Search Results (300)

Under the impact of earthquakes, substation systems may experience equipment failures, prolonged functional recovery periods, and expanded power outage consequences. This paper proposes a collaborative optimization framework for enhancing the seismic performance of substation systems and deploying repair resources, aimed at pre-earthquake planning. The system function is characterized by the outage availability of feeders weighted by load and user importance, and the user outage cost is explicitly defined as an economic consequence indicator with monetary units, rather than a dimensionless resilience index. A directed graph model is constructed to simulate the post-earthquake functional recovery process, and under given seismic hazard and vulnerability parameters, Monte Carlo sampling is used to capture the randomness of equipment condition failures and repair durations. Sensitivity analysis is employed to identify critical equipment, and the elitism-preservation and adaptive evolution non-dominated sorting genetic algorithm II (ERA-NSGA-II algorithm), which integrates heuristic initialization, adaptive evolution, and diversity maintenance mechanisms, is proposed to achieve joint optimization of repair teams and equipment reinforcement plans. A typical 220 kV substation case study demonstrates that the framework is feasible under the analyzed scenarios and exhibits better empirical search performance compared to the selected benchmark algorithm. Due to the limitations of a single topology and certain fixed input parameters, the obtained results are scenario-dependent and cannot be used to infer general applicability or global convergence. Full article

Background/Objectives: Pandemics and natural disasters may delay the diagnosis and treatment of developmental dysplasia of the hip (DDH). In this study, we evaluated the effect of 2023 Kahramanmaraş earthquake-induced healthcare disruption on the DDH screening program. Methods: Medical records of infants referred for DDH screening between February 2022 and February 2025 were retrospectively reviewed. Three consecutive twelve-month intervals: the pre-earthquake year (February 2022 to February 2023), the earthquake year (February 2023 to February 2024), and the post-earthquake year (February 2024 to February 2025) were evaluated to detect early and late effects of the earthquake on the DDH screening. Results: A thousand infants in the pre-earthquake year, 404 in the earthquake year, and 824 in the post-earthquake year were evaluated. A 60% reduction in referrals was detected during the earthquake year. DDH incidence in late presentations during pre-earthquake, earthquake, and post-earthquake years was 18.6%, 45.8%, and 22.2%, respectively. Pairwise analysis revealed a statistically significant increase in the post-earthquake year compared with the pre-earthquake period (Yates-corrected chi-square = 4.119, p = 0.042). No significant difference was found between the pre-earthquake year and the earthquake year (Yates chi-square = 1.362, p = 0.243), nor between the post-earthquake years (Yates chi-square = 0.084, p = 0.772). Conclusions: The earthquake caused significant disruption in healthcare and DDH screening. Health authorities planning post-disaster service recovery should proactively augment DDH screening capacity for infants who missed screening during the acute phase. Full article

Indonesia is one of the world’s most seismically active countries, experiencing frequent earthquakes that make assessing regional resilience essential for disaster risk reduction. This study dynamically evaluates socioeconomic resilience in 28 regencies/municipalities affected by destructive earthquakes between 2016 and 2022. Resilience was quantified using the Adjusted Mazziotta–Pareto Index (AMPI) at three periods: pre-event (T₀), during the event (T₁), and post-event (T₂). Index changes were interpreted as resistance (Δ₁ = T₁ − T₀), recovery (Δ₂ = T₂ − T₁), and adaptive capacity (Δ₃ = T₂ − T₀). Results show substantial regional differences in resilience trajectories: some areas experienced only minor declines during the earthquake, while others were heavily affected but recovered quickly. Cluster analysis revealed distinct typologies, including consistently high-resilience regions, rapid-recovery regions, and persistently vulnerable regions. These disparities are associated with variation in economic capacity, social vulnerability, labor market conditions, and access to health services. Overall, the findings highlight the value of a multidimensional, time-sensitive approach to measuring socioeconomic resilience. The study advances an AMPI-based temporal measurement framework and offers policy insights for development planning and disaster mitigation. Full article

Post-disaster recovery is increasingly understood not as a simple return to pre-event conditions but as a dynamic reorganization of land systems, in which land cover and land use change (LCLUC) provides an operational signature of recovery trajectories. However, most existing assessments reduce recovery to a single dimension—typically vegetation greenness—which can conflate systems that differ fundamentally in their response behavior. This study develops a multi-index Recovery Resilience Index for Land Systems (RRI-LS) within Google Earth Engine and applies it to the catastrophic liquefaction zone of the 2018 Mw 7.5 Palu earthquake (Central Sulawesi, Indonesia). Combining Sentinel-2 spectral indices (NDVI, NDBI, BSI), Dynamic World land-cover labels, and a hybrid Top-of-Atmosphere/Surface-Reflectance baseline to overcome the sparse pre-event archive, we quantify three resilience dimensions—resistance, recovery, and stability—and classify recovery into qualitatively distinct pathways. The hybrid baseline is quantitatively validated: after removing a small systematic offset, the residual discrepancy between TOA- and SR-derived indices is 2.4–4.9 times smaller than the measured disturbance signal. Site-level analysis of the three principal liquefaction hotspots (Balaroa, Petobo, Jono-Oge) and a 1 km grid expansion (n = 962 cells) reveal that disturbance-affected areas did not converge on a single outcome but diverged into bounce-back, transformational, reconstructed (non-vegetated), and degraded pathways; a basin-wide re-run at 250 m (n = 14,157 cells) reproduced the same pathway hierarchy, confirming robustness to grid resolution. Initial disturbance intensity was a poor predictor of long-term recovery (R² = 0.07, p < 0.001), underscoring that recovery is multidimensional and not reducible to a single shock variable. The emergence of a reconstructed, non-vegetated pathway—where bare-soil disturbance is resolved through built surfaces rather than re-greening—demonstrates that vegetation metrics alone are insufficient in human-dominated landscapes. The framework supports a land-system perspective in which recovery is conceptualized as the establishment of new functional equilibria. Full article

Post-earthquake structural inspections are critical for public safety and recovery, yet traditional manual assessments are slow, hazardous, and resource-intensive. This paper proposes a novel system that integrates a quadrupedal robot with a deep learning (DL) vision model to rapidly detect structural cracks and damage in the aftermath of earthquakes. A Unitree Go2 quadruped robot, equipped with cameras and sensors, is paired with a YOLOv8 instance segmentation network for near-real-time crack detection and localization. The approach addresses key limitations of manual post-disaster inspections by enabling operator-supervised, near-real-time visual crack screening in hazardous or hard-to-reach areas. The YOLOv8 model is trained on a curated dataset of crack and damage images to support crack detection and segmentation performance, and its advanced segmentation capabilities allow precise delineation of damaged regions. The integrated system is validated on a laboratory-scale concrete–steel frame with simulated damage. Preliminary results demonstrate that the Unitree Go2 quadruped robot can navigate and inspect structural elements while the AI model identifies and segments cracks and surface damage under near-real-time laboratory operating conditions. This work highlights the potential of combining advanced legged robotics and state-of-the-art DL for structural health monitoring (SHM), offering a preliminary visual screening tool that can support operator awareness and help prioritize areas requiring expert structural inspection. Full article

Post-disaster recovery is a multi-year, multi-dimensional process, yet most remote-sensing assessments rely on single indicators and are hard to apply in data-sparse regions—limiting their value for sustainable, evidence-based reconstruction. We develop a Google Earth Engine (GEE)-based multi-sensor Recovery-Activity Index (RAI), built entirely from free satellite data, and apply it to the 2023 Al Haouz earthquake (Mw 6.8) in the High Atlas, Morocco. The index is framed explicitly as an observed recovery-activity monitoring proxy, not a direct measure of welfare or resilience capacity. Monthly VIIRS nighttime-light (NTL) anomalies, Dynamic World built-up probability, and precipitation-corrected Sentinel-2 NDVI were extracted for a 30 km rural core zone (January 2022–May 2026), deseasonalized, standardized, and integrated. NTL anomalies rose after the earthquake (post-event mean +18%) and appeared to precede built-up anomalies by about two months; because monthly series are short and autocorrelated, we tested this lead with block-bootstrap and block-permutation methods and report it as a reproducible but modest early-activity lead (r = 0.65, p = 0.02; p = 0.14 after correction) that is not an artefact of optical data gaps. NDVI was governed mainly by precipitation (R² = 0.61) with negligible earthquake-attributable change, so vegetation signals do not confound the index. The integrated RAI peaked in December 2024 and proved robust to indicator weighting (pairwise r ≥ 0.97), baseline choice (r = 0.88), and spatial domain (<9% variation), with a genuinely multi-sensor peak (NTL 62%, built-up 43%). Province-level analysis revealed an uneven recovery hierarchy (Chichaoua > Al Haouz > Taroudannt) driven by differences in physical-rebuilding signal rather than baseline luminosity. Running in minutes server-side at no cost, the RAI offers data- and resource-limited administrations a scalable, reproducible tool to flag where reconstruction activity lags and to prioritize targeted ground verification—supporting more equitable, sustainability-oriented recovery governance—rather than serving as a stand-alone, validated recovery measure. Full article

The quest for seismic resiliency requires designing for performance objectives beyond life safety. Functional recovery is an emerging objective often defined as the time required to restore a building’s basic functionality to the pre-event level. Nevertheless, quantifying functional recovery is a complex, computationally intensive process that is challenging to integrate into a standard design workflow. This study develops a machine learning (ML) model to map design and geometric features of steel special moment-resisting frames (SMRFs) to their functional recovery under two hazard levels: design-basis (DBE) and maximum considered (MCE) earthquakes. First, functional recovery time was quantified for an inventory of 100 steel SMRFs with varying heights by integrating FEMA P-58 loss-based methodology with the ATC-138 framework. The building information and calculated recovery times were then used in a standard ML pipeline including feature selection, hyperparameter tuning, cross-validation, model evaluation, and model explainability. The results suggest that the ML model can accurately estimate functional recovery using design and geometric features, achieving R² values of 89% and 93% on the test set for DBE and MCE levels, respectively. In addition, for the studied regular SMRF buildings, the results indicate that building weight and the average strong-column weak-beam ratio are influential design parameters that govern functional recovery time, suggesting that a recovery-oriented design of steel SMRFs may benefit from minimizing building weight and avoiding overt column upsizing. Full article

Hatay experienced severe spatial, economic, and social losses following the earthquakes on 6 and 20 February 2023. Beyond the scale of physical destruction, the post-disaster period has brought deep transformations in everyday life, access to services, and the governance of space. This study examines the reconstruction process in Hatay from a perspective of just recovery and evaluates how the discourses of justice highlighted in policy documents are reflected in planning practice. Furthermore, the study offers empirical contributions on how justice is produced through spatial planning tools such as reserve area decisions, rubble management, expropriations, and access to services. Within the scope of the research, post-disaster policy documents, municipal reports, and media content were examined using qualitative content analysis, and the findings were supported by field-based spatial observations. The analyses show that, although the discourse of participation is frequently emphasized, it remains limited in decision-making processes; and issues related to the needs of vulnerable groups and equal access to services are more weakly represented. Spatial examples highlight the gap between normative discourses and practice through reserve area decisions, debris dumping management, and environmental risks. Overall, the study reveals that the principles of just recovery have been only partially implemented in the reconstruction process in Hatay, and that, for long-term resilience, participation, spatial equality, and the recognition of diverse lifestyles need to be strengthened at the institutional level. Full article

To determine the factors influencing juvenile recruitment of the kelp Eisenia bicyclis, a seven-year monitoring survey was conducted in an area affected by seismic subsidence caused by the 2011 earthquake and subsequent breakwater restoration. Juvenile recruitment was high in September 2011 and in May to June of 2013–2015, but low in 2012, 2016 and 2017. Analysis of the relationship between environmental factors and juvenile recruitment revealed that recruitment was associated with light intensity, with lower water temperature two months prior, and an increase in nutrients four months prior. The seasonal increase in nutrient concentrations during winter may have been influenced by the seasonal northwestward coastal current. In contrast, despite elevated nutrient concentrations, recruitment was relatively poor in 2016–2017. This might be attributed to the unstable seabed environment associated with the breakwater construction. Our monthly monitoring of both the number of E. bicyclis juvenile recruitments and environmental factors at the same site demonstrated that a time-lagged increase in nutrient concentrations and a decrease in water temperature are associated with Eisenia bicyclis juvenile recruitment. This study provides fundamental information on kelp recruitment that will contribute to predicting the recovery of kelp communities following disturbances and recruitment dynamics under environmental change. Full article

Seismic codes in high-risk earthquake zones magnify the embodied environmental impact of buildings by increasing structural mass. While the existing literature evaluates this burden holistically, this study isolates the environmental penalty of seismic design at the component level using building information modeling (BIM). Within this scope, an eight-story reinforced concrete residential building was modeled at LOD 300 and comparatively analyzed under TBDY-2018 (seismic) and a strictly theoretical TS-500 (gravity-only) baseline scenario. This gravity-only model acts solely as a mathematical isolation tool rather than a buildable design option. Using the CML 2001 methodology and Türkiye-specific environmental product declarations (EPDs), calculations covered the production (A1–A3), end-of-life (C1–C4), and recovery (Module D) stages of the building. Findings reveal that seismic mass increases create a nonlinear, asymmetric effect on environmental indicators. Increased concrete volume dictates the global warming potential (GWP), whereas steel reinforcement—driven by ductility demands—elevates the photochemical ozone creation potential (POCP) and acidification potential (AP) much more aggressively than concrete. Conversely, while seismic reinforcement provides a negative emission credit during the recovery stage (Module D), quantitative analysis reveals that this circular benefit is marginally small (offsetting approximately 2% of the steel-related GWP), proving mathematically insufficient to neutralize the massive upfront ecological debt. Consequently, the additional environmental penalty necessitated by seismic safety must be managed through early-stage BIM optimization and alternative mitigation strategies, such as seismic isolation. Full article

Earthquake-induced infrastructure disruption can delay emergency response and prolong recovery, yet many post-earthquake damage studies either focus primarily on superstructures or examine individual infrastructure sectors separately. This study presents a questionnaire-based expert assessment of earthquake-induced damage factors and mitigation measures in urban infrastructure systems. Fourteen damage factors and seventeen mitigation measures were identified through a structured literature review and evaluated by 424 technical experts using a five-point Likert scale. The responses were analyzed using reliability analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), the Relative Importance Index (IRI), and Pearson correlation analysis. The dataset showed high internal consistency (Cronbach’s alpha = 0.926), with KMO = 0.941 and a significant Bartlett’s test (p < 0.001), confirming its suitability for factor analysis. EFA and CFA grouped the damage factors into three dimensions: Post-Earthquake Intervention Challenge (PEIC), Health Food Water Security (HFWS), and After-Earthquake Preparedness (AEP). IRI results ranked PEIC as the highest-priority expert-perceived factor group (average IRI = 90.61%), followed by HFWS (88.32%) and AEP (85.10%). Pearson correlations indicated that resilient network and pipeline infrastructure, resource diversification and redundant distribution capacities, regular maintenance and inspection, strategic stockpiles, site-selection reassessment, slope stabilization, and early warning systems were strongly associated with one or more factor groups (r > 0.60; p < 0.001). The findings should be interpreted as expert-perceived priorities rather than objective damage probabilities; nevertheless, they provide a structured basis for preliminary prioritization of urban infrastructure resilience measures in earthquake-prone contexts. Full article

Residential satisfaction serves as a critical metric for lived resilience, reflecting the sustained functionality of sociospatial systems. However, its long-term evolution in post-disaster, rapidly urbanizing landscapes remains under-researched. This study evaluates sociospatial dynamics in İzmit, Turkey, nearly three decades after the 1999 İzmit Earthquake, analyzing how urbanization trajectories shape resilience outcomes. Grounded in a bottom-up spillover model, the research utilizes a Structural Equation Modeling (SEM) framework to analyze resident survey data, complemented by a spatial “sections-based” analysis to capture intraurban variability across distinct development processes. Social capital emerged as the strongest predictor of residential satisfaction, potentially acting as a compensatory mechanism in deprived neighborhoods, despite physical deficiencies. Findings revealed profound sociospatial heterogeneity in long-term urban recovery. Paradoxically, contemporary mass housing exhibited lower satisfaction scores than older informal developments, challenging the assumption that formal planning and modern construction inherently guarantee sustained resilience. By utilizing residential satisfaction to interpret uneven lived resilience across urbanization trajectories, this study advocates for prioritizing the most influential quality domains through targeted interventions. These insights move beyond technical recovery metrics to offer a transferable framework for disaster-prone cities seeking to align institutional planning goals with the actualized residential satisfaction of communities. Full article

Post-war reconstruction in Aleppo requires more than replacing damaged buildings; it demands an understanding of the city’s historically layered urban fabrics, their differing socio-spatial logics, and their unequal capacities for recovery. Following severe conflict-related destruction during the Syrian civil war, particularly between 2012 and 2016, and the additional impact of the February 2023 earthquake, Aleppo’s recovery is further complicated by the heritage significance of its Ancient City, inscribed on the UNESCO World Heritage List in 1986 and included on the List of World Heritage in Danger since 2013. This study examines how urban morphology can guide reconstruction through a comparative analysis of four neighborhoods representing major phases of Aleppo’s development: Jdaideh, Azizieh, Mohafaza, and Jabal Badro. Using a qualitative historical–morphological approach, the research analyzes figure–ground relations, street-network structure, degrees of transition between public, semi-public, semi-private, and private spaces, and landmark–node systems to identify the spatial characteristics, temporal persistence, and planning meaning of each district. The findings show that Aleppo is not a homogeneous urban system but a city composed of distinct fabrics with different strengths, vulnerabilities, and reconstruction needs. The comparison further demonstrates that density alone is not an adequate indicator of urban quality or resilience. The study concludes that reconstruction should be based on fabric-specific strategies, including preservation-sensitive rehabilitation, reinforcement of public nodes, balanced connectivity, governance-aware phasing, and incremental upgrading. Urban morphology is therefore proposed as a practical, but not exhaustive, framework for context-sensitive recovery in conflict-affected and historically layered cities. Full article

Background and rationale: A review of the current disaster-recovery literature highlights the lack of standard frameworks for comparing recovery experiences. Indicator-based evaluation tools are often context-specific, and the generalizability of lessons learned from case studies is limited. This research offers a diagnostic framework that can be used both as a tool for analyzing and strengthening specific instances of disaster recovery and for comparing recoveries across contexts. Methodology: The literature search was conducted to identify important elements of recovery. Results: This article presents the Recovery Typology Framework (RTF)—a tool for analyzing and characterizing recovery efforts, identifying recovery strengths and weaknesses, and comparing disaster recovery across settings and scales. Useful to both scholars and practitioners, the RTF is divided into process, outcome, and assessment aspects of disaster recovery. Recovery processes can be efficient or participatory. Recovery outcomes can be aimed at stabilization, restoration, or improvement. Both objective and subjective assessment methods can be used to evaluate recovery processes and outcomes. Types of evaluation vary from formative to summative throughout the recovery process. This article applies the RTF to Israel’s national long-term earthquake recovery plans, demonstrating how this tool can be used to characterize, critique, and improve recovery planning. Contribution and usefulness: The study contributes to disaster-recovery scholarship by offering a conceptual–analytical framework that integrates governance processes, recovery outcomes, and assessment mechanisms into a single comparative structure. Rather than proposing a prescriptive or empirically validated model, the RTF is designed as a diagnostic and interpretive tool that can be applied across diverse contexts to reveal trade-offs and guide more reflexive recovery planning. The framework makes it possible to identify the unique blend of elements in specific recovery experiences, outlines the trade-offs implicit in recovery decision-making, and facilitates comparison of recovery experiences across contexts. Contribution to UN SDGs: The RTF offers a tool for identifying areas of recovery that contribute to and threaten the long-term sustainability of recovery efforts. Full article