Search Results (636)

Deep and ultra-deep hydrocarbon resources are of strategic importance for energy security. The pronounced ductility of deep reservoirs and faults makes hydraulic fracture propagation and induced seismicity mechanisms fundamentally different from those in conventional brittle reservoirs. This review systematically synthesizes recent theoretical, experimental, and numerical advances in hydraulic-fracturing-induced seismicity, covering triggering mechanisms, fault reactivation risk evaluation, and perspectives. A core distinction is identified: brittle faults exhibit instantaneous stick-slip rupture with high seismic frequency and significant magnitude, whereas ductile faults undergo stable aseismic creep and progressive slip, with long-term deformation prone to delayed large earthquakes—their nucleation shows unique mechanical responses including high stress drop, low rupture velocity, and low seismic radiation efficiency. Subsequently, four major challenges are distilled for risk evaluation systems: insufficient dynamic characterization of mechanical parameters in ductile reservoirs, lack of fracturing-control strategies adapted to ductile behavior, poor understanding of multi-scale slip transition mechanisms, and inadequacy of multi-field coupling models in capturing long-term delayed evolution. Traditional brittle-based risk frameworks cannot characterize the time-dependent slip and progressive reactivation of ductile faults, limiting their applicability to deep reservoirs. Future works are proposed, including refined characterization of mechanical parameters under high temperature and pressure, intelligent full-cycle hydrofracturing control, quantitative criteria for slip activation, and optimization of long-term multi-field coupling models. This study elucidates recent progress in hydrofracturing-induced seismicity mechanisms and quantitative risk assessment in ductile reservoirs, filling a gap in the conventional brittle-dominant research. It also provides theoretical support for seismic risk evaluation and early warning in deep fracturing operations, with significant implications for improving induced seismicity management and ensuring safe, efficient, and sustainable deep-resource development. Full article

This study proposes a regional-scale evaluation framework for assessing the seismic resilience of residential buildings by integrating earthquake monitoring and early-warning capacity, seismic hazard background, and structural seismic capacity. An indicator system consisting of three primary indicators, six secondary indicators, and twelve tertiary indicators was established based on the literature, seismic design codes, disaster risk census data, expert consultation, and data availability. The analytic hierarchy process was used to determine indicator weights, and multi-source data were integrated to evaluate 11 prefecture-level cities in Hebei Province. The results show that structural seismic capacity is the dominant factor affecting the comprehensive ranking, followed by seismic hazard background and earthquake monitoring and early-warning capacity. Cities with a higher combined floor-area proportion of reinforced–concrete and brick–concrete residential buildings and stronger earthquake monitoring and early-warning capacity generally exhibit higher seismic resilience. The proposed framework provides a practical decision-support tool for identifying regional weaknesses, prioritizing retrofitting of existing buildings, and supporting future development of seismic resilience policies and technical guidelines. Full article

Earthquake-induced soil liquefaction represents a severe geohazard causing catastrophic infrastructure failure in prone coastal zones, requiring an advanced environmental spatial assessment for their sustainable land-use planning. This study utilizes advanced computational intelligence models to predict earthquake-induced soil liquefaction in Boumerdès, Algeria, an area heavily affected by the 2003 (M_w 6.8) earthquake. Utilizing a comprehensive subsurface database of 1984 geotechnical records encompassing lithology, hydrogeological configurations, and seismic parameters, advanced deep learning frameworks are developed and optimized via automated Neural Architecture Search (NAS). The continuous Factor of Safety (F_s) is calculated to distinguish stable profiles from vulnerable strata, benchmarking conventional ANN and DNN models against NAS-optimized variants (NAS-ANN and NAS-DNN) using a stratified 5-fold cross-validation scheme. The optimized hybrid NAS-DNN framework effectively captured non-linear soil responses, achieving a training correlation coefficient ($R_{train}$) of 0.9518, a validation coefficient ($R_{validation}$) of 0.8843, and a cross-validated mean $R$ of approximately 0.82, demonstrating improved predictive reliability compared to traditional models. Ultimately, this optimal network is embedded into the ‘GeoLiquefy-AI (v1.0)’ interface. To ensure reliability for safety-critical applications, we integrated a SHAP explainable AI framework, validating the model’s geomechanical logic by mapping physical soil-liquefaction dependencies. This deployment-ready tool enables rapid, transparent hazard calculations, providing a scalable platform for seismic microzonation and proactive urban risk mitigation. Full article

In southern Kazakhstan, landslide control is highly relevant for settlements located in areas with mountainous terrain. This study was conducted to observe the results of applying electrical, seismic and magnetic surveys to investigate a landslide-prone slope in the Almaty Region. The aim was to improve the reliability of geophysical data when assessing the state of the landslide body. For the first time for this landslide, based on a joint analysis of geoelectric and velocity characteristics, the structural heterogeneities associated with unconsolidated and water-saturated soils, as well as fractures, were identified. The combination of data on the elastic, electrical and magnetic properties of the studied medium significantly improved the clarity of the interpretation of geophysical data when studying the landslide massif. This made it possible to refine the internal structure of the landslide and identify areas with an increased likelihood of deformation. The obtained results provide a reliable basis for assessing slope stability and demonstrate the need for integrated geophysical surveys to reduce the risk of landslides. Along with engineering–geological methods, geophysical surveys will be an integral component of monitoring landslide-prone areas, which will enable timely implementation of organizational measures to protect infrastructure facilities. Full article

Buried pipelines with corrosion defects crossing reverse faults exhibit complex seismic responses under the combined effects of ground motion and fault displacement. Traditional finite-element analysis is computationally inefficient to comprehensively address such problems. This paper proposes a backpropagation neural network (BPNN)-based method for seismic response prediction and fragility assessment. A three-dimensional finite-element model is first employed to analyze the effects of corrosion depth-to-thickness ratio, diameter-to-thickness ratio, internal pressure, and burial depth on the axial compressive strain of the pipeline. Consequently, a BPNN model is constructed with these parameters, along with fault displacement, as inputs with the peak compressive strain as the output. The BPNN model demonstrated excellent predictive performance, with a maximum prediction error below 15%. The incremental dynamic analysis (IDA) method is then applied to map strength and damage indices of the pipeline, enabling quantitative evaluation of its failure probability and functional integrity under various conditions. It is found that higher diameter-to-thickness ratio (D/t) corresponds to a higher likelihood of the pipeline reaching adverse performance levels; this is also accompanied by a reduction in functional integrity. Specifically, as D/t increases from 72 to 144, the probability of pipe wall damage and the risk of transmission function loss rise significantly, highlighting the pronounced fragility of thin-walled pipelines subjected to fault movement. Moreover, corrosion defects exacerbate pipeline fragility: a corrosion depth equivalent to 10% of the wall thickness substantially amplifies strain responses, resulting in an approximately 80% probability of moderate damage, while a corrosion depth of 40% elevates the probability of severe damage beyond 60%. Full article

The transition to low-carbon energy systems is expanding the use of subsurface resources for heat extraction, energy storage, carbon management, and infrastructure reuse. This review examines hydraulic fracturing as a context-dependent engineering intervention across enhanced geothermal systems, geothermal reuse of depleted reservoirs and wells, unconventional gas, underground hydrogen storage, CO₂-based subsurface engineering, and natural hydrogen. We synthesize how stimulation can improve permeability, connectivity, heat exchange, injectivity, and deliverability, while evaluating constraints related to water use, induced seismicity, leakage, well and caprock integrity, life-cycle emissions, and public acceptance. The central trade-off is that higher stimulation efficiency does not necessarily produce greater sustainability. Short-term gains in flow or energy delivery can increase long-term risks to containment, thermal performance, seismic safety, and environmental accountability. Evidence is strongest for enhanced geothermal systems and commercial unconventional gas, whereas field support remains limited for porous-media hydrogen storage, CO₂-based fracturing, and natural hydrogen. Responsible deployment therefore requires site-specific boundaries, real-time monitoring, multi-physics and data-driven modeling, life-cycle assessment, and adaptive governance. Hydraulic fracturing can enable selected sustainable subsurface applications, but its value depends on balancing engineering performance against long-term environmental and integrity constraints. Full article

Coastal lagoon Salt Ponds are highly valuable wetland systems where traditional salt production coexists with ecosystems of significant ecological importance, often characterized by high environmental sensitivity. In data-scarce coastal settings, particularly those located near river channels and drainage networks, assessing multiple environmental hazards remains a major challenge. This study proposes a simplified and transferable methodological framework for multi-hazard assessment in coastal Salt Pond environments (DAFFLE; Data Acquisition Fluvial Flooding and Liquefaction Evaluation), with particular emphasis on areas where field data are limited and fluvial processes and seismic effects may interact. The approach integrates three main components: first, improved terrain modelling using global elevation datasets and ICESat-2 laser altimetry data to better represent very flat coastal areas; second, flood hazard simulation by modelling water depths under different flood scenarios to map potential inundation; third, liquefaction susceptibility is assessed using surficial geological data and key geomorphological parameters, producing simplified probabilistic hazard maps informed by existing seismic hazard datasets or scenario-based assumptions. The proposed framework provides a scalable and practical tool for first-order multi-hazard assessment in vulnerable coastal Salt Pond environments. It supports comparative hazard analyses and decision-making in regions where detailed site-specific data and extensive field investigations are not available, offering a consistent baseline for coastal lagoon Salt Pond risk evaluation and management. Full article

The analysis of earthquake-induced damage scenarios at the urban scale is a fundamental tool for seismic risk assessment and mitigation and the management of urbanized areas exposed to seismic hazards. This paper presents a methodology for simulating earthquake damage scenarios over large urban territories that explicitly accounts for the cumulative effects of seismic sequences. The proposed approach models the progressive accumulation of structural damage and the resulting evolution of building vulnerability under repeated seismic loading. From a complex systems perspective, the methodology describes urban areas as collections of buildings whose vulnerability evolves through memory-dependent processes. Under this framework, the final damage scenario emerges from the cumulative effects of the entire seismic history rather than from the contribution of individual earthquakes considered in isolation. The study extends previous work by the authors, in which instrumentally derived macroseismic intensity maps were integrated with observed building damage data from the 2009 L’Aquila seismic sequence. The results demonstrated that the methodology could successfully reproduce the spatial distribution of observed damage when considering not only the mainshock but also all seismic events exceeding a selected magnitude threshold. In this contribution, new developments of the calibration procedure are presented, together with applications to the 2013 Garfagnana-Lunigiana and the 2016–2017 Central Italy seismic sequences. Through a comparative analysis of these case studies, the influence of different seismic sequence characteristics and building stock features on damage evolution is investigated. The results provide further insight into the capabilities and limitations of the proposed methodology, highlighting its potential as a tool for interpreting post-earthquake damage patterns and supporting seismic risk assessment and mitigation strategies. Full article

The Lombardy Region is characterised by relatively moderate seismic activity, particularly in the Alpine area and its western sector. Significant damage has instead been caused by historical earthquakes with magnitudes greater than M_W 5.0 in the eastern and south-western parts of the region. To enhance preparedness and prevention strategies, the Civil Protection Organisational Unit of Regione Lombardia, together with the Eucentre Foundation and the Civil Protection School of Lombardy (PoliS-Lombardia), organised a regional seismic emergency exercise, the so-called EXE.LOMB.EST 2023, which is an initiative aimed to test and train emergency response capabilities in parts of the region most at risk from seismic events. EXE.LOMB.EST 2023 was a civil protection technical–thematic exercise that involved various groups of participants through tailored training paths. With both educational and practical objectives, the exercise was developed over the course of 2023. In the six months leading up to the final event, approximately 12 training sessions were held to prepare participants according to the identified themes. The final field exercise took place from 9–14 October 2023, during the Italian Civil Protection Week, and included the participation of 15 municipalities. The programme was designed to simulate all key phases of regional emergency management, from activating support functions to assessing damage to cultural heritage. Participants included the Italian Civil Protection Department—as an advisor—the Italian Fire Department; UAS networks, the prefectures and provinces of Brescia, Cremona, and Mantua; municipal officials and certified structural damage assessment experts, with the latter sometimes simply identified as technical personnel and/or technical experts in what follows. The exercise was also a valuable opportunity to test and refine the most advanced emergency management technologies and systems in Italy. The paper discusses notable outcomes, in addition to key steps, and also highlights gaps and issues still open for further/future developments of similar exercises in Italy and abroad. Full article

Earthquake-induced accidents involving buried oil and gas pipelines in water-network regions are governed by coupled seismic, hydrological, geotechnical, and emergency-response factors, while complete accident records are scarce. To support scenario-based consequence analysis under sparse-data conditions, this study develops an accident scenario analysis framework that integrates numerical simulation with Bayesian probabilistic inference. Scenario elements are organized according to four categories: disaster-causing factors, elements at risk, hazard-inducing environment, and emergency management. Finite element analysis and computational fluid dynamics are used to quantify pipeline mechanical response and hydraulic-scour effects, and the resulting physical responses are embedded in a dynamic Bayesian network as state evidence and transition constraints. Triangular fuzzy numbers are used to process expert evaluations and determine node probabilities. The resulting multi-mechanism simulation-Bayesian inference framework quantifies the accident chain from earthquake loading to pipeline deformation, leakage, fire or explosion, and emergency control. Forward reasoning estimates the probability of each scenario state, sensitivity analysis identifies key drivers, including strong earthquakes triggering landslides and rainfall during flood seasons, and disaster-chain analysis clarifies the dominant causative pathways. The framework provides a reproducible basis for scenario analysis, consequence assessment, monitoring and early warning, and emergency response planning for buried oil and gas pipelines exposed to seismic hazards in water-network regions. Full article

Sustainable landslide hazard mitigation requires scenario-based assessment of nature-based solutions under realistic hydromechanical and multi-hazard conditions. This study evaluates the mechanical effect of Vetiver grass (Chrysopogon zizanioides) on slope stability in Loja, southern Ecuador, through an integrated framework combining geotechnical characterization, direct shear testing, finite element modelling, limit equilibrium analysis, and targeted statistical evaluation. Three fine-grained soils, classified as CH, MH, and ML, were analysed under baseline groundwater conditions, groundwater with an 8 kN/m² surcharge, and groundwater with surcharge plus pseudo-static seismic loading. Vetiver reinforcement increased apparent cohesion by 8.92–27.65% and internal friction angle by 6.90–17.43%, with the highest cohesion gain in ML soil. Numerical results showed that stabilization was controlled by soil type, slope geometry, loading condition, and interaction between the 2.0 m root-reinforced layer and the governing failure mechanism. Under surcharge loading, FS for ML at 0.5H:1V increased from 1.056 to 1.450. Under combined loading, FS increased from 0.217 to 1.440 for ML at 1H:1V and from 0.587 to 2.060 for CH at 1H:1V. Targeted ANOVA/MANOVA for MH soil confirmed the influence of geometry and combined loading. Therefore, Vetiver should be considered a complementary, site-specific, and risk-informed mitigation measure rather than a universal stabilization solution. Full article

Landslides are among the most destructive geological hazards, posing significant threats to human life, infrastructure, and ecological environments. In this research, to improve the accuracy and reliability of landslide susceptibility assessment, Guangdong Province was selected as the study area, and a multi-source environmental factor dataset incorporating topographic, geological, hydrological, climatic, vegetation, and anthropogenic factors was constructed. Geological factors, including fault distance and seismic point distance, were introduced to characterize the influence of tectonic activities on slope instability. A landslide inventory and a non-landslide sample dataset were established for model training and validation. The Extreme Gradient Boosting (XGBoost) model was employed for landslide susceptibility mapping, and SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of different conditioning factors. The results showed that the model achieved an area under the receiver operating characteristic curve (AUC) of 0.8335 on the independent test dataset and a mean AUC of 0.8457 ± 0.0219 for a five-fold stratified cross-validation. The high-susceptibility areas were primarily distributed in the mountainous and hilly regions of northern and eastern Guangdong Province. Vegetation-related variables, road proximity, land-cover type, slope, and distance to coal mines were identified as important contributors to landslide occurrence. This study provides useful references for geological hazard prevention, risk management, and sustainable regional planning. Full article

This study offers a new approach to probabilistic earthquake hazard assessment (PEHA) in the densely populated regions of Southern Sumatra and West Java, Indonesia. While much attention is given to powerful, offshore megathrust earthquakes, this research focuses on a different yet equally dangerous threat: shallow, moderate-magnitude earthquakes (4.5 ≤ M_w ≤ 6.5) that occur on land. These events, often caused by unmapped faults, pose a significant risk due to their proximity to major cities and infrastructure. To develop a more reliable model, a best-fit earthquake rate model was estimated using declustered shallow earthquake events as a reference. This model enhances existing methods by offering a more precise depiction of where these shallow, damaging earthquakes are likely to occur. We accomplished this by analyzing a comprehensive probability of exceedance (PoE) of earthquakes with magnitudes up to 6.5 and depths up to 50 km that occurred between 1963 and 2022, mapping and modeling both the known active faults and the historical seismic activity in the region, and using advanced statistical methods to create a highly reliable, integrated seismicity rate model. The final product, the Integrated Most Reliable Spatial Seismicity Rate Model (Model_IMRSSR), is proposed as a useful tool for government authorities and urban planners. It can be used to create detailed seismic hazard maps that highlight areas of highest risk, especially those with unmapped faults. By guiding development away from these high-risk zones and identifying specific locations for physical reinforcement, this research provides a framework for sustainable investment. The proactive use of these findings can lead to more resilient communities and a significant reduction in potential damage and loss of life from future earthquakes. Full article

Reinforced concrete frame structures (RCFSs) subjected to strong seismic excitation may enter a metastable semi-ruin state before global collapse, characterized by severe local damage, degraded stability, and high secondary collapse risk. However, systematic experimental investigations and quantitative identification techniques for this critical transitional state are still lacking in existing seismic engineering literature, forming a notable research gap for post-earthquake safety evaluation. To investigate this critical transition, a Digital Image Correlation (DIC)-assisted shaking table test was conducted on a 1/25-scale RCFS specimen derived from an earthquake-damaged exterior-corridor teaching building, using the Wolong ground motion recorded during the 2008 Wenchuan earthquake as input. DIC was employed to track the full-field evolution of cracking, through-crack development, and concrete cover spalling under incremental seismic loading. Four local damage indices—crack line density (CLD), crack propagation rate (CPR), through-crack ratio (TCR), and concrete spalling ratio (CSR)—were extracted and evaluated with the inter-story drift ratio (IDR) to quantify local-to-global degradation. The results show that visible cracks initiated at PGA = 0.3 g, while accelerated crack propagation occurred at 0.7–0.8 g, with CPR peaks of 1187.5 and 1140 mm/g, respectively. At 0.5–1.0 g, the crack number increased from 13 to 26, total crack length reached 0.443 m, CLD increased to 3.9 × 10⁻⁴, and TCR reached 37.04%. At 1.1–1.5 g, crack development approached saturation, with total crack length of 0.552 m, maximum TCR of 63.6%, and CLD of 4.8 × 10⁻⁴. Under ultimate excitation of 1.6–1.8 g, the crack number stabilized at 33–34, TCR remained around 63%, cumulative spalling area reached 1026 mm², CSR reached 0.015, and the third-floor IDR approached the 1/50 elastoplastic limit. Severe through-cracking, reinforcement exposure, concrete spalling, and residual inclination indicated the onset of the semi-ruin state. The proposed multi-index framework provides quantitative support for semi-ruin-state identification and post-earthquake secondary collapse risk assessment of RCFSs. Full article

The seismic performance of tunnel structures is significantly influenced by the randomness of ground motions. Traditional probabilistic risk assessments, which rely on limited recorded ground-motion data, often suffer from small-sample bias and fail to capture the full distribution of seismic input. To overcome this limitation, this study employs a physically based stochastic ground-motion model to generate a large and statistically representative sample ensemble. A probabilistic seismic risk assessment framework is then developed using the stochastic finite element method, explicitly incorporating ground-motion uncertainty. Four statistical criteria, namely practicality, correlation, efficiency, and proficiency, are systematically applied to evaluate candidate intensity measures (IMs) and identify the optimal one. Among all candidates, PGA exhibits the best overall performance, with the highest regression fitness (R² = 0.873) and the lowest dispersion (β_D = 0.197), followed by PGV (R² = 0.848, β_D = 0.215). Fragility curves for different damage states are subsequently derived. Results indicate that structural responses vary considerably under stochastic ground-motion excitation, and the failure probability follows a typical S-shaped curve as intensity increases. Moreover, the failure probabilities for different damage states exhibit nonlinear growth at higher intensity levels. These findings provide a mathematical basis for probability-based seismic design and risk assessment of tunnel structures. Full article