tibetan Plateau

As the forefront of inland extension on the Indian plate, the northeastern Tibetan Plateau, marked by low strain rates and high stress levels, is one of the regions with the highest seismic risk. Analyzing seismicity through statistical methods holds significant scientific value for understanding tectonic conditions and assessing earthquake risk. However, seismic monitoring capacity in this region remains limited, and earthquake frequency is low, complicating efforts to improve earthquake catalogs through enhanced identification and localization techniques. Bi-scale empirical probability integral transformation (BEPIT), a statistical method, can address these data gaps by supplementing missing events shortly after moderate to large earthquakes, resulting in a more reliable statistical data set. In this study, we analyzed six earthquake sequences with magnitudes of MS ≥ 6.0 that occurred in northeastern Tibet since 2009, following the upgrade of the regional seismic network. Using BEPIT, we supplemented short-term missing aftershocks in these sequences, creating a more complete earthquake catalog. ETAS model parameters and b values for these sequences were then estimated using maximum likelihood methods to analyze parameter variability across sequences. The findings indicate that the b value is low, reflecting relatively high regional stress. The background seismicity rate is very low, with most mainshocks in these sequences being background events rather than foreshock-driven events. The p-parameter of the ETAS model is high, indicating that aftershocks decay relatively quickly, while the α-parameter is also elevated, suggesting that aftershocks are predominantly induced by the mainshock. These conditions suggest that earthquake prediction in this region is challenging through seismicity analysis alone, and alternative approaches integrating non-seismic data, such as electromagnetic and fluid monitoring, may offer more viable solutions. This study provides valuable insights into earthquake forecasting in the northeastern Tibetan Plateau.

Stochastic event-based probabilistic earthquake risk assessment framework for Uganda: towards informing the National Policy for Disaster preparedness and management

Abstract Catastrophic earthquakes in Uganda have the potential for detrimental consequences on the socio-economic welfare and resilience of communities. Despite considerable efforts in predicting earthquake risk across Africa, a national comprehensive seismic risk study for Uganda does not exist. With increasing population, urbanisation and rapid construction, seismic risk is escalating fast and is compounded by the high vulnerability of buildings and scanty disaster prevention and mitigation strategies. This study uses the probabilistic event-based risk calculator of the OpenQuake-engine to assess potential risks resulting from future earthquakes. Although the building exposure model is largely inferred and projected from the national population and housing census of 2014, total replacement costs are obtained by performing series of interviews with local engineering practitioners. Analytical vulnerability curves are selected from Global Earthquake Model (GEM) database. Seismic hazard studies confirm that western Uganda is exposed to the highest level of seismicity where peak ground accelerations on rock ground can reach up to 0.27 g over a 475-year return period. Relative to Uganda’s gross domestic product, the associated seismic risk estimates indicate mean economic loss ratios of 0.36%, 2.72% and 4.94% over 10, 50 and 100-year return periods respectively; with mean annual economic loss of US$ 74.7 million (0.34% relative to the total replacement value) and annual deaths averaging 71 persons across the whole country. It is envisaged that the findings will inform strategic land use planning patterns, earthquake insurance pricing and foster the continuous improvement of Uganda’s National Policy for Disaster Preparedness and Management.

Earthquake-based multi-hazard resilience assessment: a case study of Istanbul, Turkey (neighborhood level)

We developed a model integrating 28 criteria spanning social, economic, community, environmental, and physical dimensions to evaluate earthquake resilience of Istanbul, a city with a population of 16 million and significant seismic risk, at both district and subdistrict/neighborhood levels. The resilience assessment uses the Bayesian Best-Worst Method, a multi-criteria decision-making framework that combines expert knowledge and statistical assessments. The results reveal that Istanbul’s overall Resilience Score (RS) is 0.48, on a 0-1 scale, suggesting a moderate capacity to endure and recover from seismic events. Catalca, Adalar, and Arnavutkoy rank among the most resilient districts, whereas Esenler and Gungoren exhibit lower resilience. On a subdistrict level, Suleymaniye (Fatih) has the highest RS at 0.59, while Yavuz Sultan Selim (Fatih) ranks the lowest with 0.22. These findings provide actionable and practical data-driven insights for policymakers and urban planners, underscoring the need for targeted interventions to improve resilience in high-risk areas in Istanbul.

A Review on Seismic Vulnerability Assessment with Emphasis on Structural Health Monitoring

Abstract Earthquake-prone regions like the circum-Pacific belt necessitate proactive measures to mitigate seismic risks. This study explores the synergy between Seismic Vulnerability Assessment (SVA) and Structural Health Monitoring (SHM) for enhanced infrastructure resilience. The researchers integrate machine learning algorithms with diverse sensors, including seismometers and accelerometers, to analyze vibration data for non-destructive testing. This multifaceted approach, encompassing ground-penetrating radar for subsurface analysis and acoustic emission/ultrasonic testing for internal assessment, refines SHM precision for pre-emptive vulnerability identification in various materials and configurations. By analyzing existing SVA studies focused on SHM, the authors ad-dress current gaps in monitoring practices, aiming to create more robust, scalable, and adaptable solutions for diverse structures and environments. The research dissects various SHM methods, analyzes their parameters and prototypes, and examines challenges in extant structure assessments. The authors propose com-prehensive strategies and solutions to bridge research gaps, leveraging non-destructive methods like the rebound hammer test and ferro scanning alongside computer-aided structural design software and strategic retrofitting. The authors aim to advance SHM for effective and interdisciplinary seismic risk mitigation strategies through collaborative efforts across academia, industry, and government sectors.

Simulating seismic wavefields using generative artificial intelligence

Simulating realistic seismic wavefields is crucial for a range of seismic tasks, including acquisition designing, imaging, and inversion. Conventional numerical seismic wave simulators are computationally expensive for large 3D models, and discrepancies between simulated and observed waveforms arise from wave equation selection and input physical parameters such as the subsurface elastic models and the source parameters. To address these challenges, we adopt a data-driven artificial intelligence approach and propose a conditional generative modeling (CGM) framework for seismic wave simulation. The novel CGM framework learns complex 3D wave physics and subsurface heterogeneities from the observed data without relying on explicit physics constraints. As a result, trained CGM-based models act as stochastic wave-propagation operators encoded with a local subsurface model and a local moment tensor solution defined by training data sets. Given these models, we can simulate multicomponent seismic data for arbitrary acquisition settings within the area of the observation, using source and receiver geometries and source parameters as input conditional variables. In this study, we develop four models within the CGM framework — CGM-GM-1D/3D, CGM-Wave, and CGM-FAS — and demonstrate their performance using two seismic data sets: a small low-density data set of natural earthquake waveforms from the San Francisco Bay Area, a region with high seismic risks, and a large high-density data set from induced seismicity records of the Geysers geothermal field. The CGM framework reproduces the waveforms, the spectra, and the kinematic features of the real observations, demonstrating the ability to generate waveforms for arbitrary source locations, receiver locations, and source parameters. We address key challenges, including data density, acquisition geometry, scaling, and generation variability, and we outline future directions for advancing the CGM framework in seismic applications and beyond.

Analysis And Improvement of Poor Bearing Capacity Soils in Düzce Province by Jet grouting Method

Turkey is situated in an earthquake-prone zone, and due to its geographic location, Düzce is among the most hazardous regions in the country and the world. The seismic risk in Düzce province is significantly influenced by the North Anatolian Fault (NAF), which is characterized by an active tectonic structure. This area has experienced earthquakes of varying magnitudes both before and after the instrumental recording period. Especially, two significant earthquakes occurred in 1999: on August 17, an earthquake with a moment magnitude of Mw = 7.4 struck the Adapazarı-İzmit region. Shortly after, on November 12, 1999, another earthquake with a moment magnitude of Mw=7.1 hit Düzce area at approximately 18:57, lasting for about 30 seconds. During the August 17 earthquake, the eastern section of the Düzce fault, measuring 43 km, was activated. The November 12 earthquake is considered to have been triggered by the previously unbroken eastern section of the Düzce fault as a result of the initial faulting. In the city center, the soil composition mainly consists of fine gravel and sandy gravel in certain areas. Previous earthquakes have resulted in structural damages primarily due to bearing capacity issues, and liquefaction phenomena have also been observed in some locations. In this study, the soil beneath a planned three-story building in Düzce, an area identified having a liquefaction risk, was improved using the jet grout method.

Adapting Rapid Visual Screening (RVS) for Seismic risk assessment in developing countries: a case study of Dir City, Pakistan

Frequent seismic activity in Pakistan, exemplified by the devastating 2005 Kashmir earthquake, underscores the need for robust methods to assess building vulnerability. This study focuses on adapting FEMA's Rapid Visual Screening (RVS) methodology for application in developing countries like Pakistan, with Dir City which is situated in Zone III according to seismic hazard maps of Building Code of Pakistan in Khyber Pakhtunkhwa serving as a case study. The majority of the city's structures are old, non-engineered and were built by local masons, therefore they could be in danger during a seismic event. Recognizing local challenges, the FEMA Data Collection Form was modified to incorporate region-specific attributes such as slope, foundation type, liquefaction potential, and structural configurations, including unreinforced masonry, confined masonry, stone masonry, and RC frames with URM infill. The methodology emphasizes the use of score modifiers to account for material and construction practices unique to the region. This paper discusses the adaptation process, challenges in implementing RVS in resource-limited settings, and the importance of localized frameworks for seismic risk assessment. The study aims to provide a methodological foundation for future efforts to enhance seismic resilience in high-risk zones.

National seismic risk assessment: an overview and practical guide

National seismic risk assessment: an overview and practical guide

Probabilistic parametric analysis of capacity, fragility and expected seismic damage of framed reinforced concrete buildings

Abstract 1160 framed reinforced concrete buildings, holding 3–13 stories, have been modelled in a probabilistic way. Capacity spectra have been calculated and characterized by means of 5 parameters: initial stiffness, ultimate capacity (Sdu, Sau), μ and σ. μ is related to ductility, and σ controls the way the building strength degrades. A strong correlation among these five parameters has been found. Moreover, the also high correlation between the initial stiffness and the number of stories allows to define median and percentile capacity spectra starting from the number of stories. Afterwards, for each number of stories, a fragility analysis is performed for the median capacity spectrum. The Barcelona city, in Spain, is used to depict the expected physical damage for a probabilistic and a deterministic seismic hazard scenario. Two procedures, based on the bilinear form of the capacity spectrum and on the Park and Ang damage index, are used to define damage states thresholds. The probabilistic scenario is more damaging than the deterministic one is and, damage states thresholds based on the capacity spectrum lead to more damages than the ones based on the Park and Ang damage index. Mean damage states close to 2.0 are obtained, being 25% and 6% the probabilities of Severe, and Complete, damage states, which would have significant impact on the number of homeless people and victims. Maps of expected damages depict the physical seismic risk of the probabilistic scenario. These scenarios are very useful for emergency planning and for earthquake protection.

Seismic fragility, loss, and resiliency of old railway masonry arch bridges under near-field ground motion

ABSTRACT Iran’s historic railway arch bridges, many over a century old, face seismic risks due to their original design neglecting lateral loads. As they are often situated in seismically active zones near fault lines, understanding their seismic vulnerability is crucial. The goal of this study is to assess the seismic fragility and loss estimation of such bridges under various seismic events. To this end, the seismic performance of two masonry arch bridges, specifically a bridge with two long spans, named 2LS20, and a bridge featuring five small spans, named 5SS06, along the old Tehran-Qom railway line, is assessed under a set of 28 near-field and 22 far-fault earthquake records. Throughout the nonlinear incremental dynamic analysis, seismic fragility analyses were performed to ascertain the likelihood of damage across distinct limit states for each bridge model under the action of near and far-fault ground motions. Subsequently, throughout the resiliency analysis, the recovery process was simulated to assess the progress of bridge functionality with time. Moreover, seismic loss analyses were conducted to assess the comparative economic viability of the studied models. The results indicate the 5SS06 bridge for lower vulnerability and repair costs with near-fault motions posing higher risks, particularly for extreme damage conditions. Resilience varies significantly based on recovery models, with the 5SS06 bridge showing more effortless recovery under far-fault motions and higher resilience factors under near-fault records. Additionally, predictive equations were proposed for managing seismic risks in similar bridge infrastructures, particularly in Iran’s railway network, aiding resilience and risk mitigation planning.

Seismic risk and intangible landscape heritage on São Jorge

Seismic risk and intangible landscape heritage on São Jorge

Calibrating collapse and fatality rates for the assessment of fatalities due to earthquakes

In recent decades, hundreds of studies have covered seismic vulnerability assessment and the derivation of fragility models. However, these studies primarily focus on structural damage and the associated repair costs. Assessing fatalities requires an evaluation of the proportion of damaged buildings that suffer partial or total collapse, as well as the expected fatality rates for occupants inside those collapsed buildings. To support these modeling needs, we reviewed past studies on casualties, existing proposals for collapse and fatality rates, and detailed damage databases that characterize different collapse mechanisms. Based on this review, collapse and fatality rates are proposed relative to a baseline building class. These rates are further calibrated considering reported fatalities since 1950 and the average annual fatalities estimated by the Global Seismic Risk Model of the Global Earthquake Model (GEM) Foundation. Results show that the probability of collapse tends to decrease with the number of stories, while fatality rates have the opposite trend. Furthermore, an open-access database of calibrated collapse and fatality rates is provided and can be used to assess fatalities due to earthquake scenarios or in probabilistic seismic risk analysis.

Enhancing seismic risk assessment through probabilistic analysis of liquefaction potential index: a computational intelligence approach

ABSTRACT This study proposes a novel computational intelligence approach for enhancing seismic risk assessment through probabilistic analysis of the liquefaction potential index (LPI). The methodology leverages a hybridised model that combines backpropagation neural networks (BcNN) with an improved firefly algorithm (IFF) to predict LPI values accurately and efficiently. The BcNN-IFF model was trained and validated using a comprehensive dataset comprising the geotechnical and seismic parameters from various earthquake-prone regions worldwide. Feature importance analysis revealed that total stress (σv’) and peak ground acceleration (amax) were the most influential factors in the LPI prediction. The BcNN-IFF model demonstrated superior performance compared to other hybrid models, achieving 91% accuracy in the training phase and 85% accuracy in the testing phase, with low root mean square error and mean absolute error values. The practical implications of the BcNN-IFF model highlights its potential for reliable liquefaction susceptibility assessments, particularly in resource-constrained environments. Future research directions are outlined, emphasising the need for uncertainty quantification, data augmentation, and computational efficiency enhancements to improve the applicability of the model in real-world scenarios. This study advances state-of-the-art computational modelling and provides a foundation for integrating such tools into geotechnical engineering practices for effective seismic risk assessment and infrastructure resilience.

Design of Resilient Flood Resistant RC Structure in Sloped Areas

Flooding, worsened by urban development in vulnerable areas, requires both structural and non-structural measures to manage risks. Current RC building codes like IS 456:2000 and ASCE 07-2002 address seismic and wind loads but often overlook flood-specific impacts. This project integrates flood loads into RC building analysis using CSI-ETABS, simulating flood conditions with a 3-meter depth and 3 m/s flow velocity across RC buildings on both flat and 20-degree slope surfaces with fixed-base and pile foundations. Key findings reveal that base shear increases by 51.9% in pile versus fixed foundations. Displacement rises by up to 8.26% in regular models on flat surfaces, while story acceleration increases by 12.34%, indicating heightened sensitivity to flood loads in certain configurations. Further analysis highlights that bending moments are highest in 0-degree irregular models with pile foundations but decrease by 4.26% when comparing flood to earthquake loads. Vertical irregularities raise seismic risk by 25%, while regular models on a 20-degree slope with either fixed or pile foundations demonstrate greater stability. To ensure safety under flood and seismic loads, recommended structural dimensions include beams of 230 x 600 mm, columns of 500 x 500 mm, and a slab thickness of 150 mm. These results underscore the need to include flood load considerations in building codes to enhance urban resilience against severe flood events.

Seismic Risk Classification of Building Clusters Using MST Clustering and UAV Remote Sensing.

The fundamental attribute that is essential for the seismic capacity assessment of houses is the building structure type. Conventionally, remote sensing assessment of the seismic capacity for houses has been based on the image features of individual houses, instead of the spatial similarity between them. To enhance the classification accuracy of house structure types, this work proposes a minimum spanning tree (MST) house clustering structure type classification method based on the spatial similarity of houses. First, the method employs the geometric characteristics of residential buildings to calculate the Gestalt factor that characterizes the visual distance. Subsequently, a Delaunay triangular mesh is constructed to create a proximity map between the houses, with the MST generated using visual distance as the weighting factor. Then, the spatial proximity similarity of house clusters is obtained through pruning. Finally, a support vector machine is employed to categorize the architectural structure of the housing complex, viz., simple houses, brick-concrete houses, and frame houses. This classification is based on the geometric, textural, height, and spatial distribution characteristics of the houses. We have conducted a remote sensing classification experiment of house structure types with Zhushan County, Hubei Province as the study area. The results show that the MST clustering method improves the classification accuracy of brick-concrete houses to 95.4% and the classification accuracy of simple houses to 93.4%. Compared to the single-family-based classification method of building structure types, the classification accuracy of frame-structure buildings is improved to 87%. The Kappa coefficient increased to 0.89. This study significantly improves the classification accuracy of building structure types by introducing spatial similarity. Furthermore, it shows the potential for spatial similarity in classifying building structure types.