Search Results (100)

This paper analyses the role of landscape as a fundamental dimension of post-earthquake recovery in the inland areas of Central Italy, arguing that reconstruction must be understood not only as the repair of damaged buildings but as a broader territorial process affecting identity, spatial organization, and long-term settlement trajectories. In this sense, post-earthquake recovery is also interpreted as a strategic opportunity to reinforce coast–inland relationships, acknowledging the structural interdependence between inland Apennine areas and coastal urban systems. Drawing on insights from applied research conducted in the L’Aquila 2009 crater and on the conceptual framework developed within the PRIN TRIALS project, the paper discusses how seismic events accelerate pre-existing territorial dynamics and produce enduring transformations, particularly in the proximity landscapes surrounding historic centres. Rather than presenting empirical findings, the contribution offers a theoretical and operational framework aimed at integrating landscape considerations into reconstruction processes. It outlines key concepts such as landscape quality, transformative resilience, and permanent temporariness; reviews critical normative aspects linked to emergency procedures; and proposes a set of landscape-oriented guidelines and criteria for the contextual integration of reconstruction projects. These include landscape quality objectives, multiscalar readings of identity values, and operational tools such as visual-impact assessment, Project Reference Context analysis, and principles for managing transformations in peri-urban and historic environments. Overall, the paper argues that adopting a landscape-based perspective can strengthen territorial cohesion, support the sustainable redevelopment of historic centres and their surroundings, and embed post-earthquake reconstruction within broader coast–inland territorial strategies aimed at long-term resilience and balanced regional development in Apennine communities. Full article

This article examines the failure of the two most recent temporary housing programs implemented in Spain following two major disasters: the 2011 Lorca earthquake and the 2021 La Palma volcanic eruption. Despite differing hazard typologies, both cases resulted in incomplete and ultimately unsuccessful housing programs, with only 13 of the 60 planned units built in Lorca and 121 of the 200 planned units delivered in La Palma. Using a qualitative comparative case study approach, the research analyzes governance decisions, housing design, and implementation processes to assess their impact on the sustainability of post-disaster temporary housing. The analysis adopts the five dimensions of sustainability—environmental, economic, social, cultural, and institutional—as an integrated analytical framework for evaluating public management performance in post-disaster temporary housing. The findings show that early decision-making, shaped by political urgency, technical misjudgments, and the absence of adaptive governance, led to severe delays, cost overruns, inadequate and energy-inefficient construction, and the formation of marginalized settlements. This study concludes that the lack of regulatory frameworks, legal instruments, and operational protocols for temporary housing in Spain was a determining factor in both failures, generating vulnerability, prolonging recovery processes, and undermining sustainability across all five dimensions. By drawing lessons from these cases, this article contributes to debates on resilient and sustainable post-disaster recovery and highlights the urgent need for integrated regulatory frameworks for temporary housing in Spain. Full article

Post-earthquake fires often cause more severe losses than the earthquakes themselves, highlighting the critical role of water distribution networks (WDNs) in mitigating fire risks. This study proposed an improved assessment framework for the post-earthquake firefighting functionality of WDNs. This framework integrates a WDN firefighting simulation model into a cloud model-based assessment method. By combining seismic damage and firefighting scenarios, the simulation model derives sample values of the functional indexes through Monte Carlo simulations. These indexes integrate the spatiotemporal characteristics of the firefighting flow and pressure deficiencies to assess a WDN’s capability to control fire and address fire hazards across three dimensions: average, severe, and prolonged severe deficiencies. The cloud model-based assessment method integrates the sample values of functional indexes with expert opinions, enabling qualitative and quantitative assessments under stochastic–fuzzy conditions. An illustrative study validated the efficacy of this method. The flow- and pressure-based indexes elucidated functionality degradation owing to excessive firefighting flow and the diminished supply capacity of a WDN, respectively. The spatiotemporal characteristics of severe flow and pressure deficiencies demonstrated the capability of firefighting resources to manage concurrent fires while ensuring a sustained water supply to fire sites. This method addressed the limitations of traditional quantitative and qualitative assessment approaches, resulting in more reliable outcomes. Full article

Post-disaster reconstruction remains largely excluded from circular-economy approaches. This gap is particularly evident in earthquake-affected inner territories, where reconstruction is constrained by severe logistical challenges—especially in relation to rubble management—and where debris is often composed of materials closely tied to local building cultures and community identities. In these contexts, rebuilding still predominantly follows linear, emergency-driven models that treat rubble primarily as waste. This study introduces Rubble as a Material Bank (RMB), a digital–material framework that reconceptualises earthquake rubble as a traceable and programmable resource for circular reconstruction. RMB defines a rubble-to-component chain that integrates material characterisation, data-driven management, robotic fabrication, and reversible architectural design. Selected downstream segments of this chain are experimentally validated through the TRAP project, developed within the European TARGET-X programme. The experimentation focuses on extrusion-based fabrication of dry-assembled wall components using rubble-derived aggregates. The results indicate that digitally governed workflows can enable material reuse, while also revealing technical and regulatory constraints that currently limit large-scale implementation. Full article

Effective disaster management depends on rapidly understanding earthquake damage, yet traditional methods struggle to operate at scale and rely on expert inspections that become difficult when access is limited or time is critical. Satellite-based damage detection also faces limitations, particularly under adverse weather conditions and delays associated with satellite overpass schedules. This study introduces a machine learning-based approach to assess post-earthquake building damage using real observations collected after the event. The aim is to develop fast and reliable estimation techniques that can be deployed immediately after the mainshock by integrating structural, seismic, and geographic data. Three machine learning models—Random Forest, Histogram Gradient Boosting, and Bagging Classifier—are evaluated across both reinforced concrete and masonry buildings and across multiple spatial levels, including building, district, and city scales. Damage is categorized using practical three-class (traffic light) and detailed four-class systems. The models generally perform better in simpler classifications, with the Bagging Classifier offering the most consistent results across different scales. Although detecting severely damaged buildings remains challenging in some cases, the three-class system proves especially effective for supporting rapid decision-making during emergency response. Overall, this study demonstrates how machine learning can provide faster, scalable, and practical earthquake damage assessments that benefit emergency teams and urban planners. Full article

In the initial phase of large-scale disasters, delayed external relief supplies make scientific local emergency supply allocation crucial—not only for reducing casualties, but also for advancing sustainable disaster response, a key link in enhancing post-disaster resilience. Existing research mostly focuses on cross-regional material allocation while overlooking local challenges like low resource efficiency and unbalanced supply–demand dynamics. To tackle these limitations in the existing research, this study develops a multi-objective collaborative local emergency supply allocation model centered on sustainability. It uses an improved TOPSIS method to quantify the urgency of needs in disaster-stricken areas, prioritizing material distribution to vulnerable regions in line with the principle of “no vulnerable area left neglected in relief efforts”. The study also integrates the entropy weight method and analytic hierarchy process (AHP) to ensure rational indicator weighting, and designs a double-layer encoded genetic algorithm to obtain optimal allocation schemes that balance efficiency, fairness, and sustainability. Validated using the 2013 Ya’an Earthquake case study, the model outperforms traditional local allocation approaches: it boosts resource utilization efficiency by reducing material shortage rates, accelerates post-disaster recovery by shortening response times, and improves allocation fairness. Findings provide empirical support for the establishment of “local–external” collaborative rescue systems and sustainable disaster risk reduction frameworks. Empirical calculations using case-specific data and real-world estimates verify the model’s practical applicability: it meets the requirements for fair and rapid allocation needs, aligns with the goals of sustainable disaster management, and lowers the carbon footprint of relief operations by lessening reliance on long-distance external materials. Full article

How does digital technology mediate decision-making and shape our understanding of disaster recovery? I address this question by examining both the administrative and cinematic uses of digital images in the reconstruction process following the 2011 Great East Japan Earthquake. Post-disaster digital mediation is characterized by the administrative use of what has been termed “operational images,” designed not for interpretation but for action, particularly in disaster response and prevention. I connect the social and ethical dimensions of post-disaster recovery with the ontological dimensions of the technological characteristics of digital photography. By comparing Japanese independent filmmaker Haruka Komori’s digital filmmaking practice with the operational images utilized by administrative and research bodies, I aim to demonstrate how her particular digital aesthetics elicit the latent capacity of the “digital unconscious” and offer new modes of perceiving post-disaster recovery, in contrast to both other forms of post-disaster digital mediation and to analog photography. Through close analyses, I argue that her work articulates an alternative vision of recovery—one rooted not in spatial management or predictive planning, but in physical attachment to place, trust in the future, and imaginative engagement with survivors and the dead. Full article

Building a sustainable and resilient humanitarian logistics system is essential for reducing disaster losses and supporting long-term socio-economic recovery. Following a major disaster, rapidly organizing casualty evacuation while maintaining system robustness is a fundamental component of sustainable emergency management. This study develops a two-stage robust optimization model for designing a sustainable humanitarian logistics network that simultaneously accounts for two critical post-disaster uncertainties: (i) interruption risks at temporary medical points and (ii) uncertain casualty demand. By explicitly differentiating deprivation costs between mild and serious injuries, the model quantifies human suffering in monetary terms, thereby integrating social and economic sustainability considerations into the optimization framework. A customized column-and-constraint generation (C&CG) algorithm with proven finite convergence is proposed to ensure tractability and practical applicability. Using the 2008 Wenchuan earthquake as a real-world case study, involving 10 affected areas and 10 candidate temporary medical points, the results demonstrate that the proposed approach yields evacuation plans that remain feasible under all tested worst-case realizations, substantially reducing deprivation costs compared with existing benchmarks. The findings highlight that strategically increasing the capacity of key temporary medical nodes enhances the sustainability and resilience of the emergency medical system, offering evidence-based insights for designing sustainable and robust disaster-response strategies. Full article

Effective post-disaster management requires continuous and reliable monitoring of the evolving disaster situation. While remote sensing provides objective measurements of ground deformation, social media data offer dynamic insights into public perception and disaster progression. However, integrating these complementary data sources to achieve sustained monitoring of disaster remains a challenge. To address this, we propose a novel framework that combines Sentinel-1 SAR data with Sina Weibo posts to improve dynamic earthquake impact assessment. Physical damage was quantified using D-InSAR-derived deformation. Disaster-related locations were identified using a fine-tuned pre-trained language model, and public sentiment was inferred through prompt-based few-shot learning with a large language model. Spatiotemporal analysis was performed to examine the relationship between sentiment dynamics and varying levels of physical damage, followed by an analysis of topic transitions within regional semantic networks to compare discussion patterns across areas. A case study of the 2023 Jishishan earthquake demonstrates the framework’s capability to continuously track disaster evolution: regions experiencing severe physical damage exhibit clear concentrations of negative sentiment, whereas increases in positive sentiment coincide with areas where rescue operations are effectively underway. These findings indicate that integrating the two data sources improves continuous disaster monitoring and situational awareness, thereby supporting emergency response. Full article

Social media platforms have emerged as a critical infrastructure for disaster communication and emergency management. However, how public opinion varies across platforms during earthquake events and how such differences can inform resilient disaster strategies remain underexplored. This study analyzes public opinion responses to the 2023 M5.5 Dezhou earthquake across three major Chinese social media platforms—Sina Weibo, Bilibili, and Douyin—based on 28,557 posts. By combining Latent Dirichlet Allocation (LDA), Word2Vec, and Convolutional Neural Networks (CNNs), we examine the temporal, spatial, thematic, and emotional patterns of public discourse. The results show (1) a bimodal public attention pattern within 24 h of the earthquake, with platform-specific response timings; (2) spatial clustering of public concern in the epicenter (Shandong) and historically high-risk regions (Sichuan–Chongqing); (3) differentiated topic preferences reflecting platform functions—emotional expression (Weibo), science popularization (Bilibili), and real-time impact sharing (Douyin); and (4) a predominance of positive/neutral sentiment, influenced by user demographics and algorithmic content curation. This study proposes a resilience-oriented public opinion analysis framework aligned with the disaster lifecycle and offers recommendations for platform-specific risk communication, psychological support, and policy planning. Findings contribute to digital disaster governance and the integration of social media analytics into sustainable emergency management. Full article

Natural disasters have various adverse effects on human lives, making it challenging for authorities to manage post-disaster situations with limited resources. Due to the extreme extent of the damage, the huge amount of resources needed to restore life to normality makes such a situation challenging. For this purpose, different methodologies have been proposed to effectively handle these types of situations. All these methodologies consider different aspects of the post-earthquake context, taking into account core parameters such as the time and cost required for reconstruction, as well as the people directly affected by the earthquake. In this paper, we conduct a Systematic Literature Review (SLR) of various state-of-the-art techniques proposed for different phases of post-earthquake situations, specifically for reconstruction planning with sustainability considerations. All these proposed solutions are differentiated on the basis of input data, parameters, and type of solutions (data sciences, civil engineering, socio-economics, and modelling). The time range chosen to filter out relevant studies is between 2000 and 2025. Eventually, we reviewed 55 related articles out of 47,539 analysed from seven different digital libraries. The findings of this SLR reveal that optimization and simulation-based approaches dominate the current research landscape, with a growing trend toward data-driven and AI-assisted reconstruction planning. However, only a few studies focus on integrating socio-economic, environmental, and physical infrastructure aspects, which represents a major research gap. These findings provide insights that can guide future researchers in designing more comprehensive frameworks to improve post-earthquake reconstruction in a sustainable manner by prioritising economic, social, and environmental infrastructures, as well as facilities for affected individuals, thereby utilising available resources more effectively. Full article

The present article focuses on the assessment of the potential advantages and disadvantages of the utilisation of modern building inventory technologies in crisis situations, using a case study of Ukraine, currently engulfed in armed conflict. The following methods are described in detail: laser scanning, 360-degree camera images, and photo series. The authors conducted an in-depth SWOT/TOWS analysis, adapted to the specifics of the post-conflict environment, with a view to the future reconstruction of damaged buildings. The originality of the study lies in the use of a modified, quantitative version of the conventional SWOT analysis, supplemented with a weighting and rating system, which allowed for a more accurate assessment of the effectiveness of various technologies, including laser scanning. While the study focuses on the Ukrainian context, the authors emphasise that the developed methodology is universal and can be successfully applied to other critical areas, such as regions affected by earthquakes, floods, fires, or technological disasters. A modified SWOT/TOWS analysis can serve as a valuable tool in crisis management and infrastructure reconstruction during emergencies, providing the data necessary for making rational and effective decisions regarding the use of modern technologies in construction. The analysis revealed that, of the analysed inventory strategies, only laser scanning technology fits the so-called “maxi-maxi” strategy, a scenario in which both internal resources and external capabilities are maximised. The remaining two strategies were designated as “maxi-mini,” signifying that their implementation is associated with elevated levels of risk despite their inherent advantages. It is imperative to acknowledge the existence of substantial external threats that persist. Nevertheless, this does not constitute a complete rejection of the concept. This study examines armed conflict as a research context for a selection of buildings in Ukraine. The analysis was constrained to the three most prevalent methods: The use of TLS, SfM, and 360-degree cameras is also a key component of the methodology. Full article

Disaster management requires the development of effective technologies for managing both pre-and post-disaster processes. Therefore, utilizing effective tools and techniques to mitigate the disaster risks or lower the adversarial impacts is essential. Over the last decade, digital twin (DT) applications have found a wider implementation area for varying purposes, but most importantly, they are utilized for simulating disaster impacts. This study aims to develop an open-source digital twin (DT) framework for earthquake disaster management in the Cayirova district of Kocaeli, Türkiye, one of the country’s most seismically active regions. The primary objective is to enhance local resilience by integrating multi-source data into a unified digital environment that supports risk assessment, response planning, and recovery coordination. The digital model developed using QGIS (3.40.9 Bratislava), Autodesk InfraWorks 2025 software for DT modeling integrates various data types, including geospatial, environmental, transportation, utility, and demographic data. As a result, the developed model is expected to be used as a digital database for disaster management, storing both geospatial and building data in a unified structure. The developed model also aims to contribute to sustainable practices in cities, where disaster risks are particularly critical. In this respect, the developed model is expected to create sustainable logistics chains and sustainable targets aiming to reduce the number of people affected by disasters, reducing the direct economic losses caused by disasters. In this framework, the developed model is expected to further assess seismic risk and mitigate risks with DTs. These capabilities enable the project to establish an open-source district-level DT system implemented for the first time in Cayirova, provide an alternative disaster model focused on region-specific earthquakes, and integrate 2D/3D assets into an operational, ready-to-respond digital database. In terms of practical importance, the model provides a digital database (digital backup) that can be used in emergencies, helping decision-makers make faster, data-driven decisions. The significance of this study lies in bridging the gap between urban digitalization and disaster resilience by providing a scalable and transparent tool for local governments. Ultimately, the developed DT contributes to sustainable urban management, enhancing preparedness, adaptive capacity, and post-disaster recovery efficiency. Full article

Earthquakes, fires, and climate change-related hazards increasingly threaten cultural heritage. Documenting and identifying the significance of heritage sites before disasters is essential for archival purposes and for guiding post-disaster interventions such as consolidation, reconstruction, or redesign. Although various post-disaster strategies exist in the literature, they often lack consideration of pre-disaster values and authentic qualities, limiting their effectiveness in value-based regeneration. This study proposes a framework for managing post-disaster interventions grounded in pre-disaster documentation of heritage values, authenticity, and integrity. The methodology includes seven phases: case selection; site survey and documentation; thematic analysis and mapping; quantification of qualitative data; synthesis of pre-disaster analysis results to define values, problems, and potentials; post-disaster assessment using aerial and terrestrial imagery; and development of targeted intervention strategies. This study focuses on two areas in Antakya, Türkiye: Kurtuluş Street and Kuyulu Neighborhood, affected by the 2023 earthquake (M 7.7). These areas represent different historical layers: a Hellenistic grid plan with French-style buildings, and an organic Ottoman settlement morphology, respectively. Conservation data collected in 2019 inform the analysis. Mapping techniques evaluate attributes such as spatial characteristics, typologies, and structural systems. The study concludes that traces of pre-disaster spatial patterns and building features should inform post-disaster designs, ensuring sustainable, earthquake-resistant, and value-based interventions. Full article

Rural landscapes are changing rapidly, yet many assessments remain descriptive and weakly connected to planning instruments. This study connects rural landscape analysis with planning and management by examining post-earthquake transformations in Topaktaş (Tuşba, Van), a village redesigned and relocated after the 2011 events. Using ArcGIS 10.8 and the Analytic Hierarchy Process (AHP), we integrate DEM, slope, aspect, CORINE land cover Plus, surface-water presence/seasonality, and proximity to hazards (active and surface-rupture faults) and infrastructure (Karasu Stream, highways, village roads). A risk overlay is treated as a hard constraint. We produce suitability maps for settlement, agriculture, recreation, and industry; derive a composite optimum land-use surface; and translate outputs into decision rules (e.g., a 0–100 m fault no-build setback, riparian buffers, and slope thresholds) with an outline for implementation and monitoring. Key findings show legacy footprints at lower elevations, while new footprints cluster near the upper elevation band (DEM range 1642–1735 m). Most of the area exhibits 0–3% slopes, supporting low-impact access where hazards are manageable; however, several newly designated settlement tracts conflict with risk and water-service conditions. Although limited to a single case and available data resolutions, the workflow is transferable: it moves beyond mapping to actionable planning instruments—zoning overlays, buffers, thresholds, and phased management—supporting sustainable, culturally informed post-earthquake reconstruction. Full article