Search Results (307)

Multiple uncertainties in traffic demand fluctuations and infrastructure vulnerability during seismic events pose significant challenges for the resilience assessment of highway transportation networks (HTNs). While Monte Carlo simulation remains the dominant approach for uncertainty propagation, its high computational cost limits its scalability, particularly in metropolitan-scale networks. This study proposes an EQResNet framework for accelerated post-earthquake resilience assessment of HTNs. The model integrates network topology, interregional traffic demand, and roadway characteristics into a streamlined deep neural network architecture. A comprehensive surrogate modeling strategy is developed to replace conventional traffic simulation modules, including highway status realization, shortest path computation, and traffic flow assignment. Combined with seismic fragility models and recovery functions for regional bridges, the framework captures the dynamic evolution of HTN functionality following seismic events. A multi-dimensional resilience evaluation system is also established to quantify network performance from emergency response and recovery perspectives. A case study on the Sioux Falls network under probabilistic earthquake scenarios demonstrates the effectiveness of the proposed method, achieving 95% prediction accuracy while reducing computational time by 90% compared to traditional numerical simulations. The results highlight the framework’s potential as a scalable, efficient, and reliable tool for large-scale post-disaster transportation system analysis. Full article

The advanced determination of the type (foreshock–aftershock–swarm) of an ongoing seismic cluster is quite challenging; only retrospective solutions have thus far been proposed. In the period of January–March 2025, a seismic cluster, recorded between Santorini volcano and Amorgos Island, South Aegean Sea, caused considerable social concern. A rapid increase in both the seismicity rate and the earthquake magnitudes was noted until the mainshock of M_L = 5.3 on 10 February; afterwards, activity gradually diminished. Fault-plane solutions indicated SW-NE normal faulting. The epicenters moved with a mean velocity of ~0.72 km/day from SW to NE up to the mainshock area at a distance of ~25 km. Crucial questions publicly emerged during the cluster. Was it a foreshock–aftershock activity or a swarm of possibly volcanic origin? We performed real-time discrimination of the cluster type based on a daily re-evaluation of the space–time–magnitude changes and their significance relative to background seismicity using earthquake statistics and the topological metric betweenness centrality. Our findings were periodically documented during the ongoing cluster starting from the fourth cluster day (2 February 2025), at which point we determined that it was a foreshock and not a case of seismic swarm. The third day after the M_L = 5.3 mainshock, a typical aftershock decay was detected. The observed foreshock properties favored a cascade mechanism, likely facilitated by non-volcanic material softening and the likely subdiffusion processes in a dense fault network. This mechanism was possibly combined with an aseismic nucleation process if transient geodetic deformation was present. No significant aftershock expansion towards the NE was noted, possibly due to the presence of a geometrical fault barrier east of the Anydros Ridge. The 2025 activity offered an excellent opportunity to investigate deciphering the type of ongoing seismicity cluster for real-time discrimination between foreshocks, aftershocks, and swarms. Full article

Through a systematic review of sustainable rural dwelling recovery, this study offers a broader reflection on retrofitting practices, viewing eco-efficiency as a means to enhance both cultural heritage and agricultural landscapes. The work is based on the assumption that vernacular architecture in rural contexts embodies historical, cultural, and typological values worthy of preservation, while remaining adaptable to reuse through eco-efficient solutions and technological innovation. Using the PRISMA protocol, 115 scientific contributions were selected from 1711 initial records and classified into four macro-groups: landscape relationships; seismic and energy retrofitting; construction techniques and innovative materials; and morphological–typological analysis. Results show a predominance (over 50%) of passive design strategies, compatible materials, and low-impact techniques, while active systems are applied more selectively to protect cultural integrity. The study identifies replicable methodological models combining sustainability, cultural continuity, and functional adaptation, offering recommendations for future operational guidelines. Conscious eco-efficient retrofitting thus emerges as a strategic tool for the integrated valorization of rural landscapes and heritage. Full article

Radon anomalies have long been explored as potential geochemical precursors to seismic activity due to their responsiveness to subsurface stress variations. However, before this study, the scientific progression of this research domain had not been systematically examined through a quantitative lens. This study presents a comprehensive bibliometric analysis of 379 articles published between 1977 and 2025 and indexed in Scopus and Web of Science. Utilizing the Bibliometrix R-package and its Biblioshiny interface, the analysis investigates temporal publication trends, leading countries, institutions, international collaboration networks, and thematic evolution. The results reveal a marked increase in research output since 2010, with China, India, and Italy emerging as the most prolific contributors. Thematic mapping indicates a shift from conventional geochemical monitoring toward the integration of artificial intelligence techniques, such as decision trees and neural networks, for anomaly detection and predictive modeling. Notwithstanding this methodological evolution, core research themes remain centered on radon concentration monitoring and the analysis of environmental parameters. Overall, the findings highlight the coexistence of traditional and emerging approaches, emphasizing the importance of standardized methodologies and interdisciplinary collaboration. This bibliometric synthesis provides strategic insights to inform future research and strengthen the role of radon monitoring in seismic early warning systems. Full article

This study presents a scenario-based, two-dimensional flood modeling approach to assess the potential downstream impacts of a piping-induced dam failure triggered by seismic activity. The case study focuses on the Doğantepe Dam in northwestern Türkiye, located near an active branch of the North Anatolian Fault. Critical deformation zones were previously identified through PLAXIS 2D seismic analyses, which served as the physical basis for a dam break scenario. This scenario was modeled using the HEC-RAS 2D platform, incorporating high-resolution topographic data, reservoir capacity, and spatially varying Manning’s roughness coefficients. The simulation results show that the flood wave reaches downstream settlements within the first 30 min, with water depths exceeding 3.0 m in low-lying areas and flow velocities surpassing 6.0 m/s, reaching up to 7.0 m/s in narrow sections. Inundation extents and hydraulic parameters such as water depth and duration were spatially mapped to assess flood hazards. The study demonstrates that integrating physically based seismic deformation data with hydrodynamic modeling provides a realistic and applicable framework for evaluating flood risks and informing emergency response planning. Full article

Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by $72\%$ cost reduction compared to FEM in high-dimensional spaces ($p<0.01,n=15$ benchmarks). Third, pioneering cross-domain knowledge transfer through application-specific architectures: TFE-PINN for turbulent flows ($5.12\pm 0.87\%$ error in NASA hypersonic tests), ReconPINN for medical imaging ($▵\mathrm{SSIM}=+0.18\pm 0.04$ on multi-institutional MRI), and SeisPINN for seismic systems ($0.52\pm 0.18$ km localization accuracy). We further present a technological roadmap highlighting three critical directions for PINN 2.0: neuro-symbolic, federated physics learning, and quantum-accelerated optimization. This work provides methodological guidelines and theoretical foundations for next-generation scientific machine learning systems. Full article

Enhancing the seismic resilience of urban water distribution networks (WDNs) requires the improvement of both earthquake resistance and rapid recovery capabilities within the system. This paper proposes a multi-objective method to enhance the seismic resilience of the WDNs, focusing on system restoration capabilities while comprehensively considering the hydraulic recovery index, maintenance time, and maintenance cost. The method utilizes a random simulation approach to generate various damage scenarios for the WDN, considering pipe leakage, pipe bursts, and variations in node flow resulting from changes in water pressure. It characterizes the functions of the WDN through hydraulic service satisfaction and quantifies system resilience using a performance response function. Additionally, it determines the optimal dispatch strategy for emergency repair teams and the optimal emergency repair sequence for earthquake-damaged networks using a genetic algorithm. Furthermore, a comprehensive computational platform has been developed to systematically analyze and optimize seismic resilience strategies for WDNs. The feasibility of the proposed method is demonstrated through an example involving the WDN in Xi’an City. The results indicate that the single-objective seismic resilience improvement method based on the hydraulic recovery index is the most effective for enhancing the seismic resilience of the WDN. In contrast, the multi-objective method proposed in this article reduces repair time by 17.9% and repair costs by 3.4%, while only resulting in a 0.2% decrease in the seismic resilience of the WDN. This method demonstrates the most favorable comprehensive restoration effect, and the success of our method in achieving a symmetrically balanced restoration outcome demonstrates its value. The proposed methodology and software can provide both theoretical frameworks and technical support for urban WDN administrators. Full article

Existing theoretical models of wave-induced flow face challenges in coal applications due to the scarcity of experimental data in the seismic-frequency band. Additionally, traditional viscoelastic combination models exhibit inherent limitations in accurately capturing the attenuation characteristics of rocks. To overcome these constraints, we propose a novel binary classification physical fractal model, which provides a more robust framework for analyzing wave dispersion and attenuation in complex coal. The fractal cell was regarded as an element to re-establish the viscoelastic constitutive equation. In the new constitutive equation, three key fractional orders, $\alpha$, $\beta$, and $\gamma$, emerged. Among them, $\alpha$ mainly affects the attenuation at low frequencies; $\beta$ controls the attenuation in the middle-frequency band; and $\gamma$ dominates the attenuation in the tail-frequency band. After fitting with the measured attenuation data of partially saturated coal samples under variable confining pressures and variable temperature conditions, the results show that this model can effectively represent the attenuation characteristics of elastic wave propagation in coals with different coal structures. It provides a new theoretical model and analysis ideas for the study of elastic wave attenuation in tectonic coals and is of great significance for an in-depth understanding of the physical properties of coals and related geophysical prospecting. Full article

Urban disaster risks are escalating, and university campus emergency shelters (UCESs) are key to alleviating the supply–demand imbalance in emergency shelter services (ESSs) within high-density central urban areas. However, existing studies lacked the measurement of UCES service effectiveness from a regional supply–demand perspective, limiting the ability to guide planning practices. Therefore, we focused on the capacity of UCESs to improve regional supply–demand relationships and developed a service effectiveness evaluation framework for UCESs in the central urban area of Tianjin under an earthquake scenario. We identified emergency shelter spaces within the campuses and developed a campus–city collaborative shelter capacity model to determine their service supply capacity. Then we quantified regional service demand driven by seismic risk. Finally, we quantified the service effectiveness of each UCES by constructing a service effectiveness evaluation model. Results showed that (1) the total shelter capacity and service coverage of 13 UCESs accounted for approximately 32.1% of the central district’s population and 67.5% of its land area, indicating their strong potential to provide large-scale ESSs. (2) Average seismic risk values ranged from 0.200 to 0.260, exhibiting the characteristic of being higher in the south and lower in the north. (3) Service effectiveness was classified into three levels—higher (1.150–1.257), medium (0.957–0.988), and lower (0.842–0.932)—corresponding to planning interventions that can be implemented based on them. This study aims to reveal differences between different UCESs to improve regional supply–demand relationships by evaluating their service effectiveness and supporting refined emergency management and planning decisions. Full article

Amid increasing interest in enhanced oil recovery and carbon geological sequestration programs, improved static reservoir lithofacies models are emerging as a requirement for well-guided project management. Building reservoir models can leverage seismic attribute clustering for seismic facies mapping. One challenge is that machine learning (ML) seismic facies mapping is prone to a wide range of equally possible outcomes when traditional unsupervised ML classification is used. There is a need to constrain ML seismic facies outcomes to limit the predicted seismic facies to those that meet the requirements of geological plausibility for a given depositional setting. To this end, this study utilizes an unsupervised comparative hierarchical and K-means ML classification of the whole 3D seismic data spectrum and a suite of spectral bands to overcome the cluster “facies” number uncertainty in ML data partition algorithms. This comparative ML, which was leveraged with seismic resolution data preconditioning, predicted geologically plausible seismic facies, i.e., seismic facies with spatial continuity, consistent morphology across seismic bands, and two ML algorithms. Furthermore, the variation of seismic facies classes was validated against observed lithofacies at well locations for the Mississippian carbonates of Kansas. The study provides a benchmark for both unsupervised ML seismic facies clustering and an understanding of seismic facies implications for reservoir/saline-aquifer aspects in building reliable static reservoir models. Three-dimensional seismic reflection P-wave data and a suite of well logs and drilling reports constitute the data for predicting seismic facies based on seismic attribute input to hierarchical analysis and K-means clustering models. The results of seismic facies, six facies clusters, are analyzed in integration with the target-interval mineralogy and reservoir porosity. The study unravels the nature of the seismic (litho) facies interplay with porosity and sheds light on interpreting unsupervised machine learning facies in tandem with both reservoir porosity and estimated (Umaa-RHOmaa) mineralogy. Full article

Throughout history, human societies have sought to explain natural phenomena through the lens of mythology. Earthquakes, as sudden and often devastating events, have inspired a range of symbolic and mythological interpretations across different civilizations. It was not until the 18th and 19th centuries that a more positivist and scientific approach began to emerge regarding the explanation of earthquakes, recognizing their origin as stemming from processes occurring beneath the Earth’s surface. A pivotal moment in the emergence of modern seismology was the Lisbon earthquake of 1755, which marked a significant shift towards scientific inquiry. This means that the question of how earthquakes occur has been resolved; thanks to advancements in scientific, geological, and geophysical research, it is now well understood that seismic events result from the collision and movement of lithospheric or tectonic plates. The contemporary challenge that emerges, however, lies in whether such seismic phenomena can be accurately predicted. In this paper, a systematic attempt is made to use techniques based on Grammatical Evolution to determine the magnitude of earthquakes. These techniques use freely available data in which the history of large earthquakes is introduced before the application of the proposed techniques. From the execution of the experiments, it has become clear that the use of these techniques can allow for more effective estimation of the magnitude of earthquakes compared to other machine learning techniques from the relevant literature. Full article

Given the critical role of electrical cabinets in the post-earthquake recovery and emergency response of nuclear power plants (NPPs), a comprehensive assessment of their seismic performance is essential to ensure operational safety. This study analyzed seismic behavior by fabricating an electrical cabinet model based on the dynamic characteristics and field surveys of equipment installed in a Korean-type NPP. A shaking table test with simultaneous tri-axial excitation was conducted, incrementally increasing the seismic motion until damage was observed. A numerical model was then developed based on the experimental results, followed by a seismic response analysis and comparison of results. The findings verified that assuming fixed anchorage conditions in the numerical model may significantly overestimate seismic performance, as it fails to account for the nonlinear behavior of the anchorage system, as well as the superposition between global and local modes caused by cabinet rocking and impact under strong seismic loading. Furthermore, damage and impact at the anchorage amplified acceleration responses, significantly affecting the high-frequency range and the vertical behavior, leading to substantial amplification of the in-cabinet response spectrum. Full article

Pulsed Power Plasma Stimulation (3PS) represents a promising and environmentally favorable alternative to conventional well stimulation techniques for enhancing subsurface permeability. This comprehensive review tracks the evolution of plasma-based rock stimulation, offering insights from key laboratory, numerical, and field-scale studies. The review begins with foundational electrohydraulic discharge concepts and progresses through the evolution of Pulsed Arc Electrohydraulic Discharge (PAED) and the more advanced 3PS systems. High-voltage, ultrafast plasma discharges generate mechanical shockwaves and localized thermal effects that result in complex fracture networks, particularly in tight and crystalline formations. Compared to conventional well stimulation techniques, 3PS reduces water use, avoids chemical additives, and minimizes induced seismicity. Laboratory studies demonstrate significant improvements in permeability, porosity, and fracture intensity, while field trials show an increase in production from oil, gas, and geothermal wells. However, 3PS faces some limitations such as short stimulation radii and logistical constraints in wireline-based delivery systems. Emerging technologies like plasma-assisted drilling and hybrid PDC–plasma tools offer promising integration pathways. Overall, 3PS provides a practical, scalable, low-impact stimulation approach with broad applicability across energy sectors, especially in environmentally sensitive or water-scarce regions. Full article

This study investigates the stability of high-rise wind turbine tower foundations in collapsible loess regions through finite element analysis. The mechanisms by which wind load, extreme rainfall load, and seismic load interact during the dynamic response of a pile foundation under single-action and intercoupling conditions are analyzed. A comprehensive multi-parameter analytical model is developed to evaluate pile foundation stability, incorporating key indicators including pile skin friction, average axial stress of pile groups, horizontal displacement at pile tops, and pile inclination. The results show that, among single-load conditions, seismic loading has the most pronounced impact on foundation stability. The peak horizontal displacement at the pile top induced by seismic loads reaches 10.07 mm, substantially exceeding the effects of wind and rainfall loads, posing a direct threat to wind turbine tower safety. Under coupled loading conditions, notable nonlinear interaction effects emerge. Wind–earthquake coupled loading amplifies horizontal displacement by 1.85 times compared to single seismic loading. Rainfall–earthquake coupled loading reduces the peak of positive skin friction by 20.17%. Notably, all seismic-involved loading combinations significantly compromise the pile foundation safety margin. The seismic load is the dominant influencing factor in various loading conditions, and its coupling with other loads induces nonlinear superposition effects. These findings provide critical insights for wind turbine foundation design in collapsible loess areas and strongly support the need for enhanced seismic considerations in engineering practice. Full article

The deterioration of aging masonry structures poses significant challenges to structural safety, particularly under seismic loading. In response to the growing need for effective retrofitting solutions, stiff-type polyurea (STPU) has emerged as a promising material due to its high tensile strength, durability, and rapid application characteristics. This study investigates the seismic performance of masonry walls retrofitted with STPU through both shaking table tests and finite element analysis (FEA). Three types of specimens (non-strengthened, STPU-strengthened, and STPU + GFRP-strengthened walls) were subjected to out-of-plane seismic loading with additional mass loading to simulate real-world conditions. Experimental results demonstrated that STPU significantly improved the ductility and seismic resistance of masonry walls, with the STPU + GFRP hybrid system showing the highest performance. A simplified micro-model using ABAQUS successfully captured the primary failure modes and load-bearing behavior observed in the experiments. Furthermore, a parametric study on STPU thickness identified 2 mm as the most efficient thickness considering both strengthening effect and material economy. These findings confirm the effectiveness of STPU as a retrofitting material and demonstrate the reliability of the proposed numerical modeling approach in predicting the seismic response of retrofitted masonry structures. Full article