Earthquake-prone Regions Research Articles

Development of a regionally consistent and fully probabilistic earthquake risk model for Central Asia

Abstract. A fully probabilistic earthquake risk model was developed for five countries in Central Asia, providing updated earthquake loss estimates with a higher level of detail on all components with respect to previous studies in the region. Additionally, a regionally consistent approach that, on the one hand, allows for direct comparisons at different disaggregation levels (e.g., country and oblast) was used. On the other hand, this updated earthquake risk assessment aims to facilitate initiating a policy dialogue regarding national and regional disaster risk financing and insurance applications. This earthquake risk model made use of a regional probabilistic seismic hazard analysis and a comprehensive exposure database on which different types of assets and sectors were included and for which two scenarios (the years 2020 and 2080) were modeled. For each type of asset, a unique vulnerability function was derived and later used for convolution with the hazard data, which allowed for the estimation of the loss exceedance curve, at different disaggregation levels, from where other risk metrics such as the average annual loss (AAL) and specific return period losses were obtained. The regional earthquake AAL for the five countries and for the 2020 exposure scenario has been estimated to be around USD 2 billion, with the Kyrgyz Republic and Tajikistan being the countries with the highest earthquake risk levels in the region. Besides the probabilistic earthquake risk results, as-if scenarios were modeled using a pseudo-deterministic approach to assess the human and economic losses for realistic and representative earthquakes for the main cities within earthquake-prone regions in the five countries of the study area.

Sustainable mitigation method for earthquake-induced liquefaction using gravel-tire chips mixture: an integrated approach

This study investigates the potential of using Gravel-Tire Chips Mixture (GTCM) drains to mitigate soil liquefaction during earthquakes through an integrated experimental and numerical approach. The experiments involved a controlled setup with three distinct scenarios, evaluating the effectiveness of GTCM drain configurations in controlling excess pore water pressure in foundation soils, thereby reducing liquefaction and building settlement under seismic loading. Numerical simulations were performed to complement the physical tests and provide deeper insights into soil-drain interactions during dynamic events. The experimental results showed that GTCM drains significantly reduced settlement, with a reduction of up to 40 times in cases where GTCM drains were used compared to unmitigated conditions. Additionally, the presence of GTCM drains was effective in dissipating excess pore water pressure, which is a primary cause of liquefaction. Numerical simulations confirmed the experimental findings, indicating minimal pore water pressure buildup and reduced deformation in soils reinforced with GTCM drains. This research demonstrates that GTCM drains provide a sustainable and effective method to mitigate liquefaction and reduce earthquake-induced structural damage. The findings emphasize the importance of strategic drain placement and highlight the environmental benefits of repurposing waste materials such as tires for geotechnical applications, offering a cost-effective solution for enhancing infrastructure resilience in earthquake-prone regions.

Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities

Abstract Enhancing resilience against seismic hazards in earthquake-prone regions is essential for reducing the devastating impacts of disasters. Seismic resilience refers to a community’s ability to withstand and recover from earthquake impacts, while preparedness gaps are the areas where current measures are insufficient to effectively respond to or mitigate earthquake damage. This study focuses on Montenegro – a region with frequent seismic activity – aiming to assess resilience levels, identify critical gaps in preparedness, and evaluate the effectiveness of existing response strategies. Using qualitative methods, including semi-structured interviews, the research gathered insights from residents of Montenegro’s most vulnerable cities: Nikšić, Podgorica, Bar, Kotor, Cetinje, Budva, Herceg Novi, and Berane. Participants, chosen for their first-hand experience with significant earthquake impacts, provided valuable perspectives on various aspects of resilience, from local government response to individual preparedness. This research revealed significant disparities in resilience across communities: for instance, approximately 62.5% of the respondents highlighted inadequate education as a barrier to effective earthquake preparedness, and only 37.5% reported awareness of basic earthquake response procedures. Furthermore, while some communities, such as urban areas with accessible services, reported higher preparedness levels, rural areas showed deficiencies, with 50% of the respondents from these areas identifying a lack of organized drills and limited public awareness initiatives. These findings underscore the urgent need for community-specific preparedness programs and enhancements in both structural resilience and public education to bolster community readiness effectively. Also, findings highlight the need for customized preparedness programs tailored to specific community needs, alongside improvements in structural safety measures and educational outreach. The study underscores the importance of a comprehensive approach involving detailed risk assessments, community-focused preparedness training, and stronger public awareness initiatives. Furthermore, the study calls for enhanced local government capabilities to sustain proactive response measures, including rapid mobilization of emergency resources and regular disaster simulations, to build long-term resilience across communities.

Teleseismic virtual-source reverse time migration of upper crustal structures in the Three Gorges region, China

Summary The Three Gorges (TG) in central China is an earthquake prone region that lacks active-source seismic surveys, hence requiring high-resolution passive seismic imaging to reveal crustal structures in detail. While teleseismic virtual-source reflection (TVR) profiling is effective for imaging gently dipping upper crustal structures, it is unsuitable for mapping steep structures beneath rugged topography in the TG region. Here we develop a teleseismic virtual-source reverse time migration (TV-RTM) to improve the imaging of steep structures. Synthetic tests are conducted to demonstrate the validity and resolution of the TV-RTM method. To mitigate the imaging inaccuracy due to sparse station spacing, we interpolate direct-arrival waveforms to achieve a doubling of the minimally required station spacing from 2 km to 4 km. The TV-RTM images of the upper crustal structure beneath a 2D array in the TG region reveal significant fold and thrust structures that correlate well with the surface geology and tectonic framework. The resolution of the images is assessed using 2D and 3D synthetic models with the station and source geometry of field data. The TV-RTM method provides a new passive seismic solution for studying upper crustal structures in regions that lack active-source seismic data.

Evaluating the Seismic Resilience of Newly Constructed Concrete Building in Zinda Jan District After the Devastating October 2023 Earthquake in Herat, Afghanistan

This study aims to evaluate the impact of the October 2023 earthquake on the Zinda Jan district, focusing on the structural integrity of more than 2000 newly constructed buildings situated near fault lines. The specific objective is to identify any vulnerabilities in the design of these structures. Advanced computer analyses, including Linear and Non-linear (Push-over analysis) assessments using Etabs software, were employed to investigate the structural performance of the buildings. The analysis revealed significant insights into the structural integrity of the new constructions. It was observed that certain central columns exhibited inadequate strength, thereby posing a considerable risk of failure during seismic events. This vulnerability primarily stems from a disparity in strength between columns and beams, with the latter being stronger. This research contributes to the field by emphasizing the critical role of meticulous design and analysis in safeguarding buildings located in earthquake-prone regions

Comparative Study of Tube-In-Tube and Bundled Tube System in High Rise Buildings

This study evaluates the seismic performance of Tube-in-Tube and Bundled Tube structural systems for high-rise buildings, following IS 1893 standards. Key parameters, including storey displacement, drift, and shear, were assessed in Seismic Zones 3 and 5. In Seismic Zone 5, the Bundled Tube system reduces storey displacement by about 33% compared to the Tube-in-Tube system. Similarly, in Seismic Zone 3, displacements are lowered by 21-24%. The Tube-in-Tube system exhibits higher storey drift, particularly in Seismic Zone 5, where the Bundled Tube system offers a remarkable 85% reduction. In Seismic Zone 3, this reduction ranges from 45-50%. In terms of storey shear, the Bundled Tube system consistently outperforms the Tube-in-Tube system, reducing shear forces by 24-30% across both seismic zones. Overall, the Bundled Tube system proves to be more effective under seismic conditions, minimizing displacements, drifts, and shear forces, making it the superior choice for high-rise structures in earthquake-prone regions. This improvement in performance contributes to safer structural designs in compliance with IS 1893 standards.

Dynamic Behavior of Rectangular Tanks with Limited Freeboard Under Seismic Loads: Experimental, Analytical and Machine Learning Investigations

Abstract One of the most common types of fluid storage tanks are rectangular concrete tanks placed on the ground. Ensuring the seismic performance of these tanks is crucial as, besides damage and losses during a crisis, they can lead to other crises such as fires or toxic material leaks. One pivotal factor influencing the seismic behavior of these tanks is the height of the freeboard. A comprehensive understanding of how freeboard height impacts the impulsive and convective masses is essential for the effective design and dynamic analysis of rectangular tanks. While numerous numerical and experimental studies have explored this field, the influence of various filling levels, fluid properties, and tank geometries under seismic loading conditions requires further investigation. In this study, both an experimental model and a theoretical model were subjected to different tank fill levels, tank geometries, and various freeboard heights under the influence of different earthquake records. The results showed that tank height and geometry significantly affect the changes in impulsive and convective masses. Notably, in contrast to previous studies, our results demonstrate a nonlinear relationship between freeboard height and the variations in these masses. These new findings are crucial for refining current design practices and improving the resilience of rectangular tanks in earthquake-prone regions.

Improving mental health response in earthquake-prone regions: recommendations following the recent earthquake in Türkiye

We are members of Students’ Education, Empowerment and Development in Mental Health, a group of medical students and psychiatrists from around the globe under the Psychiatry, Medicine and Primary Care Section of the World Psychiatric Association. In this article, we put forward recommendations to help improve the mental health response in disaster-prone regions such as Türkiye. We recommend a three-step multi-tiered mental health response system that could significantly help to address the immediate, short-term and long-term mental health needs of communities directly affected by a disaster. The recommendation draws from the relevant literature and, most importantly, from our lived experiences of living in earthquake-prone countries.

Uncertainty-Aware Seismic Signal Discrimination using Bayesian Convolutional Neural Networks

Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region and achieved an accuracy of 99.1%. Monte Carlo Sampling subsequently creates a 95% prediction interval for probabilities that considers epistemic and aleatoric uncertainties. The ability to visualize both aleatoric and epistemic uncertainties provides decision-makers with information to determine the reliability of seismic signal classifications. Further, the use of Bayesian CNN for seismic signal classification provides a more robust foundation for decision-making and risk assessment in earthquake-prone regions.

Crustal structures and anisotropy in southeastern China: Continental collision and intraplate earthquakes

This study investigates the role of crustal structures and anisotropy in southeastern China regarding intraplate earthquakes within the context of concentrated and perturbed tectonic stresses. The region is characterized by a complex multiphase tectonic history,including the ongoing collision between the Philippine Plate and the South China Sea margin. Utilizing the teleseismic receiver function method, we offer a comprehensive depiction of the crustal structures and layered anisotropy in Guangdong and Fujian, two earthquake-prone areas. Our results reveal that the crustal anisotropy is stratified into two layers, with a slow axis perpendicular to the orogenic belt in the upper-middle crust, whereas the fast axis aligns perpendicularly at lower crust levels. The lateral heterogeneity and anisotropy of the crustal structure emphasize both the boundary delineation and differential movement between continental blocks. The observed increase in density and intensity of intraplate earthquakes across this boundary supports our conclusion regarding a connection between decoupled crustal deformation and seismicity. Specifically, the foliation of metamorphic rocks during the Mesozoic collision generated orogen-parallel fabrics in the upper-middle crust, while ductile shearing resulted in orogen-perpendicular fast directions at lower crust levels. In contrast, mismatch evolution between continental blocks reactivated by present collision provides stress conditions conducive to earthquake occurrence in southeastern China. Our findings provide valuable insights into mechanisms behind intraplate earthquakes as well as tectonic evolution within continental collision zones.Plain Language SummaryThe mechanism of intraplate earthquakes is a topic of great interest in the study of continental collision and regional tectonic evolution. The controlling factors behind these earthquakes are highly complex and uncertain. In this study, we aim to investigate the role of crustal structures and anisotropy in southeastern China within the context of intraplate seismic activity. This region, which is currently experiencing a collision between the Philippine Plate and the South China Sea Margin, has a multiphase tectonic history that includes remnants of paleo-Pacific subduction. By utilizing the receiver function method, we provide a detailed picture of crustal structures and layered anisotropy in Guangdong and Fujian, two earthquake-prone regions in southeastern China. Our findings reveal that anisotropy in the crust can be stratified into two layers: an upper-middle crust and a lower crust with a slow axis and a fast axis, both perpendicular to the orogeny, respectively. The lateral heterogeneity and anisotropy of the crustal structure highlight the boundary and differential movement between the continental blocks. The enhanced density and intensity of the intraplate earthquakes across this boundary provide evidence for a connection between decoupled crustal deformation and seismicity. Specifically, our observations suggest that (1) the Mesozoic continental collision induced metamorphic foliation in the upper-middle crust, (2) ductile shearing is characteristic of the lower crust, and (3) ongoing collision has led to mismatch evolution between continental blocks, which creates stress conditions favorable for earthquake occurrence.

Edy App: Earthquake Mitigation Innovation Using GIS for Disaster Response in Pariaman, Indonesia

Pariaman City, located along the Semangko fault line and the subduction zone of the Indo-Australian and Eurasian plates, is highly vulnerable to earthquakes and tsunamis. The region's susceptibility to large earthquakes, followed by vertical deformations, highlights the need for effective disaster mitigation. In response, this study proposes the development of the Earthquake Buddy Application (EDY App), designed to assist with earthquake mitigation efforts. The EDY App leverages Geographic Information System (GIS) technology to map earthquake-prone areas, provide early warnings, suggest evacuation routes and Temporary Evacuation Sites (TES), and facilitate aid distribution. This Android-based mobile application focuses on four key aspects: education, mitigation, evacuation, and donation. Key features include an Earthquake Vulnerability Map, real-time early warnings, mitigation instructions, TES recommendations with distance and time estimations, emergency contact options, and donation channels. Built with React Native, PHP, and MySQL, and integrated with Google Maps API, the app is designed to be user-friendly and accessible to the public. The EDY App’s contribution to earthquake mitigation lies in its ability to create a disaster-aware community. By offering comprehensive information and support during critical moments, the app can potentially reduce casualties and enhance community preparedness in earthquake-prone regions like Pariaman. Through this innovation, it is expected that residents will be better equipped to respond to natural disasters, promoting a culture of safety and resilience.

Dynamic response analysis of pipelines in gas distribution stations under seismic loads

Seismic loads in earthquake-prone regions significantly affect the structural integrity of pipelines at urban natural gas distribution stations, compromising safety. This study analyzes the dynamic response of pipelines in gas distribution stations under seismic loading and proposes effective measures to mitigate stress concentration. A numerical model of the pipeline in the gas distribution station, based on actual cases, was established. The material’s constitutive relationship was derived from tensile tests. The seismic response spectrum, obtained from ground motion data, was introduced to analyze the pipeline’s mechanical behavior. The dynamic response analysis identified critical stress concentration locations in the pipeline under operational conditions. Based on displacement analysis, the study recommended seismic measures. Results indicated that under seismic loading, higher stress values occurred primarily in pipeline sections with intricate flow pattern variations. The manifold connection was the most vulnerable point, with its average stress being 3.37 times that of the system’s average stress. The proposed seismic measures effectively reduced stress concentration at these critical points, mitigating the earthquake’s impact on the gas distribution station. This study establishes a theoretical framework for improving the seismic design and maintenance of natural gas distribution stations, enhancing their seismic resilience and operational safety.

ГРАДОСТРОИТЕЛЬНЫЕ ОСОБЕННОСТИ ВЫСОТНОГО СТРОИТЕЛЬСТВА В РЕСПУБЛИКЕ КРЫМ

The principles of design and construction of high-rise buildings in seismic areas, the influence of urban planning principles of designing such buildings on architecture and planning solutions of buildings, especially on the example of the Republic of Crimea, are considered. Based on the experience of previous studies, the author has identified the main types of seismic protection, features of their use, advantages and disadvantages that can be used when placing high-rise buildings in the design territories. The objectives of the study are determined by the development of earthquake-prone regions, in particular the Republic of Crimea, and the development of high-rise construction. The research methodology determines the ways to solve problems related to the design and construction of high-rise buildings and structures, taking into account the requirements of urban planning.

Seismic vulnerability assessment of buildings using rapid visual screening for hybrid UMNN-RFO approach

This chapter proposes a hybrid approach for seismic vulnerability assessment of buildings using rapid visual screening (RVS). The Unconstrained Monotonic Neural Network (UMNN) and Red Fox Optimisation (RFO) are combined to create the hybrid technique, thus, the term ‘UMNN-RFO approach’. The UMNN algorithm predicts the RVS score and the RFO algorithm optimizes the RVS score. The RVS methods via Google Maps are being used to examine the Andaman and Nicobar Islands because it is one of the main earthquake prone regions in the world. A score was given after a screening that looked at Reinforced Concrete moment-resisting frames, load-bearing structures, steel frames (SF) and wooden frames (WF). By then, the MATLAB platform will have the proposed model implemented. The proposed approach outperforms all current techniques, including Granular Computing Artificial Neural Network (GrC-ANN), Particle Swarm Optimization-Back Propagation Neural Network (PSO-BPNN) and Genetic Algorithm-Artificial Neural Network (GA-ANN). The proposed method’s accuracy is 90%, determination coefficient is 0.91 and the Root Mean Square error is 0.27%. From the result, the proposed method concluded that older structures are more likely to sustain greater damage shortly than more recently built ones based on the score.

Cyclic loading experiments on seismic performance of precast concrete superposed shear walls with CFST end columns

The seismic performance of precast concrete shear walls is a major concern when considering their application in earthquake-prone regions. To improve the seismic performance and constructability of conventional precast concrete superposed shear walls, an innovative design, termed Precast Concrete Superposed Shear Walls with CFST End Columns (PCSSWEC), has been proposed. Given the limited research on the seismic performance of PCSSWECs, cyclic loading experiments were conducted on three full-scale PCSSWEC specimens and one monolithic RC shear wall specimen. The design axial compression ratio (ACR) and the thickness of the steel tube were employed as research variables. Experimental observations and results gave a comprehensive view of the hysteretic behavior of PCSSWECs, reflecting a favorable seismic performance (in terms of failure modes, global and local damage, force-displacement responses, load-bearing capacity, stiffness and strength degradation, deformability, ductility, and energy dissipation capacity). The experiment also revealed significant differences between PCSSWECs and monolithic RC shear walls in concrete crack development and deformation characteristics, confirming that the CFST columns and the wall panel could work together until the failure phase. Moderately increasing the ACR (0.3–0.5) was beneficial for PCSSWECs. However, increasing the steel tube thickness (3.5 mm–5.0 mm) could lead to unfavorable degradation in strength and stiffness and weaken ductility. In addition, a simplified method was proposed to evaluate the flexural strength of PCSSWECs.

Seismic performance of precast UHPC pipe pile with two pile-cap beam connection types: An experimental and numerical study

To develop an effective pile foundation scheme for earthquake-prone regions, this study introduces a novel pile structure that integrates ultra-high-performance concrete (UHPC) with traditional prestressed high-strength concrete (PHC) pipe piles. The research focuses on assessing the impact of various connection forms between the pile and cap beam on the seismic performance of bridge substructures. Two 1/3-scale specimens were meticulously designed and tested: one featuring a cast-in-place (CIP) connection and the other incorporating precast assembly (PA) connection between the pipe pile and cap beam. Cyclic loading tests were conducted to evaluate the failure mode, lateral capacity, ductility, energy dissipation ability, residual displacement, rebar strain, curvature distribution and rotation of UHPC pipe piles with the two connection forms. The results indicate that the specimen with a CIP connection exhibits a higher horizontal load capacity and stronger energy dissipation ability, while the specimen with the PA connection displays superior self-centering ability, increased ductility, and causes less damage to the cap beam. Finally, finite element models were developed to analyze the effects of design parameters on the seismic performance of the pile connected by the two methods. This research may provide valuable design guidance for incorporating UHPC in pile foundations. To facilitate its practical implementation in engineering projects, further theoretical and experimental research is recommended in this paper.

The 2023 Mw 6.8 Morocco earthquake induced atmospheric and ionospheric anomalies

Earth observations through Global Navigation Satellite System (GNSS) and Remote Sensing (RS) technologies play a significant role in natural hazard surveillance, particularly in the context of earthquake prediction and detection. This study introduces a distinctive Deep Learning (DL) based approach to identify ionospheric and atmospheric precursors, utilizing data from multiple satellite sources and provides a comprehensive analysis of spatiotemporally varying precursors, contributing to the understanding and monitoring of seismic activity in earthquake-prone regions. In our investigation of the Morocco earthquake on September 08, 2023 (Mw 6.8), we analyzed various precursors including Total Electron Content (TEC), Air Pressure (AP), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Air Temperature (AT). Our study aims to identify a synchronized anomalous window of potential earthquake precursors using Standard Deviation (STDEV), Continuous Wavelet Transform (CWT), and Long Short-Term Memory Inputs (LSTM) network. Both statistical and deep learning methods revealed abnormal fluctuations as precursors occurring within 8–9 days before the earthquake near the epicenter. Additionally, we detected geomagnetic anomalies in the ionosphere 6 days prior to and 4 days after the earthquake, coinciding with active geomagnetic storm days. This research underlined the importance of combining multiple earthquake precursors using statistical and deep learning approaches to support the understanding of the Lithosphere-Atmosphere-Ionosphere-Coupling (LAIC) phenomena.

Enhancing soil liquefaction risk assessment with metaheuristics and hybrid learning techniques

ABSTRACT Soil liquefaction has garnered significant research attention over several decades due to its profound impact on infrastructure systems in earthquake-prone regions. Given the limitations inherent in the assumptions and approximations of conventional methods, machine learning has emerged as a robust and efficient approach for predicting soil liquefaction potential. This study aims to develop a synergistic JS-CNN-XGB model, unifying the Jellyfish Search (JS) optimizer with a hybrid deep/machine learning model. This amalgamation leverages the feature extraction capabilities of the Convolutional Neural Network (CNN) alongside the classification prowess of the eXtreme Gradient Boosting (XGB) algorithm. The proposed model seamlessly integrates into a user-friendly prediction system, streamlining the liquefaction prediction process. Validation is achieved through case studies involving historical earthquake recordings with diverse classification ratios and varying input attribute quantities. Compared to existing studies, our proposed system showcases a notable 6.2% enhancement in overall liquefaction assessment accuracy, demonstrating improved detection capabilities in imbalanced and balanced datasets. In conclusion, this study underscores an automated system that presents a robust solution to the challenges posed by liquefaction potential assessment in geotechnical engineering.

Unlocking Resilience: Examining the Influence of Fluid Viscous Dampers on Seismic Performance of Reinforced-concrete Structures in Earthquake-prone Regions

Unlocking Resilience: Examining the Influence of Fluid Viscous Dampers on Seismic Performance of Reinforced-concrete Structures in Earthquake-prone Regions

Fundamental Frequency Prediction of Historic Masonry Towers Based on Artificial Neural Networks

ABSTRACT Historic masonry towers, with their brittle materials, slenderness, and distinctive shapes, are highly susceptible to lateral excitations. The urgency of preserving surviving ones in earthquake-prone regions has become apparent. There is a prioritization of identifying and reinforcing the most vulnerable masonry towers. Predictions are based on the earthquake spectrum specific to each region, effectively alerting to the seismic vulnerabilities of towers constructed within those areas. Rather than formulating relationships based on known geometrical parameters, this study relies on an Artificial Neural Network (ANN)-based model to promptly estimate the fundamental frequency of masonry towers. Measurements taken from 19 actual masonry towers are utilized for training the networks. Various distinct tower parameters as well as their combinations are considered. Only geometrical information is taken into account while material properties are not considered. The performances of ANNs are directly compared to some empirical equations. The ANN-based techniques are evaluated by testing with 20 different towers that are not considered in the training process. The proposed ANN tool demonstrates practicality and robustness when estimating the lowest frequency of masonry towers based only on geometrical information. The slenderness ratio that is rarely considered in existing equations remarkably enhances the accuracy of fundamental frequency anticipation.